![](https://cdn-images-1.medium.com/max/1600/0*zmdmEhU288au9mft.jpg)

There was a time when sports betting only occurred in sports arenas and on playing fields. However, nowadays, most bets are placed through web or mobile applications. Thanks to smartphones and the widespread availability of the internet, we can now place bets, regardless of where we are or what we're doing at the moment. What can you do to make your app the most popular and functional? Consider incorporating the best features for sports betting applications.

## Sports betting applications – what are they?

Sports betting applications, also known as **sportsbook or betting apps**, are mobile or web-based applications that allow users to place bets on various sports events and outcomes.

These applications provide a platform for users to wager money on sports games, matches, or races. They offer a wide range of betting options, such as **predicting the winner, point spreads, over/under totals, and more**. You can use a [sports betting software development company](https://softwaremind.com/services/software-for-sport-betting-industry/) to improve user experience and increase your app's effectiveness.

## Crucial features of sports betting apps for users

**The crucial aspect is the intuitiveness of your app.** Navigation should be fast and easy. The speed of the application is also significant. Only a well-functioning application will provide users with the comfort of placing bets, which is particularly important for live betting. Additionally, **some functionalities can make your application even more attractive to users**

### Quick Login

Users like to log in quickly, which is crucial in the case of live events. A nice feature of apps is the **ability to log in with an email or Facebook account**. The method of logging into the mobile app should also be as fast as possible. A good way to achieve this is by **logging in with the user's fingerprint.**

### Linking a payment card to a user account

**Connecting a credit or debit card to a user account speeds up payments.** This way, users don't have to waste time confirming payments or logging into their bank accounts to transfer funds needed to place a bet. It's quick and convenient.

### Notifications

A seemingly obvious but more significant aspect is what should be covered by notifications. Indeed, users should receive **notifications about the final results of the game**, but they can also be informed about **changes in scores**. Additionally, the app can send notifications about **ongoing events similar to those in which the user participated or those in which they are interested in taking part.**

### Transmissions

**The most thrilling aspect of placing bets is waiting for the win and tracking results.** The best sports betting apps allow users to watch matches or competitions, which is why your software should enable that feature. With **live streaming**, users can experience the excitement of sports, even if they are just commuting back home from work by bus or train. **Live streams provide users with the best experience throughout the entire betting process.**

### Tools helpful in placing bets

Many tools can be helpful in placing bets. **Statistics, results from previous competitions, or comparisons can be helpful in making accurate decisions**. Your sports betting software should allow users to utilize them by sharing it in your app.

### Live chats

Chats can be useful for two reasons. The first reason is to **contact customer service if the user encounters any problems.** Thanks to this, issues can be resolved promptly, allowing users to participate in betting without major disruptions. Another use of the chat is to **communicate with other players to discuss game results and bets.**

Many interesting features engage users of betting apps. Choose the best software functionalities for sports betting companies and implement them in your business.

<img src="https://cdn-images-1.medium.com/max/1600/0*zmdmEhU288au9mft.jpg" alt="">
There was a time when sports betting only occurred in sports arenas and on playing fields. However, nowadays, most bets are placed through web or mobile applications. Thanks to smartphones and the widespread availability of the internet, we can now place bets, regardless of where we are or what we're doing at the moment. What can you do to make your app the most popular and functional? Consider incorporating the best features for sports betting applications.
<h2 id="sports-betting-applications--what-are-they">Sports betting applications – what are they?</h2>
Sports betting applications, also known as sportsbook or betting apps, are mobile or web-based applications that allow users to place bets on various sports events and outcomes.
These applications provide a platform for users to wager money on sports games, matches, or races. They offer a wide range of betting options, such as predicting the winner, point spreads, over/under totals, and more. You can use a <a href="https://softwaremind.com/services/software-for-sport-betting-industry/">sports betting software development company</a> to improve user experience and increase your app's effectiveness.
<h2 id="crucial-features-of-sports-betting-apps-for-users">Crucial features of sports betting apps for users</h2>
The crucial aspect is the intuitiveness of your app. Navigation should be fast and easy. The speed of the application is also significant. Only a well-functioning application will provide users with the comfort of placing bets, which is particularly important for live betting. Additionally, some functionalities can make your application even more attractive to users
<h3 id="quick-login">Quick Login</h3>
Users like to log in quickly, which is crucial in the case of live events. A nice feature of apps is the ability to log in with an email or Facebook account. The method of logging into the mobile app should also be as fast as possible. A good way to achieve this is by logging in with the user's fingerprint.
<h3 id="linking-a-payment-card-to-a-user-account">Linking a payment card to a user account</h3>
Connecting a credit or debit card to a user account speeds up payments. This way, users don't have to waste time confirming payments or logging into their bank accounts to transfer funds needed to place a bet. It's quick and convenient.
<h3 id="notifications">Notifications</h3>
A seemingly obvious but more significant aspect is what should be covered by notifications. Indeed, users should receive notifications about the final results of the game, but they can also be informed about changes in scores. Additionally, the app can send notifications about ongoing events similar to those in which the user participated or those in which they are interested in taking part.
<h3 id="transmissions">Transmissions</h3>
The most thrilling aspect of placing bets is waiting for the win and tracking results. The best sports betting apps allow users to watch matches or competitions, which is why your software should enable that feature. With live streaming, users can experience the excitement of sports, even if they are just commuting back home from work by bus or train. Live streams provide users with the best experience throughout the entire betting process.
<h3 id="tools-helpful-in-placing-bets">Tools helpful in placing bets</h3>
Many tools can be helpful in placing bets. Statistics, results from previous competitions, or comparisons can be helpful in making accurate decisions. Your sports betting software should allow users to utilize them by sharing it in your app.
<h3 id="live-chats">Live chats</h3>
Chats can be useful for two reasons. The first reason is to contact customer service if the user encounters any problems. Thanks to this, issues can be resolved promptly, allowing users to participate in betting without major disruptions. Another use of the chat is to communicate with other players to discuss game results and bets.
Many interesting features engage users of betting apps. Choose the best software functionalities for sports betting companies and implement them in your business.

Software functionalities for sports betting companies – enhance your customer experience

 Top 8 LLM + RAG Projects for your AI Portfolio 2025

Embedded finance has grown into a core component of modern digital platforms. Marketplaces, ride-hailing apps, telecom operators, SaaS tools, and retail ecosystems are increasingly adding financial features directly into their user journeys. At the centre of this shift are core banking APIs, which expose essential banking logic in a format that non-financial companies can integrate into their products.

These APIs offer direct access to accounts, balances, ledgers, cards, payouts, fees, and compliance workflows. Businesses can embed financial services without building complex banking infrastructure, maintaining a regulated stack, or operating a bank. The technical and regulatory complexity remains on the provider side, while companies focus on the customer experience.

## What Is a Core Banking API in an Embedded Finance Model?

A [core banking API](https://sdk.finance/solutions/core-banking-software-solution/) serves as the interface for the fundamental building blocks of banking:

* creating and managing accounts
* maintaining multi-currency balances
* executing transactions with real-time ledger entries
* issuing and managing cards
* running transfers and payouts
* applying fee and limit rules
* performing KYC/AML checks

Embedded finance brings this functionality into **non-financial digital products**, enabling:

* digital wallets in marketplaces
* driver payouts and cards in mobility platforms
* embedded credit at checkout
* accounts and payments inside SaaS workflows
* loyalty cards or branded financial products for retail

The API abstracts risk controls, settlement logic, reconciliation, and regulatory checks, allowing businesses to deliver financial operations through a simple technical integration.

## Why Core Banking APIs Matter for Embedded Finance

### **1. Rapid time-to-market**

Companies can deploy financial features in weeks or months rather than building multi-year banking cores.

### **2. Modular, mix-and-match capabilities**

Not every product needs a full banking stack. APIs allow selective integration: accounts, cards, AML, wallets, payouts, or settlements.

### **3. Rich transactional insights**

API-driven data enables personalisation, spend analysis, risk scoring, and improved underwriting inside the existing product flow.

### **4. Built-in compliance and security**

KYC, AML, encryption, audit trails, and secure accounting are included at the infrastructure level.

### **5. Stable, consistent financial logic**

The ledger-driven backend ensures accuracy, consistency, and traceability, all essential for embedded financial operations.

## How Core Banking APIs Enable Embedded Finance

Core banking APIs sit between the regulated financial infrastructure and the digital product that needs to embed financial operations.

A simplified sequence looks like this:

1. A user interacts with a platform (marketplace, mobility app, SaaS system).
2. The platform sends an API request (account creation, payout, card issuing, identity verification).
3. The core banking provider executes the request by:
 * applying compliance rules
 * writing to the ledger
 * communicating with partner banks or card schemes
4. The provider returns a structured response that the product uses in real time.

This approach enables embedded finance without requiring companies to manage or build regulated systems themselves.

## Core Banking API Vendors for Embedded Finance

The market offers numerous providers, yet the following ones stand out as the most trustworthy for embedded finance use cases.

### **SDK.finance**

SDK.finance provides an API-first core banking and ledger platform designed specifically for embedding financial capabilities into digital products. Its embedding-first design, modular architecture, and modern technology stack support wallets, payouts, card issuing, KYC/AML, and PSP workflows. The platform is **PCI-DSS certified** and is available as a PaaS or as a full source code licence for complete ownership.

### **Unit**

Unit is a Banking-as-a-Service provider offering accounts, cards, lending, and compliance flows. It focuses on fast implementation and developer-friendly tooling, particularly for fintechs and consumer apps operating in the American market.

### **Railsr**

Railsr offers embedded finance capabilities geared toward consumer applications, including payments, card issuing, and rewards. The platform is built around fast deployment and user engagement through loyalty and card programmes.

### **Mambu**

Mambu is a composable cloud banking engine commonly used for lending, deposits, and regulated financial products. Its modular APIs also support embedded finance layers for platforms that require bank-grade product structures.

### **Finxact**

Finxact provides an API-driven cloud core used by banks and enterprise platforms. Although often adopted in institutional contexts, it fits embedded finance cases requiring full-feature product creation and high scalability.

## Short Vendor Comparison Table

***

**Vendor: SDK.finance**

**Embedded Finance Use Cases:** Wallets, accounts, payouts, card issuing, PSP rails, crypto-to-fiat, super apps, marketplaces

**Delivery Model:** SaaS or full source code licence

**Notable Strengths:** Fast launch, embedding-first design, modern stack, PCI-DSS certified, high-performance ledger

***

**Vendor: Unit**

**Embedded Finance Use Cases:** Accounts, cards, compliance for US-focused apps

**Delivery Model:** SaaS BaaS

**Notable Strengths:** Fast launch, unified compliance, strong developer tooling

***

**Vendor: Railsr**

**Embedded Finance Use Cases:** Embedded cards, payments, rewards

**Delivery Model:** SaaS BaaS

**Notable Strengths:** Rewards engine, flexible card programmes

***

**Vendor: Mambu**

**Embedded Finance Use Cases:** Lending, deposits, regulated account products

**Delivery Model:** SaaS

**Notable Strengths:** Composable architecture, global reach

***

**Vendor: Finxact**

**Embedded Finance Use Cases:** Full banking capabilities for enterprise embedded finance

**Delivery Model:** SaaS cloud core

**Notable Strengths:** Scalability, configurable product engine

***

Core Banking APIs are now the backbone of embedded finance. They give digital platforms direct access to reliable financial operations without requiring them to manage the underlying banking infrastructure. Through these APIs, companies can plug in accounts, wallets, payouts, card programmes, compliance flows, and other financial features while maintaining a clear focus on product experience.

Vendors differ in approach: some prioritise rapid deployment for consumer apps, while others offer deeper banking capabilities for enterprise platforms. SDK.finance stands out with its embedding-first design, modern stack, PCI-DSS certification, and flexible deployment options, making it well suited for organisations building robust embedded finance products with long-term control over their core.

Embedded finance has grown into a core component of modern digital platforms. Marketplaces, ride-hailing apps, telecom operators, SaaS tools, and retail ecosystems are increasingly adding financial features directly into their user journeys. At the centre of this shift are core banking APIs, which expose essential banking logic in a format that non-financial companies can integrate into their products.
These APIs offer direct access to accounts, balances, ledgers, cards, payouts, fees, and compliance workflows. Businesses can embed financial services without building complex banking infrastructure, maintaining a regulated stack, or operating a bank. The technical and regulatory complexity remains on the provider side, while companies focus on the customer experience.
<h2 id="what-is-a-core-banking-api-in-an-embedded-finance-model">What Is a Core Banking API in an Embedded Finance Model?</h2>
A <a href="https://sdk.finance/solutions/core-banking-software-solution/">core banking API</a> serves as the interface for the fundamental building blocks of banking:
<ul>
<li>creating and managing accounts</li>
<li>maintaining multi-currency balances</li>
<li>executing transactions with real-time ledger entries</li>
<li>issuing and managing cards</li>
<li>running transfers and payouts</li>
<li>applying fee and limit rules</li>
<li>performing KYC/AML checks</li>
</ul>
Embedded finance brings this functionality into non-financial digital products, enabling:
<ul>
<li>digital wallets in marketplaces</li>
<li>driver payouts and cards in mobility platforms</li>
<li>embedded credit at checkout</li>
<li>accounts and payments inside SaaS workflows</li>
<li>loyalty cards or branded financial products for retail</li>
</ul>
The API abstracts risk controls, settlement logic, reconciliation, and regulatory checks, allowing businesses to deliver financial operations through a simple technical integration.
<h2 id="why-core-banking-apis-matter-for-embedded-finance">Why Core Banking APIs Matter for Embedded Finance</h2>
<h3 id="1-rapid-time-to-market">1. Rapid time-to-market</h3>
Companies can deploy financial features in weeks or months rather than building multi-year banking cores.
<h3 id="2-modular-mix-and-match-capabilities">2. Modular, mix-and-match capabilities</h3>
Not every product needs a full banking stack. APIs allow selective integration: accounts, cards, AML, wallets, payouts, or settlements.
<h3 id="3-rich-transactional-insights">3. Rich transactional insights</h3>
API-driven data enables personalisation, spend analysis, risk scoring, and improved underwriting inside the existing product flow.
<h3 id="4-built-in-compliance-and-security">4. Built-in compliance and security</h3>
KYC, AML, encryption, audit trails, and secure accounting are included at the infrastructure level.
<h3 id="5-stable-consistent-financial-logic">5. Stable, consistent financial logic</h3>
The ledger-driven backend ensures accuracy, consistency, and traceability, all essential for embedded financial operations.
<h2 id="how-core-banking-apis-enable-embedded-finance">How Core Banking APIs Enable Embedded Finance</h2>
Core banking APIs sit between the regulated financial infrastructure and the digital product that needs to embed financial operations.
A simplified sequence looks like this:
<ol>
<li>A user interacts with a platform (marketplace, mobility app, SaaS system).</li>
<li>The platform sends an API request (account creation, payout, card issuing, identity verification).</li>
<li>The core banking provider executes the request by:
<ul>
<li>applying compliance rules</li>
<li>writing to the ledger</li>
<li>communicating with partner banks or card schemes</li>
</ul>
</li>
<li>The provider returns a structured response that the product uses in real time.</li>
</ol>
This approach enables embedded finance without requiring companies to manage or build regulated systems themselves.
<h2 id="core-banking-api-vendors-for-embedded-finance">Core Banking API Vendors for Embedded Finance</h2>
The market offers numerous providers, yet the following ones stand out as the most trustworthy for embedded finance use cases.
<h3 id="sdkfinance">SDK.finance</h3>
SDK.finance provides an API-first core banking and ledger platform designed specifically for embedding financial capabilities into digital products. Its embedding-first design, modular architecture, and modern technology stack support wallets, payouts, card issuing, KYC/AML, and PSP workflows. The platform is PCI-DSS certified and is available as a PaaS or as a full source code licence for complete ownership.
<h3 id="unit">Unit</h3>
Unit is a Banking-as-a-Service provider offering accounts, cards, lending, and compliance flows. It focuses on fast implementation and developer-friendly tooling, particularly for fintechs and consumer apps operating in the American market.
<h3 id="railsr">Railsr</h3>
Railsr offers embedded finance capabilities geared toward consumer applications, including payments, card issuing, and rewards. The platform is built around fast deployment and user engagement through loyalty and card programmes.
<h3 id="mambu">Mambu</h3>
Mambu is a composable cloud banking engine commonly used for lending, deposits, and regulated financial products. Its modular APIs also support embedded finance layers for platforms that require bank-grade product structures.
<h3 id="finxact">Finxact</h3>
Finxact provides an API-driven cloud core used by banks and enterprise platforms. Although often adopted in institutional contexts, it fits embedded finance cases requiring full-feature product creation and high scalability.
<h2 id="short-vendor-comparison-table">Short Vendor Comparison Table</h2>
<hr>
Vendor: SDK.finance
Embedded Finance Use Cases: Wallets, accounts, payouts, card issuing, PSP rails, crypto-to-fiat, super apps, marketplaces
Delivery Model: SaaS or full source code licence
Notable Strengths: Fast launch, embedding-first design, modern stack, PCI-DSS certified, high-performance ledger
<hr>
Vendor: Unit
Embedded Finance Use Cases: Accounts, cards, compliance for US-focused apps
Delivery Model: SaaS BaaS
Notable Strengths: Fast launch, unified compliance, strong developer tooling
<hr>
Vendor: Railsr
Embedded Finance Use Cases: Embedded cards, payments, rewards
Delivery Model: SaaS BaaS
Notable Strengths: Rewards engine, flexible card programmes
<hr>
Vendor: Mambu
Embedded Finance Use Cases: Lending, deposits, regulated account products
Delivery Model: SaaS
Notable Strengths: Composable architecture, global reach
<hr>
Vendor: Finxact
Embedded Finance Use Cases: Full banking capabilities for enterprise embedded finance
Delivery Model: SaaS cloud core
Notable Strengths: Scalability, configurable product engine
<hr>
Core Banking APIs are now the backbone of embedded finance. They give digital platforms direct access to reliable financial operations without requiring them to manage the underlying banking infrastructure. Through these APIs, companies can plug in accounts, wallets, payouts, card programmes, compliance flows, and other financial features while maintaining a clear focus on product experience.
Vendors differ in approach: some prioritise rapid deployment for consumer apps, while others offer deeper banking capabilities for enterprise platforms. SDK.finance stands out with its embedding-first design, modern stack, PCI-DSS certification, and flexible deployment options, making it well suited for organisations building robust embedded finance products with long-term control over their core.

Core Banking API for Embedded Finance: How Digital Products Gain Banking Capabilities Through APIs

![](https://cdn-images-1.medium.com/max/1600/1*XMf_cR1ST_NpeJrjNq0R-w.png)

### The Problem

You’ve built a nice little API using AWS API Gateway - serving as a web-hook endpoint for an external Order Management System.
It takes orders from your web app, saves them to a database, and calls a few downstream services.
It runs through this stack:
 **API Gateway → Lambda → Database**.

It works perfectly on a normal day.

Then one day…

* Your marketing team runs a big sale.
* Or a partner’s integration script gets stuck in a loop.
* Or a new feature upstream pushes thousands of requests by mistake.

Suddenly you’re facing an **event storm -** a huge, unexpected spike in requests hitting your API all at once.

### What’s an Event Storm?

An **event storm** is when the number of incoming requests in a short time is way higher than your system normally handles.
 It’s not a slow increase. It’s a flood.

**Example triggers:**

* Black Friday or Diwali sale on your e-commerce site.
* A cron job gone rogue sending thousands of API calls per second.
* A botnet hammering your public endpoint.
* A sudden surge in user signups after media coverage.

**Quick note on Lambda scaling:** Lambda doesn’t go from 0 → 100K concurrent executions instantly. Each region has a burst concurrency limit (for example \~3000 in `us-east-1`) and then scales up by about 500–1000 per minute.
So when I say “100K requests hit,” I mean the **inbound spike hitting API Gateway**, not the actual concurrency Lambda reached. The scaling ramp-up actually makes the need for a buffer more obvious -SQS can absorb the full burst immediately, while Lambda drains at the pace it can realistically scale to.

### Why It’s a Problem in AWS Lambda

AWS Lambda scales fast, which sounds great until you realise:

* Every Lambda execution counts toward your account concurrency limit.
* If you hit that limit, all other Lambdas in your account may start failing too.
* Downstream systems (like a database) may crash from too many parallel requests.

With API Gateway directly invoking Lambda, an event storm can burn through concurrency in seconds.

### The Safer Approach

We’ll redesign the flow so that:

* API Gateway sends requests to **SQS** instead of Lambda directly.
* If SQS accepts it, API Gateway returns **202 Accepted** with an **ack id** (SQS `MessageId`).

```
HTTP/1.1 202 Accepted{"status":"accepted","messageId":"c1c7...","receivedAt":1735296623123}
```

* Processing happens later via the consumer Lambda.
* Lambda reads from SQS in **controlled batches** and upserts into database
* Concurrency is **capped** so we never overload the database.
* Failed messages go to a **DLQ** for later reprocessing.

This way, the **storm is buffered** in SQS, and you can process at a steady pace without losing data.

### Architecture Overview

1. **API Gateway** -> receives incoming HTTP requests.
2. **SQS (Standard Queue)** -> buffers all requests.
3. **Lambda Consumer ->** pulls messages from SQS in small batches.
4. **DLQ ->** stores failed messages for later replay.

**Don’t forget about caching at the API layer.**
If your API is serving repetitive data (like catalog lookups, product info, or configuration files), you can enable **API Gateway caching**. This stores responses in an in-memory cache so repeated requests don’t even reach Lambda or SQS.
In my case the requests were mostly unique and user-specific, so caching didn’t help much. But for many APIs, caching at API GW can drastically reduce Lambda concurrency and costs.

### Step 0: Recommended Starting Settings

You can tweak these later, but start here:

* **Lambda timeout:** 30s
* **Lambda reserved concurrency (***the max number of concurrent executions this function can ever use*)**:** 50
* **SQS batch size:** 10
* **Max batching window** (*how long Lambda waits to gather more records before invoking the function*)**:** 2s
* **SQS visibility timeout** (*the time a message stays hidden after being picked up, before it can be retried*)**:** 90s
* **DLQ maxReceiveCount (***the number of times a message can fail before being sent to the DLQ*)**:** 5

Why these help:

* Max parallel work = 50 (*Lambda reserved concurrency***)** × 10 (*SQS batch size***)** = **500 messages at once**.
* SQS buffers the rest during a spike.
* Visibility timeout prevents duplicate processing.
* DLQ ensures nothing is lost.

### Step 1: Create SQS and DLQ

**Create DLQ**

```
aws sqs create-queue --queue-name orders-dlq
```

**Create main queue with DLQ attached**

```
DLQ_URL=$(aws sqs get-queue-url --queue-name orders-dlq --query 'QueueUrl' --output text)DLQ_ARN=$(aws sqs get-queue-attributes --queue-url $DLQ_URL --attribute-names QueueArn --query 'Attributes.QueueArn' --output text)aws sqs create-queue \ --queue-name orders \ --attributes '{ "RedrivePolicy":"{\"deadLetterTargetArn\":\"'"$DLQ_ARN"'\",\"maxReceiveCount\":\"5\"}", "VisibilityTimeout":"90" }'
```

### Step 2: Connect API Gateway to SQS

#### Option A: Direct integration

* No Lambda in between.
* API Gateway sends request body directly to SQS.

**IAM role for API Gateway**

```
{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": ["sqs:SendMessage"], "Resource": "arn:aws:sqs:<REGION>:<ACCOUNT_ID>:orders" }]}
```

#### Option B: Producer Lambda

If you need validation/auth before pushing to SQS:

```
// producer/index.mjsimport { SQSClient, SendMessageCommand } from "@aws-sdk/client-sqs";const sqs = new SQSClient({});const QUEUE_URL = process.env.QUEUE_URL;export const handler = async (event) => { const body = JSON.parse(event.body || "{}"); if (!body.orderId) { return { statusCode: 400, body: JSON.stringify({ error: "orderId required" }) }; } await sqs.send(new SendMessageCommand({ QueueUrl: QUEUE_URL, MessageBody: JSON.stringify(body) })); return { statusCode: 202, body: JSON.stringify({ status: "accepted" }) };};
```

**API Gateway caching reminder:** If traffic includes repeated requests, enable API Gateway caching. It cuts load before it even reaches SQS or Lambda.

**Step 3: Create the Consumer Lambda**

```
// consumer/index.mjsexport const handler = async (event) => { for (const record of event.Records) { try { const msg = JSON.parse(record.body); await processOrder(msg); } catch (err) { console.error("Failed for messageId", record.messageId, err); throw err; // forces SQS to retry or send to DLQ } }};async function processOrder(msg) { await new Promise(r => setTimeout(r, 150)); // simulate work}
```

**Event Source Mapping**

* Batch size: 10
* Max batching window: 2s

**Reserved concurrency:** 50 (prevents overload)

### Step 3.1: Don’t kill your database connections

One hidden problem when Lambda talks to RDS: each Lambda instance may open a new DB connection. During a spike, you can exhaust DB connections fast.

The fix is to use **RDS Proxy**. It sits between Lambda and the database, pools connections, and reuses them across Lambda invocations. This keeps your database safe from connection storms.

Alternatives:

* For PostgreSQL/MySQL, some teams also run PgBouncer on ECS/Fargate.
* Or, for workloads with unpredictable spikes, consider DynamoDB which doesn’t have connection limits.

### Step 4: Tune for Storms

* **Visibility timeout** ≥ 3 × Lambda timeout.
* Do throughput math:

```
parallelism = reserved_concurrency × batch_sizerecords/sec ≈ parallelism / avg_processing_seconds
```

### Step 5: Alerts

**Queue depth alarm**

```
aws cloudwatch put-metric-alarm \ --alarm-name "orders-queue-depth-high" \ --metric-name ApproximateNumberOfMessagesVisible \ --namespace AWS/SQS \ --dimensions Name=QueueName,Value=orders \ --statistic Average \ --period 60 \ --threshold 50000 \ --comparison-operator GreaterThanThreshold \ --evaluation-periods 5 \ --alarm-actions <SNS_TOPIC_ARN>
```

**Oldest message age alarm ->** Warns you when messages have been waiting too long in the queue (in **seconds**).

```
aws cloudwatch put-metric-alarm \ --alarm-name "orders-oldest-message-too-old" \ --namespace "AWS/SQS" \ --metric-name "ApproximateAgeOfOldestMessage" \ --dimensions "Name=QueueName,Value=orders" \ --statistic "Maximum" \ --period 60 \ --threshold 600 \ # warn if oldest message > 10 minutes old --comparison-operator "GreaterThanThreshold" \ --evaluation-periods 5 \ # for 5 minutes if period=60 --treat-missing-data "notBreaching" \ --unit "Seconds" \ --alarm-actions "<SNS_TOPIC_ARN>"
```

### Step 6: DLQ Replay

Fix the bug → replay messages from DLQ to main queue with a small Lambda.

```
import { SQSClient, ReceiveMessageCommand, DeleteMessageCommand, SendMessageCommand } from "@aws-sdk/client-sqs";const sqs = new SQSClient({});const DLQ_URL = process.env.DLQ_URL;const MAIN_URL = process.env.MAIN_URL;export const handler = async () => { const res = await sqs.send(new ReceiveMessageCommand({ QueueUrl: DLQ_URL, MaxNumberOfMessages: 10, WaitTimeSeconds: 1 })); if (!res.Messages) return; for (const m of res.Messages) { await sqs.send(new SendMessageCommand({ QueueUrl: MAIN_URL, MessageBody: m.Body })); await sqs.send(new DeleteMessageCommand({ QueueUrl: DLQ_URL, ReceiptHandle: m.ReceiptHandle })); }};
```

* Pick `THRESHOLD_SECONDS` well below your SQS **message retention** (default 4 days) but high enough to avoid noise. Start with 600-1800 seconds for most APIs, or align it with your SLA.
* Use `Maximum` (not `Average`) - you care about the single **oldest** message.
* Pair this with the **queue depth** alarm you already have.

### Important Pointers..

* **Don’t** let Lambda’s scaling run wild in a spike.
* **Do** buffer in SQS and process at a pace your system can handle.
* **Always** set a DLQ - losing data is worse than slow processing.
* **Monitor** queue depth and message age - that’s your early warning.
* **Use RDS Proxy** when Lambdas talk to relational databases.
* **Add API Gateway caching** if traffic is repetitive.

### **One last note,**

This whole design works best for **asynchronous APIs-**&#x70;laces where you can acknowledge a request and process it later.

But what if your API is **synchronous** and the client needs a response *right away*? In that case, queues won’t help. You need other tools like rate limiting, provisioned concurrency, and sometimes containers.

I wrote a follow-up on handling **synchronous API traffic spikes** [here](https://medium.com/aws-in-plain-english/surviving-traffic-surges-in-sync-apis-rate-limits-warm-lambdas-and-smart-scaling-d04488ad94db?sk=6a2f4645f254fd28119b2f5ab263269d):
[https://medium.com/aws-in-plain-english/surviving-traffic-surges-in-sync-apis-rate-limits-warm-lambdas-and-smart-scaling-d04488ad94db?sk=6a2f4645f254fd28119b2f5ab263269d](https://medium.com/aws-in-plain-english/surviving-traffic-surges-in-sync-apis-rate-limits-warm-lambdas-and-smart-scaling-d04488ad94db?sk=6a2f4645f254fd28119b2f5ab263269d)

Together, these two posts cover both sides of the coin:

* Async APIs → buffer with SQS.
* Sync APIs → throttle, pre-warm, or containerize.

### ☕️ Found This Helpful?

If this guide helped you debug a tricky front-end issue or saved you from a wild goose chase :
you can [buy me a coffee](http://buymeacoffee.com/subodhshet0)

### A message from our Founder

**Hey,** [**Sunil**](https://linkedin.com/in/sunilsandhu) **here.** I wanted to take a moment to thank you for reading until the end and for being a part of this community.

Did you know that our team run these publications as a volunteer effort to over 3.5m monthly readers? **We don’t receive any funding, we do this to support the community. ❤️**

If you want to show some love, please take a moment to **follow me on** [**LinkedIn**](https://linkedin.com/in/sunilsandhu)**,** [**TikTok**](https://tiktok.com/@messyfounder), [**Instagram**](https://instagram.com/sunilsandhu). You can also subscribe to our [**weekly newsletter**](https://newsletter.plainenglish.io/).

And before you go, don’t forget to **clap** and **follow** the writer️!

<img src="https://cdn-images-1.medium.com/max/1600/1*XMf_cR1ST_NpeJrjNq0R-w.png" alt="">
<h3 id="the-problem">The Problem</h3>
You’ve built a nice little API using AWS API Gateway - serving as a web-hook endpoint for an external Order Management System.
It takes orders from your web app, saves them to a database, and calls a few downstream services.
It runs through this stack:
 API Gateway → Lambda → Database.
It works perfectly on a normal day.
Then one day…
<ul>
<li>Your marketing team runs a big sale.</li>
<li>Or a partner’s integration script gets stuck in a loop.</li>
<li>Or a new feature upstream pushes thousands of requests by mistake.</li>
</ul>
Suddenly you’re facing an event storm - a huge, unexpected spike in requests hitting your API all at once.
<h3 id="whats-an-eventstorm">What’s an Event Storm?</h3>
An event storm is when the number of incoming requests in a short time is way higher than your system normally handles.
 It’s not a slow increase. It’s a flood.
Example triggers:
<ul>
<li>Black Friday or Diwali sale on your e-commerce site.</li>
<li>A cron job gone rogue sending thousands of API calls per second.</li>
<li>A botnet hammering your public endpoint.</li>
<li>A sudden surge in user signups after media coverage.</li>
</ul>
Quick note on Lambda scaling: Lambda doesn’t go from 0 → 100K concurrent executions instantly. Each region has a burst concurrency limit (for example ~3000 in <code>us-east-1</code>) and then scales up by about 500–1000 per minute.
So when I say “100K requests hit,” I mean the inbound spike hitting API Gateway, not the actual concurrency Lambda reached. The scaling ramp-up actually makes the need for a buffer more obvious -SQS can absorb the full burst immediately, while Lambda drains at the pace it can realistically scale to.
<h3 id="why-its-a-problem-in-awslambda">Why It’s a Problem in AWS Lambda</h3>
AWS Lambda scales fast, which sounds great until you realise:
<ul>
<li>Every Lambda execution counts toward your account concurrency limit.</li>
<li>If you hit that limit, all other Lambdas in your account may start failing too.</li>
<li>Downstream systems (like a database) may crash from too many parallel requests.</li>
</ul>
With API Gateway directly invoking Lambda, an event storm can burn through concurrency in seconds.
<h3 id="the-saferapproach">The Safer Approach</h3>
We’ll redesign the flow so that:
<ul>
<li>API Gateway sends requests to SQS instead of Lambda directly.</li>
<li>If SQS accepts it, API Gateway returns 202 Accepted with an ack id (SQS <code>MessageId</code>).</li>
</ul>
<pre><code class="code-highlight">HTTP/1.1 202 Accepted{"status":"accepted","messageId":"c1c7...","receivedAt":1735296623123}
</code></pre>
<ul>
<li>Processing happens later via the consumer Lambda.</li>
<li>Lambda reads from SQS in controlled batches and upserts into database</li>
<li>Concurrency is capped so we never overload the database.</li>
<li>Failed messages go to a DLQ for later reprocessing.</li>
</ul>
This way, the storm is buffered in SQS, and you can process at a steady pace without losing data.
<h3 id="architecture-overview">Architecture Overview</h3>
<ol>
<li>API Gateway -> receives incoming HTTP requests.</li>
<li>SQS (Standard Queue) -> buffers all requests.</li>
<li>Lambda Consumer -> pulls messages from SQS in small batches.</li>
<li>DLQ -> stores failed messages for later replay.</li>
</ol>
Don’t forget about caching at the API layer.
If your API is serving repetitive data (like catalog lookups, product info, or configuration files), you can enable API Gateway caching. This stores responses in an in-memory cache so repeated requests don’t even reach Lambda or SQS.
In my case the requests were mostly unique and user-specific, so caching didn’t help much. But for many APIs, caching at API GW can drastically reduce Lambda concurrency and costs.
<h3 id="step-0-recommended-startingsettings">Step 0: Recommended Starting Settings</h3>
You can tweak these later, but start here:
<ul>
<li>Lambda timeout: 30s</li>
<li>Lambda reserved concurrency (the max number of concurrent executions this function can ever use): 50</li>
<li>SQS batch size: 10</li>
<li>Max batching window (how long Lambda waits to gather more records before invoking the function): 2s</li>
<li>SQS visibility timeout (the time a message stays hidden after being picked up, before it can be retried): 90s</li>
<li>DLQ maxReceiveCount (the number of times a message can fail before being sent to the DLQ): 5</li>
</ul>
Why these help:
<ul>
<li>Max parallel work = 50 (Lambda reserved concurrency**)** × 10 (SQS batch size**)** = 500 messages at once.</li>
<li>SQS buffers the rest during a spike.</li>
<li>Visibility timeout prevents duplicate processing.</li>
<li>DLQ ensures nothing is lost.</li>
</ul>
<h3 id="step-1-create-sqs-anddlq">Step 1: Create SQS and DLQ</h3>
Create DLQ
<pre><code class="code-highlight">aws sqs create-queue --queue-name orders-dlq
</code></pre>
Create main queue with DLQ attached
<pre><code class="code-highlight">DLQ_URL=$(aws sqs get-queue-url --queue-name orders-dlq --query 'QueueUrl' --output text)DLQ_ARN=$(aws sqs get-queue-attributes --queue-url $DLQ_URL --attribute-names QueueArn --query 'Attributes.QueueArn' --output text)aws sqs create-queue \ --queue-name orders \ --attributes '{ "RedrivePolicy":"{\"deadLetterTargetArn\":\"'"$DLQ_ARN"'\",\"maxReceiveCount\":\"5\"}", "VisibilityTimeout":"90" }'
</code></pre>
<h3 id="step-2-connect-api-gateway-tosqs">Step 2: Connect API Gateway to SQS</h3>
<h4 id="option-a-direct-integration">Option A: Direct integration</h4>
<ul>
<li>No Lambda in between.</li>
<li>API Gateway sends request body directly to SQS.</li>
</ul>
IAM role for API Gateway
<pre><code class="code-highlight">{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": ["sqs:SendMessage"], "Resource": "arn:aws:sqs:&#x3C;REGION>:&#x3C;ACCOUNT_ID>:orders" }]}
</code></pre>
<h4 id="option-b-producerlambda">Option B: Producer Lambda</h4>
If you need validation/auth before pushing to SQS:
<pre><code class="code-highlight">// producer/index.mjsimport { SQSClient, SendMessageCommand } from "@aws-sdk/client-sqs";const sqs = new SQSClient({});const QUEUE_URL = process.env.QUEUE_URL;export const handler = async (event) => { const body = JSON.parse(event.body || "{}"); if (!body.orderId) { return { statusCode: 400, body: JSON.stringify({ error: "orderId required" }) }; } await sqs.send(new SendMessageCommand({ QueueUrl: QUEUE_URL, MessageBody: JSON.stringify(body) })); return { statusCode: 202, body: JSON.stringify({ status: "accepted" }) };};
</code></pre>
API Gateway caching reminder: If traffic includes repeated requests, enable API Gateway caching. It cuts load before it even reaches SQS or Lambda.
Step 3: Create the Consumer Lambda
<pre><code class="code-highlight">// consumer/index.mjsexport const handler = async (event) => { for (const record of event.Records) { try { const msg = JSON.parse(record.body); await processOrder(msg); } catch (err) { console.error("Failed for messageId", record.messageId, err); throw err; // forces SQS to retry or send to DLQ } }};async function processOrder(msg) { await new Promise(r => setTimeout(r, 150)); // simulate work}
</code></pre>
Event Source Mapping
<ul>
<li>Batch size: 10</li>
<li>Max batching window: 2s</li>
</ul>
Reserved concurrency: 50 (prevents overload)
<h3 id="step-31-dont-kill-your-database-connections">Step 3.1: Don’t kill your database connections</h3>
One hidden problem when Lambda talks to RDS: each Lambda instance may open a new DB connection. During a spike, you can exhaust DB connections fast.
The fix is to use RDS Proxy. It sits between Lambda and the database, pools connections, and reuses them across Lambda invocations. This keeps your database safe from connection storms.
Alternatives:
<ul>
<li>For PostgreSQL/MySQL, some teams also run PgBouncer on ECS/Fargate.</li>
<li>Or, for workloads with unpredictable spikes, consider DynamoDB which doesn’t have connection limits.</li>
</ul>
<h3 id="step-4-tune-forstorms">Step 4: Tune for Storms</h3>
<ul>
<li>Visibility timeout ≥ 3 × Lambda timeout.</li>
<li>Do throughput math:</li>
</ul>
<pre><code class="code-highlight">parallelism = reserved_concurrency × batch_sizerecords/sec ≈ parallelism / avg_processing_seconds
</code></pre>
<h3 id="step-5alerts">Step 5: Alerts</h3>
Queue depth alarm
<pre><code class="code-highlight">aws cloudwatch put-metric-alarm \ --alarm-name "orders-queue-depth-high" \ --metric-name ApproximateNumberOfMessagesVisible \ --namespace AWS/SQS \ --dimensions Name=QueueName,Value=orders \ --statistic Average \ --period 60 \ --threshold 50000 \ --comparison-operator GreaterThanThreshold \ --evaluation-periods 5 \ --alarm-actions &#x3C;SNS_TOPIC_ARN>
</code></pre>
Oldest message age alarm -> Warns you when messages have been waiting too long in the queue (in seconds).
<pre><code class="code-highlight">aws cloudwatch put-metric-alarm \ --alarm-name "orders-oldest-message-too-old" \ --namespace "AWS/SQS" \ --metric-name "ApproximateAgeOfOldestMessage" \ --dimensions "Name=QueueName,Value=orders" \ --statistic "Maximum" \ --period 60 \ --threshold 600 \ # warn if oldest message > 10 minutes old --comparison-operator "GreaterThanThreshold" \ --evaluation-periods 5 \ # for 5 minutes if period=60 --treat-missing-data "notBreaching" \ --unit "Seconds" \ --alarm-actions "&#x3C;SNS_TOPIC_ARN>"
</code></pre>
<h3 id="step-6-dlqreplay">Step 6: DLQ Replay</h3>
Fix the bug → replay messages from DLQ to main queue with a small Lambda.
<pre><code class="code-highlight">import { SQSClient, ReceiveMessageCommand, DeleteMessageCommand, SendMessageCommand } from "@aws-sdk/client-sqs";const sqs = new SQSClient({});const DLQ_URL = process.env.DLQ_URL;const MAIN_URL = process.env.MAIN_URL;export const handler = async () => { const res = await sqs.send(new ReceiveMessageCommand({ QueueUrl: DLQ_URL, MaxNumberOfMessages: 10, WaitTimeSeconds: 1 })); if (!res.Messages) return; for (const m of res.Messages) { await sqs.send(new SendMessageCommand({ QueueUrl: MAIN_URL, MessageBody: m.Body })); await sqs.send(new DeleteMessageCommand({ QueueUrl: DLQ_URL, ReceiptHandle: m.ReceiptHandle })); }};
</code></pre>
<ul>
<li>Pick <code>THRESHOLD_SECONDS</code> well below your SQS message retention (default 4 days) but high enough to avoid noise. Start with 600-1800 seconds for most APIs, or align it with your SLA.</li>
<li>Use <code>Maximum</code> (not <code>Average</code>) - you care about the single oldest message.</li>
<li>Pair this with the queue depth alarm you already have.</li>
</ul>
<h3 id="important-pointers">Important Pointers..</h3>
<ul>
<li>Don’t let Lambda’s scaling run wild in a spike.</li>
<li>Do buffer in SQS and process at a pace your system can handle.</li>
<li>Always set a DLQ - losing data is worse than slow processing.</li>
<li>Monitor queue depth and message age - that’s your early warning.</li>
<li>Use RDS Proxy when Lambdas talk to relational databases.</li>
<li>Add API Gateway caching if traffic is repetitive.</li>
</ul>
<h3 id="one-lastnote">One last note,</h3>
This whole design works best for asynchronous APIs-places where you can acknowledge a request and process it later.
But what if your API is synchronous and the client needs a response right away? In that case, queues won’t help. You need other tools like rate limiting, provisioned concurrency, and sometimes containers.
I wrote a follow-up on handling synchronous API traffic spikes <a href="https://medium.com/aws-in-plain-english/surviving-traffic-surges-in-sync-apis-rate-limits-warm-lambdas-and-smart-scaling-d04488ad94db?sk=6a2f4645f254fd28119b2f5ab263269d">here</a>:
<a href="https://medium.com/aws-in-plain-english/surviving-traffic-surges-in-sync-apis-rate-limits-warm-lambdas-and-smart-scaling-d04488ad94db?sk=6a2f4645f254fd28119b2f5ab263269d">https://medium.com/aws-in-plain-english/surviving-traffic-surges-in-sync-apis-rate-limits-warm-lambdas-and-smart-scaling-d04488ad94db?sk=6a2f4645f254fd28119b2f5ab263269d</a>
Together, these two posts cover both sides of the coin:
<ul>
<li>Async APIs → buffer with SQS.</li>
<li>Sync APIs → throttle, pre-warm, or containerize.</li>
</ul>
<h3 id="️-found-thishelpful">☕️ Found This Helpful?</h3>
If this guide helped you debug a tricky front-end issue or saved you from a wild goose chase :
you can <a href="http://buymeacoffee.com/subodhshet0">buy me a coffee</a>
<h3 id="a-message-from-ourfounder">A message from our Founder</h3>
Hey, <a href="https://linkedin.com/in/sunilsandhu">Sunil</a> here. I wanted to take a moment to thank you for reading until the end and for being a part of this community.
Did you know that our team run these publications as a volunteer effort to over 3.5m monthly readers? We don’t receive any funding, we do this to support the community. ❤️
If you want to show some love, please take a moment to follow me on <a href="https://linkedin.com/in/sunilsandhu">LinkedIn</a>, <a href="https://tiktok.com/@messyfounder">TikTok</a>, <a href="https://instagram.com/sunilsandhu">Instagram</a>. You can also subscribe to our <a href="https://newsletter.plainenglish.io/">weekly newsletter</a>.
And before you go, don’t forget to clap and follow the writer️!

How to Stop AWS Lambda from Melting When 100K Requests Hit at Once

For the modern small business owner, the creative process has changed forever. You no longer need to know how to use complex software like Photoshop; you need to know how to write an effective AI Prompt Engineering for Branding prompt.

![](https://cdn-images-1.medium.com/max/1600/1*mq4Z-LZN8T_07_YKsBgcXw.png)

A prompt is more than a keyword; it is the Natural Language Design brief that the X-Design AI Agent uses to build your entire visual world. The difference between a generic result and a professional, cohesive brand lies entirely in the structure and detail of that instruction.

Most generic tools operate on simple keyword matching. If you type “YouTube logo,” you get a generic YouTube Channel logo. X-Design [AI logo generator](https://www.x-design.com/ai-logo-generator) operates by interpreting a true Visual Brand Brief, allowing you to embed your company’s story, industry standards, and required assets into a single command.

Mastering the prompt is mastering your brand’s destiny. It eliminates the back-and-forth common with human designers and moves you straight to a professional, on-brand result.

### The X-Design Prompt Structure: A Three-Layer Formula

To unlock the full potential of X-Design and AI Prompt Engineering for Branding, you need to think of your prompt not as a sentence, but as a formula with three distinct layers. This X-Design Prompt Structure ensures the AI builds your brand on a solid, strategic foundation.

![](https://cdn-images-1.medium.com/max/1600/1*u4TAaBg3hlgl8OCZFJ5uvA.png)

### Layer 1: The Foundation (What You Are Selling)

This layer provides the essential context. It grounds the AI in your business reality — who you are, what you sell, and who you are trying to reach.

**Industry & Product:** Define the tangible subject.
Example: *“An artisan bakery specializing in sourdough and rustic pastries.”*

**Brand Tone & Feeling:** Define the abstract mood and emotional atmosphere.
Example: *“The mood should be warm, trustworthy, and heritage-focused.”*

**Target Audience:** Define who the design should appeal to.
Example: *“Young professionals and families in an urban, upscale market.”*

Why it matters: Generic AI knows what “sourdough” looks like. X-Design, using this foundation, understands that a “heritage-focused” design for “upscale market” must use specific typography and a muted color palette, automatically enforcing brand standards before you even ask for them.

### Layer 2: The Visual DNA (How It Must Look)

This is the creative layer where you dictate the specific aesthetic choices. It converts abstract ideas into tangible visual styles.

**Style Guidance:** Define the artistic treatment.
Example: *“Minimalist line art, hand-drawn aesthetic, or geometric symbol.”*

**Color Palette:** Specify the exact emotional color set.
Example: *“Earthy and natural color palette, specifically using deep burnt orange and off-white.”*

**Typography Preference:** Guide the font choice for the logo design.
Example: *“A clean, modern serif font for the name, with a supporting sans-serif.”*

Why it matters: This layer provides Design Language Prompt elements. By specifying “Minimalist line art” and “deep burnt orange,” you prevent the AI from defaulting to bright, generic colors and complex vector graphics, ensuring a unique result that aligns with your vision.

### Layer 3: The Refinement & Constraint (The Professional Finish)

This layer applies final quality checks and details required for professional deployment. It is where you move from a nice graphic to a usable Logo System.

**Technical Constraint:** Specify deployment needs.
Example: *“Must be able to work in a circular stamp format and be legible when printed small.”*

**Misuse/Avoid:** Tell the AI what not to include.
Example: *“Avoid overly complicated symbols or any representation of a chef’s hat.”*

**Iteration Strategy:** Prompt for a specific change.
Example: *“Based on this design, generate three more concepts that only change the geometric symbol.”*

Why it matters: Constraints are powerful. By demanding a design that is “legible when printed small,” you force the AI Prompt Engineering for Branding system to prioritize clarity and technical robustness, which is critical for Iterative AI Design and final file usability.

### Prompt Application: From Logo to Marketing Templates

The ultimate test of AI Prompt Engineering for Branding is its ability to scale across different visual assets while maintaining a single, consistent look. X-Design uses the same prompt language, but adapts it to the specific format of each asset.

### 1. Logo Prompt Formulas (The Master Visual Brand Brief)

The logo prompt is the most important, as it defines your Visual Identity Toolkit. It should be comprehensive, combining all three layers to create the foundational asset. You can also use the photo enhancer tool to [improve the logo quality](https://www.x-design.com/image-enhancer).

Formula Example: *“\[LAYER 1: Artisan bakery specializing in rustic pastries. Brand tone should feel grounded and reliable.] \[LAYER 2: Use a clean geometric symbol combined with a strong, modern serif font. The color palette is restricted to deep forest green and cream.] \[LAYER 3: Ensure the logo is suitable for a circular coffee stamp and is highly legible at small sizes.]”*

![](https://cdn-images-1.medium.com/max/1600/1*qCpHhtfJ1dhUQK9QIqJRJQ.png)

### 2. Poster/Flyer Prompts (The Marketing Brief)

Once the Logo System is established, the prompt for marketing materials shifts focus from *what the brand is* to *what the brand is doing*, while the AI automatically enforces the stored colors and fonts.

Formula Example: *“\[ACTION: Create a square social media poster for a special holiday promotion: 25% off all pastries before 10 AM on Monday.] \[VISUALS: Use a high-quality photo of a steaming latte with a soft bokeh background. The headline must be bold and prominent.] \[CONSTRAINT: Must adhere to the approved ‘Forest Green’ color palette and use the primary headline font.]”*

![](https://cdn-images-1.medium.com/max/1600/1*volJTVkpNc_pNR2lG0n71w.png)

Key insight: You don’t prompt for color or font here; you prompt for *marketing strategy*, and the AI handles the Design Rule Enforcement instantly, ensuring On-Brand Posters.

### 3. Product Packaging Prompts (The Product Brief)

These prompts focus on application and physical constraint. You must tell the AI where the logo needs to be placed and the material it will interact with.

Formula Example: *“\[PRODUCT: Design packaging for a retail coffee bag.] \[VISUALS: Apply the ‘inverse’ (white) version of the logo system to the dark kraft paper background. Use a simple, informational layout.] \[CONSTRAINT: Use the body font for all product description text. Design must include space for a stamped ‘Best By’ date.”*

![](https://cdn-images-1.medium.com/max/1600/1*gzHFUARuQ7ZoXu8jHn_biA.png)

Key insight: This prompt uses specific terminology from your generated VI Toolkit (“inverse logo,” “body font”) to pull the exact file needed, guaranteeing Consistent Brand Touchpoints even on physical goods.

### Iteration: The Power of Targeted Refinement

A powerful AI like X-Design doesn’t just deliver one result; it delivers a series of designs that you can refine instantly using natural language. This Iterative AI Design capability drastically cuts down on development time.

### 1. Style Iteration

Instead of saying “I don’t like it,” you tell the Agent *how* to change the style based on its initial output.

* *Initial Prompt Result:* A geometric, clean logo.
* *Refinement Prompt:* “Keep the core shape, but make the lines slightly thicker to give it a more established, classic feel.”

### 2. Constraint Iteration

You can test technical changes instantly without restarting the design.

* *Initial Prompt Result:* A logo that looks great on white.
* *Refinement Prompt:* “Show me this design applied to a black background. Does the secondary font still stand out enough, or does it need to be lighter?”

This constant, conversation-based refinement is the fastest path to locking down a Future-Proof Branding asset that satisfies all your deployment needs.

### Conclusion

The ability to write a structured, detailed prompt is now the most valuable skill for any entrepreneur. In the era of the X-Design AI Agent, the complexity of a design brief is no longer limited by your software skills but by your imagination and descriptive language.

By adopting the three-layer X-Design Prompt Structure, you ensure that your visual identity is built on a strategic foundation, not random luck. This process turns your simple idea into a Visual Brand Brief that delivers professional-grade assets, allowing you to bypass the traditional design industry and build a cohesive brand on your own terms. Master the prompt, and you master your AI Brand Strategy.

For the modern small business owner, the creative process has changed forever. You no longer need to know how to use complex software like Photoshop; you need to know how to write an effective AI Prompt Engineering for Branding prompt.
<img src="https://cdn-images-1.medium.com/max/1600/1*mq4Z-LZN8T_07_YKsBgcXw.png" alt="">
A prompt is more than a keyword; it is the Natural Language Design brief that the X-Design AI Agent uses to build your entire visual world. The difference between a generic result and a professional, cohesive brand lies entirely in the structure and detail of that instruction.
Most generic tools operate on simple keyword matching. If you type “YouTube logo,” you get a generic YouTube Channel logo. X-Design <a href="https://www.x-design.com/ai-logo-generator">AI logo generator</a> operates by interpreting a true Visual Brand Brief, allowing you to embed your company’s story, industry standards, and required assets into a single command.
Mastering the prompt is mastering your brand’s destiny. It eliminates the back-and-forth common with human designers and moves you straight to a professional, on-brand result.
<h3 id="the-x-design-prompt-structure-a-three-layer-formula">The X-Design Prompt Structure: A Three-Layer Formula</h3>
To unlock the full potential of X-Design and AI Prompt Engineering for Branding, you need to think of your prompt not as a sentence, but as a formula with three distinct layers. This X-Design Prompt Structure ensures the AI builds your brand on a solid, strategic foundation.
<img src="https://cdn-images-1.medium.com/max/1600/1*u4TAaBg3hlgl8OCZFJ5uvA.png" alt="">
<h3 id="layer-1-the-foundation-what-you-areselling">Layer 1: The Foundation (What You Are Selling)</h3>
This layer provides the essential context. It grounds the AI in your business reality — who you are, what you sell, and who you are trying to reach.
Industry &#x26; Product: Define the tangible subject.
Example: “An artisan bakery specializing in sourdough and rustic pastries.”
Brand Tone &#x26; Feeling: Define the abstract mood and emotional atmosphere.
Example: “The mood should be warm, trustworthy, and heritage-focused.”
Target Audience: Define who the design should appeal to.
Example: “Young professionals and families in an urban, upscale market.”
Why it matters: Generic AI knows what “sourdough” looks like. X-Design, using this foundation, understands that a “heritage-focused” design for “upscale market” must use specific typography and a muted color palette, automatically enforcing brand standards before you even ask for them.
<h3 id="layer-2-the-visual-dna-how-it-mustlook">Layer 2: The Visual DNA (How It Must Look)</h3>
This is the creative layer where you dictate the specific aesthetic choices. It converts abstract ideas into tangible visual styles.
Style Guidance: Define the artistic treatment.
Example: “Minimalist line art, hand-drawn aesthetic, or geometric symbol.”
Color Palette: Specify the exact emotional color set.
Example: “Earthy and natural color palette, specifically using deep burnt orange and off-white.”
Typography Preference: Guide the font choice for the logo design.
Example: “A clean, modern serif font for the name, with a supporting sans-serif.”
Why it matters: This layer provides Design Language Prompt elements. By specifying “Minimalist line art” and “deep burnt orange,” you prevent the AI from defaulting to bright, generic colors and complex vector graphics, ensuring a unique result that aligns with your vision.
<h3 id="layer-3-the-refinement--constraint-the-professional-finish">Layer 3: The Refinement &#x26; Constraint (The Professional Finish)</h3>
This layer applies final quality checks and details required for professional deployment. It is where you move from a nice graphic to a usable Logo System.
Technical Constraint: Specify deployment needs.
Example: “Must be able to work in a circular stamp format and be legible when printed small.”
Misuse/Avoid: Tell the AI what not to include.
Example: “Avoid overly complicated symbols or any representation of a chef’s hat.”
Iteration Strategy: Prompt for a specific change.
Example: “Based on this design, generate three more concepts that only change the geometric symbol.”
Why it matters: Constraints are powerful. By demanding a design that is “legible when printed small,” you force the AI Prompt Engineering for Branding system to prioritize clarity and technical robustness, which is critical for Iterative AI Design and final file usability.
<h3 id="prompt-application-from-logo-to-marketing-templates">Prompt Application: From Logo to Marketing Templates</h3>
The ultimate test of AI Prompt Engineering for Branding is its ability to scale across different visual assets while maintaining a single, consistent look. X-Design uses the same prompt language, but adapts it to the specific format of each asset.
<h3 id="1-logo-prompt-formulas-the-master-visual-brandbrief">1. Logo Prompt Formulas (The Master Visual Brand Brief)</h3>
The logo prompt is the most important, as it defines your Visual Identity Toolkit. It should be comprehensive, combining all three layers to create the foundational asset. You can also use the photo enhancer tool to <a href="https://www.x-design.com/image-enhancer">improve the logo quality</a>.
Formula Example: “[LAYER 1: Artisan bakery specializing in rustic pastries. Brand tone should feel grounded and reliable.] [LAYER 2: Use a clean geometric symbol combined with a strong, modern serif font. The color palette is restricted to deep forest green and cream.] [LAYER 3: Ensure the logo is suitable for a circular coffee stamp and is highly legible at small sizes.]”
<img src="https://cdn-images-1.medium.com/max/1600/1*qCpHhtfJ1dhUQK9QIqJRJQ.png" alt="">
<h3 id="2-posterflyer-prompts-the-marketing-brief">2. Poster/Flyer Prompts (The Marketing Brief)</h3>
Once the Logo System is established, the prompt for marketing materials shifts focus from what the brand is to what the brand is doing, while the AI automatically enforces the stored colors and fonts.
Formula Example: “[ACTION: Create a square social media poster for a special holiday promotion: 25% off all pastries before 10 AM on Monday.] [VISUALS: Use a high-quality photo of a steaming latte with a soft bokeh background. The headline must be bold and prominent.] [CONSTRAINT: Must adhere to the approved ‘Forest Green’ color palette and use the primary headline font.]”
<img src="https://cdn-images-1.medium.com/max/1600/1*volJTVkpNc_pNR2lG0n71w.png" alt="">
Key insight: You don’t prompt for color or font here; you prompt for marketing strategy, and the AI handles the Design Rule Enforcement instantly, ensuring On-Brand Posters.
<h3 id="3-product-packaging-prompts-the-productbrief">3. Product Packaging Prompts (The Product Brief)</h3>
These prompts focus on application and physical constraint. You must tell the AI where the logo needs to be placed and the material it will interact with.
Formula Example: “[PRODUCT: Design packaging for a retail coffee bag.] [VISUALS: Apply the ‘inverse’ (white) version of the logo system to the dark kraft paper background. Use a simple, informational layout.] [CONSTRAINT: Use the body font for all product description text. Design must include space for a stamped ‘Best By’ date.”
<img src="https://cdn-images-1.medium.com/max/1600/1*gzHFUARuQ7ZoXu8jHn_biA.png" alt="">
Key insight: This prompt uses specific terminology from your generated VI Toolkit (“inverse logo,” “body font”) to pull the exact file needed, guaranteeing Consistent Brand Touchpoints even on physical goods.
<h3 id="iteration-the-power-of-targeted-refinement">Iteration: The Power of Targeted Refinement</h3>
A powerful AI like X-Design doesn’t just deliver one result; it delivers a series of designs that you can refine instantly using natural language. This Iterative AI Design capability drastically cuts down on development time.
<h3 id="1-style-iteration">1. Style Iteration</h3>
Instead of saying “I don’t like it,” you tell the Agent how to change the style based on its initial output.
<ul>
<li>Initial Prompt Result: A geometric, clean logo.</li>
<li>Refinement Prompt: “Keep the core shape, but make the lines slightly thicker to give it a more established, classic feel.”</li>
</ul>
<h3 id="2-constraint-iteration">2. Constraint Iteration</h3>
You can test technical changes instantly without restarting the design.
<ul>
<li>Initial Prompt Result: A logo that looks great on white.</li>
<li>Refinement Prompt: “Show me this design applied to a black background. Does the secondary font still stand out enough, or does it need to be lighter?”</li>
</ul>
This constant, conversation-based refinement is the fastest path to locking down a Future-Proof Branding asset that satisfies all your deployment needs.
<h3 id="conclusion">Conclusion</h3>
The ability to write a structured, detailed prompt is now the most valuable skill for any entrepreneur. In the era of the X-Design AI Agent, the complexity of a design brief is no longer limited by your software skills but by your imagination and descriptive language.
By adopting the three-layer X-Design Prompt Structure, you ensure that your visual identity is built on a strategic foundation, not random luck. This process turns your simple idea into a Visual Brand Brief that delivers professional-grade assets, allowing you to bypass the traditional design industry and build a cohesive brand on your own terms. Master the prompt, and you master your AI Brand Strategy.

Do you want to know about AI prompt engineering for branding and how it can help you create your brand identity? Read this guide on how effective prompts generate better results.

AI Prompt Engineering: The Brand Designer's New Language

![](https://cdn-images-1.medium.com/max/1600/1*x2Swk7sY439NC06pVRJ7sg.png)

I work in the real world on real applications that are pretty large, complex and do more than just add a TODO item to a small list. Therefore I have been very skeptical about AI coming in and being this miracle that the AI companies would have you believe. (*I have still seen no evidence of that btw. despite the recent mentions of AGI.*)

Instead, **I have seen AI as more of another tool to use in a developers’s toolkit.** It can be a very useful tool in some circumstances but a tool none the less. Therefore, I like to make small incremental steps when introducing any AI into my workflow.

Recently I have been trying out the [Atlassian MCP server](https://github.com/sooperset/mcp-atlassian?tab=readme-ov-file). I have been quite impressed so far and think it can help in my existing workflow and make my daily developer life a little better.

The README is really well documented and it was easy to get it setup in Cursor. I got it connected to our Jira board that we use at work all within a few minutes.

Basically you have to:

* Generate an api token from Atlassian
* Pull the docker image that runs this MCP server
* Add the MCP tool to your Cursor config

```
{  "mcpServers": {    "mcp-atlassian": {      "command": "docker",      "args": [        "run",        "-i",        "--rm",        "-e", "CONFLUENCE_URL",        "-e", "CONFLUENCE_USERNAME",        "-e", "CONFLUENCE_API_TOKEN",        "-e", "JIRA_URL",        "-e", "JIRA_USERNAME",        "-e", "JIRA_API_TOKEN",        "ghcr.io/sooperset/mcp-atlassian:latest"      ],      "env": {        "CONFLUENCE_URL": "https://your-company.atlassian.net/wiki",        "CONFLUENCE_USERNAME": "your.email@company.com",        "CONFLUENCE_API_TOKEN": "your_confluence_api_token",        "JIRA_URL": "https://your-company.atlassian.net",        "JIRA_USERNAME": "your.email@company.com",        "JIRA_API_TOKEN": "your_jira_api_token"      }    }  }}
```

The MCP server currently provides 42 tools which seems an awful lot and to be honest I haven’t tried them all out yet.

#### How I Use the Atlassian MCP to Scope Out Issues

There is a `get_issue` tool in this MCP and by pasting in the link to the issue, you can give your Cursor AI agent direct access to the whole contents of a specific issue in Jira.

> This is really nice when there are a bunch of comments and descriptions. Saves you having to copy and paste a bunch of data.

I then ask the AI agent to review the ticket and make a Plan (only plan for now!), **I have noticed the AI agent can be a bit keen sometimes to start cranking away at solutions** before it really ‘understands’ the issue.

I then review the plan carefully and see if I agree with the plan and also if its even referencing the right area and files of the application. **I like to take my time here because if the AI has the right context, the right files and the right instructions then it tends to do a pretty good job updating any code**.

If you leave things too open or ambiguous then the AI agent has a tendency to start doing some random things which helps no one and wastes time and money.

Once it has come up with suggestions and I am happy with it I will either do the coding myself or let the AI have a go. It really depends on the issue and the context.

#### Creating Issues Through the Atlassian MCP

This is another great use case I have found. How often have you been working away in the code and find an issue that has nothing to do with the current thing you are working on. It’s too big an issue to fix in your current PR but it’s not big enough to be a major issue.

You make a mental note — “Make a ticket for that later” — because you don’t want to ruin your flow by leaving the editor and creating a Jira ticket, but then you never do and **the bug silently persists in your code base**.

With the Jira MCP in Cursor, you can just press `cmd + i` tag the file/s and ask the AI agent to create a Jira issue in the right project. This can be done quickly, you don’t lose your flow and the actual ticket content will likely be better than if you did it yourself as AI is good at writing these things.

#### Creating Proposals or RFCs in Confluence

We use Confluence in our company for certain documentation. Whilst this is great, the thought of writing out proposals or docs can sometimes be pretty uninspiring and tedious. It’s a very useful practice when working in a team but you don’t always have the motivation.

Again, this is where AI agents can shine. **Being able to do this from your editor is really convenient and useful.** It also makes it easier to reference existing code.

I use this to create initial drafts as I always find it easier to update and amend existing content rather than creating something from scratch.

***

### Conclusion

I prefer the idea of incremental improvements in my workflows rather than big sweeping changes. I feel the Atlassian MCP is a great little addition to parts of my workflow that I previously found frustrating.

Creating accurate issues and documentation is an essential part of being a Software engineer and being able to have some useful help in this area makes this whole experience better.

Do you use the Atlassian MCP? If so, how do you use it? What other MCPs do you use and recommend?

***

### Subscribe to My Weekly Updates!

#### Enjoyed This Post?

Enjoyed this one? Why then, hunt down that “clap” button and *get clicking.* It can be clicked up to 50 times, per reader!

If you found this blog post helpful, why not stay updated with my latest content? [Subscribe](https://adam-drake-frontend-developer.medium.com/subscribe/) to receive email notifications every time I publish.

#### What You’ll Get

By subscribing, you’ll get:

* **Exciting Discoveries:** Be the first to know about the latest tools and libraries.
* **How-To Guides:** Step-by-step articles to enhance your development skills.
* **Opinion Pieces:** Thought-provoking insights into the world of frontend development.

#### Join Our Community

I live in the vibrant city of Prague, Czech Republic, with my family. My blog is more than just articles; it’s a community of like-minded developers who share a love for innovation and learning.

#### About me

I’m a passionate Frontend Developer specialising in React and TypeScript. My professional journey revolves around exploring and mastering new tools and libraries within the JavaScript ecosystem.

*Check out the original blog post on my* [*blog*](https://www.adamdrake.dev/blog)*.*

*Check out my* [*LinkedIn*](https://www.linkedin.com/in/adam-drake-ab065417/) *and* [*Github*](https://github.com/adamdrake210) *if you are interested.*

<img src="https://cdn-images-1.medium.com/max/1600/1*x2Swk7sY439NC06pVRJ7sg.png" alt="">
I work in the real world on real applications that are pretty large, complex and do more than just add a TODO item to a small list. Therefore I have been very skeptical about AI coming in and being this miracle that the AI companies would have you believe. (I have still seen no evidence of that btw. despite the recent mentions of AGI.)
Instead, I have seen AI as more of another tool to use in a developers’s toolkit. It can be a very useful tool in some circumstances but a tool none the less. Therefore, I like to make small incremental steps when introducing any AI into my workflow.
Recently I have been trying out the <a href="https://github.com/sooperset/mcp-atlassian?tab=readme-ov-file">Atlassian MCP server</a>. I have been quite impressed so far and think it can help in my existing workflow and make my daily developer life a little better.
The README is really well documented and it was easy to get it setup in Cursor. I got it connected to our Jira board that we use at work all within a few minutes.
Basically you have to:
<ul>
<li>Generate an api token from Atlassian</li>
<li>Pull the docker image that runs this MCP server</li>
<li>Add the MCP tool to your Cursor config</li>
</ul>
<pre><code class="code-highlight">{ "mcpServers": { "mcp-atlassian": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "CONFLUENCE_URL", "-e", "CONFLUENCE_USERNAME", "-e", "CONFLUENCE_API_TOKEN", "-e", "JIRA_URL", "-e", "JIRA_USERNAME", "-e", "JIRA_API_TOKEN", "ghcr.io/sooperset/mcp-atlassian:latest" ], "env": { "CONFLUENCE_URL": "https://your-company.atlassian.net/wiki", "CONFLUENCE_USERNAME": "your.email@company.com", "CONFLUENCE_API_TOKEN": "your_confluence_api_token", "JIRA_URL": "https://your-company.atlassian.net", "JIRA_USERNAME": "your.email@company.com", "JIRA_API_TOKEN": "your_jira_api_token" } } }}
</code></pre>
The MCP server currently provides 42 tools which seems an awful lot and to be honest I haven’t tried them all out yet.
<h4 id="how-i-use-the-atlassian-mcp-to-scope-outissues">How I Use the Atlassian MCP to Scope Out Issues</h4>
There is a <code>get_issue</code> tool in this MCP and by pasting in the link to the issue, you can give your Cursor AI agent direct access to the whole contents of a specific issue in Jira.
<blockquote>
This is really nice when there are a bunch of comments and descriptions. Saves you having to copy and paste a bunch of data.
</blockquote>
I then ask the AI agent to review the ticket and make a Plan (only plan for now!), I have noticed the AI agent can be a bit keen sometimes to start cranking away at solutions before it really ‘understands’ the issue.
I then review the plan carefully and see if I agree with the plan and also if its even referencing the right area and files of the application. I like to take my time here because if the AI has the right context, the right files and the right instructions then it tends to do a pretty good job updating any code.
If you leave things too open or ambiguous then the AI agent has a tendency to start doing some random things which helps no one and wastes time and money.
Once it has come up with suggestions and I am happy with it I will either do the coding myself or let the AI have a go. It really depends on the issue and the context.
<h4 id="creating-issues-through-the-atlassian-mcp">Creating Issues Through the Atlassian MCP</h4>
This is another great use case I have found. How often have you been working away in the code and find an issue that has nothing to do with the current thing you are working on. It’s too big an issue to fix in your current PR but it’s not big enough to be a major issue.
You make a mental note — “Make a ticket for that later” — because you don’t want to ruin your flow by leaving the editor and creating a Jira ticket, but then you never do and the bug silently persists in your code base.
With the Jira MCP in Cursor, you can just press <code>cmd + i</code> tag the file/s and ask the AI agent to create a Jira issue in the right project. This can be done quickly, you don’t lose your flow and the actual ticket content will likely be better than if you did it yourself as AI is good at writing these things.
<h4 id="creating-proposals-or-rfcs-in-confluence">Creating Proposals or RFCs in Confluence</h4>
We use Confluence in our company for certain documentation. Whilst this is great, the thought of writing out proposals or docs can sometimes be pretty uninspiring and tedious. It’s a very useful practice when working in a team but you don’t always have the motivation.
Again, this is where AI agents can shine. Being able to do this from your editor is really convenient and useful. It also makes it easier to reference existing code.
I use this to create initial drafts as I always find it easier to update and amend existing content rather than creating something from scratch.
<hr>
<h3 id="conclusion">Conclusion</h3>
I prefer the idea of incremental improvements in my workflows rather than big sweeping changes. I feel the Atlassian MCP is a great little addition to parts of my workflow that I previously found frustrating.
Creating accurate issues and documentation is an essential part of being a Software engineer and being able to have some useful help in this area makes this whole experience better.
Do you use the Atlassian MCP? If so, how do you use it? What other MCPs do you use and recommend?
<hr>
<h3 id="subscribe-to-my-weeklyupdates">Subscribe to My Weekly Updates!</h3>
<h4 id="enjoyed-thispost">Enjoyed This Post?</h4>
Enjoyed this one? Why then, hunt down that “clap” button and get clicking. It can be clicked up to 50 times, per reader!
If you found this blog post helpful, why not stay updated with my latest content? <a href="https://adam-drake-frontend-developer.medium.com/subscribe/">Subscribe</a> to receive email notifications every time I publish.
<h4 id="what-youllget">What You’ll Get</h4>
By subscribing, you’ll get:
<ul>
<li>Exciting Discoveries: Be the first to know about the latest tools and libraries.</li>
<li>How-To Guides: Step-by-step articles to enhance your development skills.</li>
<li>Opinion Pieces: Thought-provoking insights into the world of frontend development.</li>
</ul>
<h4 id="join-our-community">Join Our Community</h4>
I live in the vibrant city of Prague, Czech Republic, with my family. My blog is more than just articles; it’s a community of like-minded developers who share a love for innovation and learning.
<h4 id="about-me">About me</h4>
I’m a passionate Frontend Developer specialising in React and TypeScript. My professional journey revolves around exploring and mastering new tools and libraries within the JavaScript ecosystem.
Check out the original blog post on my <a href="https://www.adamdrake.dev/blog">blog</a>.
Check out my <a href="https://www.linkedin.com/in/adam-drake-ab065417/">LinkedIn</a> and <a href="https://github.com/adamdrake210">Github</a> if you are interested.

Using the Atlassian MCP server in Cursor

Finding a reliable [AI video generator](https://www.capcut.com/tools/ai-video-generator) that actually saves time feels harder than it should. The majority of tools that I have tried claim to deliver instant videos but produce videos that are difficult to watch, slow, or contain heavy watermarks. That is time-wasting and it takes away the creative momentum of small teams or individual creators like myself. I have tried almost all the options that are available in the market, and the following are some of the most thought out solutions that really work. This list eliminates the hype and narrows down to tools that produce useful outputs in a short period of time. Every pick outlines the presence of actual free-tier functionality, convenient workflows, and actual export options. The guide is perfect when you need quick social videos, voice-over explainers, or short videos without having to spend hours editing ads. My recommendations are pegged on personal experience, direct comparison, and the urge to utilize the tools that would indeed accelerate the process of video production.![](https://cdn-images-1.medium.com/max/1600/1*_vqFiRDONEmv77Z0KmFSJw.png)

By 2025, I have been able to see how AI-powered fast video tools have revolutionized how creators produce and disseminate content. Marketing specialists, independent YouTubers, and every type of people are interested in faster, more mechanism-based workflows that will not potentially compromise the quality. The most significant challenge that I had to resolve is to find free platforms that deliver professional results but are not too complicated. Each of the tools listed below is tried personally regarding its availability, video quality and creative freedom. They are the surest choices in the mind of any individual who wants to take his/her content strategy to the next level through the employment of intelligent time-saving automation.



### Best Free Online AI Video Generators in 2025

1. **CapCut —** Best for [AI avatar](https://www.capcut.com/tools/ai-avatar)-based videos with one-click editing and fast, engaging short-form content
2. **FlexClip —** Best for quick text-to-video editing
3. **HeyGen —** Best for realistic talking video generation
4. **Lumen5 —** Best for turning articles into videos
5. **InVideo —** Best for marketing and social content creation
6. **Simplified —** Best for team-based video collaboration
7. **Animoto —** Best for beginners creating slideshow-style videos

![](https://cdn-images-1.medium.com/max/1600/1*DpBEcEq5tBIiDmOqY3F-Ug.png)

#### **1. CapCut**

CapCut stands out as one of the most versatile and user-friendly AI video generator platforms in 2025. It enables every person to create full video instantly through AI automation. The platform provides AI avatars, AI templates, and AI-powered automation that transform text prompts into professional-grade content. The Instant AI Video tool of CapCut does it all, including the pacing of the scenes and the background image, making the videos look refined within just a few seconds. It’s also the ideal [YouTube Shorts maker](https://www.capcut.com/create/youtube-shorts-maker), letting creators produce social-ready content quickly and efficiently.

**Main Functional Attributes:**

* **AI Avatars:** Offers over 100 digital avatars for instant video generation. Includes custom avatar cloning to create a personalized on-screen representation that matches specific branding or identity needs.
* **AI Workflow Templates:** Comes with 30+ ready-made templates for news, education, and marketing videos. Each template includes pre-structured scenes to maintain professional pacing, tone, and flow throughout the video.
* **Instant AI Video:** Video generation on scripts with one click. Handles the editing process, visual integration and timing changes automatically and without manual video assembly.
* **AI Brainstorming Tool:** A creative assistant that proposes ideas, storyboards, and individual directions. Developed to address creative blocks and boost ideation in the marketing, education, or content development process.
* **Unlimited Media Assets:** Allows unlimited access to a large collection of stock footage, music, fonts and graphics. Ideal to tailor videos and create content that is engaging and will optimize on social media.

![](https://cdn-images-1.medium.com/max/1600/1*d5O_7w5nU_iDdRvCpeZLRw.png)

**Advantages:**

* Simple interface suitable for all skill levels
* Produces professional-quality videos in minutes
* Large selection of avatars and templates
* Strong AI automation saves time and effort
* Free to use with powerful AI-driven workflow

**Disadvantages:**

* Some features require pro account to access

#### **2. FlexClip**

FlexClip is a fast and effective online converter of turning scripts into videos. It simplifies text-to-video using AI-based templates and auto-captioning. The users can input the text quickly, choose the visuals and leave the system to develop the fluent transitions and layouts. It is most effective on social media content, tutorials and explainer videos.

![](https://cdn-images-1.medium.com/max/1600/1*ZI3CnFFnNnDVBLQxkZfTsA.png)

**Advantages:**

* Auto-subtitles improve accessibility
* Easy drag-and-drop timeline
* Offers hundreds of stock clips and music tracks
* Ideal for beginners needing fast results

**Disadvantages:**

* Limited advanced editing features
* Free plan includes watermark on exports

#### **3. HeyGen**

HeyGen does a superb job at creating a realistic talking-head video on the basis of a text. Its AI renders facial expressions, lipsync, and expressions which accompany custom voiceovers. It is extremely frequent with educational videos, product demonstrations and corporate explanations. The interface also has a strong background, language, and tone control that makes HeyGen a professional yet easy to use platform.

![](https://cdn-images-1.medium.com/max/1600/1*ebA9soR6u4G4pY-H3u3cqg.png)

**Advantages:**

* Highly realistic facial animation
* Supports multiple languages and voices
* Perfect for business and training presentations
* Produces natural speech and expressions

**Disadvantages:**

* Limited design customization options
* Slower rendering time on free accounts

#### **4. Lumen5**

Lumen5 is a firm that specializes in transforming text contents and blog posts into a trending video. It splits up written text, highlights critical sentences and automatically aligns images, animation and background music. This AI video generator is designed for marketers and bloggers who want to repurpose written content into video form quickly.

![](https://cdn-images-1.medium.com/max/1600/1*2sTLrr9j7owDa2yiouCssA.png)

**Advantages:**

* Automatic summarization of articles
* Large library of visuals and template
* Custom branding options for social media
* Generates videos from URLs and text input

**Disadvantages:**

* Customization is limited in free version
* Watermark appears on free exports

#### **5. InVideo**

InVideo is created to be utilized by marketers who want to make social videos on the large scale. Its AI platform automatically converts text into already shared clips that have professional transitions and animations. It possesses a massive media library and customizable scenes. Best in making advertisement, brand promotion and event videos with bare minimum effort.

![](https://cdn-images-1.medium.com/max/1600/1*PYlPiDlP2glDftxIZ6Fxkg.png)

**Advantages:**

* AI converts text scripts to videos quickly
* Offers numerous social media presets
* Built-in voiceover and subtitle tools
* Excellent marketing templates for quick deployment

**Disadvantages:**

* Interface can feel cluttered
* Export quality slightly reduced in free plan

#### **6. Simplified**

Simplified is a combination of AI-based video generation and team collaboration tools. It facilitates real-time editing, sharing and comments. Users are capable of producing AI-powered videos to market a product, provide tutorials, or advertisements. The platform is also connected with other creative tools to ensure brand consistency throughout all assets.

![](https://cdn-images-1.medium.com/max/1600/1*jtyK9zzaAwg5DUp03sPB_g.png)

**Advantages:**

* Real-time team editing and commenting
* Intuitive dashboard with AI assistance
* Integrates with marketing workflows
* Offers automated resizing for different platforms

**Disadvantages:**

* Performance can lag during collaboration
* Limited video duration in free plan

#### **7. Animoto**

Animoto is a simple application that is meant to be utilized in the process of creating simple videos that are in the form of slideshows. It turns images, texts and short videos into seamless and beautiful videos. The AI assists in transitions, rhythm and background music selection. It can best be used in delivering quick content, event highlights, and simple social updates.

![](https://cdn-images-1.medium.com/max/1600/1*aeRtvc3lgU7WLd2MNgPlxA.png)

**Advantages:**

* Extremely simple drag-and-drop interface
* Offers auto-timing and music sync
* Ideal for educators, beginners, and small businesses
* Supports direct sharing to social media platforms

**Disadvantages:**

* Limited customization options
* Output looks basic compared to advanced tools

### Comparison Table: Best Free Online AI Video Generators

![](https://cdn-images-1.medium.com/max/1600/1*zugeStvg_SH8rMpnp5dWdw.png)

### Conclusion

Choosing the right AI video generator depends on personal goals, workflow, and the kind of content being produced. Some are more of a storyteller and some are more of an automator or collaborator. They have been tested and compared, and one platform was of the same quality, creative, and easy to use at all times CapCut.

CapCut remains the most helpful one in 2025. Its AI avatar and YouTube Shorts maker capabilities make it ideal for both individuals and brands. The most excellent overall experience is the automation, creative flexibility and the intuitive design in terms of free video generation.

The needs of all people are diverse, but in the case of generating a quality AI-driven video quickly, CapCut will be the first tool.

### FAQs

**Which free online AI video generator is best for beginners in 2025?**

CapCut is the best appropriate one. It automates video creation through AI avatars, templates, and instant video generation. The friendly interface allows full projects even without technical expertise.

**Can I use CapCut’s AI avatar feature to make talking videos?**

Yes. CapCut offers over 100 AI avatars for text-to-speech and talking video generation. The users are even able to produce their own avatars to ensure that they receive voiceover content that is customized.

**Are these AI video generators truly free?**

Most platforms, including CapCut, FlexClip, and Lumen5 offer free plans. However, these advanced functions as 4K export or high-quality templates often require upgrade.

**Which AI video generator works best for social media and YouTube Shorts?**

CapCut is the most appropriate choice in terms of social and short-form content. Its YouTube Shorts maker and instant AI editing tools make it ideal for quick, visually engaging posts.

Finding a reliable <a href="https://www.capcut.com/tools/ai-video-generator">AI video generator</a> that actually saves time feels harder than it should. The majority of tools that I have tried claim to deliver instant videos but produce videos that are difficult to watch, slow, or contain heavy watermarks. That is time-wasting and it takes away the creative momentum of small teams or individual creators like myself. I have tried almost all the options that are available in the market, and the following are some of the most thought out solutions that really work. This list eliminates the hype and narrows down to tools that produce useful outputs in a short period of time. Every pick outlines the presence of actual free-tier functionality, convenient workflows, and actual export options. The guide is perfect when you need quick social videos, voice-over explainers, or short videos without having to spend hours editing ads. My recommendations are pegged on personal experience, direct comparison, and the urge to utilize the tools that would indeed accelerate the process of video production.<img src="https://cdn-images-1.medium.com/max/1600/1*_vqFiRDONEmv77Z0KmFSJw.png" alt="">
By 2025, I have been able to see how AI-powered fast video tools have revolutionized how creators produce and disseminate content. Marketing specialists, independent YouTubers, and every type of people are interested in faster, more mechanism-based workflows that will not potentially compromise the quality. The most significant challenge that I had to resolve is to find free platforms that deliver professional results but are not too complicated. Each of the tools listed below is tried personally regarding its availability, video quality and creative freedom. They are the surest choices in the mind of any individual who wants to take his/her content strategy to the next level through the employment of intelligent time-saving automation.
<h3 id="best-free-online-ai-video-generators-in2025">Best Free Online AI Video Generators in 2025</h3>
<ol>
<li>CapCut — Best for <a href="https://www.capcut.com/tools/ai-avatar">AI avatar</a>-based videos with one-click editing and fast, engaging short-form content</li>
<li>FlexClip — Best for quick text-to-video editing</li>
<li>HeyGen — Best for realistic talking video generation</li>
<li>Lumen5 — Best for turning articles into videos</li>
<li>InVideo — Best for marketing and social content creation</li>
<li>Simplified — Best for team-based video collaboration</li>
<li>Animoto — Best for beginners creating slideshow-style videos</li>
</ol>
<img src="https://cdn-images-1.medium.com/max/1600/1*DpBEcEq5tBIiDmOqY3F-Ug.png" alt="">
<h4 id="1-capcut">1. CapCut</h4>
CapCut stands out as one of the most versatile and user-friendly AI video generator platforms in 2025. It enables every person to create full video instantly through AI automation. The platform provides AI avatars, AI templates, and AI-powered automation that transform text prompts into professional-grade content. The Instant AI Video tool of CapCut does it all, including the pacing of the scenes and the background image, making the videos look refined within just a few seconds. It’s also the ideal <a href="https://www.capcut.com/create/youtube-shorts-maker">YouTube Shorts maker</a>, letting creators produce social-ready content quickly and efficiently.
Main Functional Attributes:
<ul>
<li>AI Avatars: Offers over 100 digital avatars for instant video generation. Includes custom avatar cloning to create a personalized on-screen representation that matches specific branding or identity needs.</li>
<li>AI Workflow Templates: Comes with 30+ ready-made templates for news, education, and marketing videos. Each template includes pre-structured scenes to maintain professional pacing, tone, and flow throughout the video.</li>
<li>Instant AI Video: Video generation on scripts with one click. Handles the editing process, visual integration and timing changes automatically and without manual video assembly.</li>
<li>AI Brainstorming Tool: A creative assistant that proposes ideas, storyboards, and individual directions. Developed to address creative blocks and boost ideation in the marketing, education, or content development process.</li>
<li>Unlimited Media Assets: Allows unlimited access to a large collection of stock footage, music, fonts and graphics. Ideal to tailor videos and create content that is engaging and will optimize on social media.</li>
</ul>
<img src="https://cdn-images-1.medium.com/max/1600/1*d5O_7w5nU_iDdRvCpeZLRw.png" alt="">
Advantages:
<ul>
<li>Simple interface suitable for all skill levels</li>
<li>Produces professional-quality videos in minutes</li>
<li>Large selection of avatars and templates</li>
<li>Strong AI automation saves time and effort</li>
<li>Free to use with powerful AI-driven workflow</li>
</ul>
Disadvantages:
<ul>
<li>Some features require pro account to access</li>
</ul>
<h4 id="2-flexclip">2. FlexClip</h4>
FlexClip is a fast and effective online converter of turning scripts into videos. It simplifies text-to-video using AI-based templates and auto-captioning. The users can input the text quickly, choose the visuals and leave the system to develop the fluent transitions and layouts. It is most effective on social media content, tutorials and explainer videos.
<img src="https://cdn-images-1.medium.com/max/1600/1*ZI3CnFFnNnDVBLQxkZfTsA.png" alt="">
Advantages:
<ul>
<li>Auto-subtitles improve accessibility</li>
<li>Easy drag-and-drop timeline</li>
<li>Offers hundreds of stock clips and music tracks</li>
<li>Ideal for beginners needing fast results</li>
</ul>
Disadvantages:
<ul>
<li>Limited advanced editing features</li>
<li>Free plan includes watermark on exports</li>
</ul>
<h4 id="3-heygen">3. HeyGen</h4>
HeyGen does a superb job at creating a realistic talking-head video on the basis of a text. Its AI renders facial expressions, lipsync, and expressions which accompany custom voiceovers. It is extremely frequent with educational videos, product demonstrations and corporate explanations. The interface also has a strong background, language, and tone control that makes HeyGen a professional yet easy to use platform.
<img src="https://cdn-images-1.medium.com/max/1600/1*ebA9soR6u4G4pY-H3u3cqg.png" alt="">
Advantages:
<ul>
<li>Highly realistic facial animation</li>
<li>Supports multiple languages and voices</li>
<li>Perfect for business and training presentations</li>
<li>Produces natural speech and expressions</li>
</ul>
Disadvantages:
<ul>
<li>Limited design customization options</li>
<li>Slower rendering time on free accounts</li>
</ul>
<h4 id="4-lumen5">4. Lumen5</h4>
Lumen5 is a firm that specializes in transforming text contents and blog posts into a trending video. It splits up written text, highlights critical sentences and automatically aligns images, animation and background music. This AI video generator is designed for marketers and bloggers who want to repurpose written content into video form quickly.
<img src="https://cdn-images-1.medium.com/max/1600/1*2sTLrr9j7owDa2yiouCssA.png" alt="">
Advantages:
<ul>
<li>Automatic summarization of articles</li>
<li>Large library of visuals and template</li>
<li>Custom branding options for social media</li>
<li>Generates videos from URLs and text input</li>
</ul>
Disadvantages:
<ul>
<li>Customization is limited in free version</li>
<li>Watermark appears on free exports</li>
</ul>
<h4 id="5-invideo">5. InVideo</h4>
InVideo is created to be utilized by marketers who want to make social videos on the large scale. Its AI platform automatically converts text into already shared clips that have professional transitions and animations. It possesses a massive media library and customizable scenes. Best in making advertisement, brand promotion and event videos with bare minimum effort.
<img src="https://cdn-images-1.medium.com/max/1600/1*PYlPiDlP2glDftxIZ6Fxkg.png" alt="">
Advantages:
<ul>
<li>AI converts text scripts to videos quickly</li>
<li>Offers numerous social media presets</li>
<li>Built-in voiceover and subtitle tools</li>
<li>Excellent marketing templates for quick deployment</li>
</ul>
Disadvantages:
<ul>
<li>Interface can feel cluttered</li>
<li>Export quality slightly reduced in free plan</li>
</ul>
<h4 id="6-simplified">6. Simplified</h4>
Simplified is a combination of AI-based video generation and team collaboration tools. It facilitates real-time editing, sharing and comments. Users are capable of producing AI-powered videos to market a product, provide tutorials, or advertisements. The platform is also connected with other creative tools to ensure brand consistency throughout all assets.
<img src="https://cdn-images-1.medium.com/max/1600/1*jtyK9zzaAwg5DUp03sPB_g.png" alt="">
Advantages:
<ul>
<li>Real-time team editing and commenting</li>
<li>Intuitive dashboard with AI assistance</li>
<li>Integrates with marketing workflows</li>
<li>Offers automated resizing for different platforms</li>
</ul>
Disadvantages:
<ul>
<li>Performance can lag during collaboration</li>
<li>Limited video duration in free plan</li>
</ul>
<h4 id="7-animoto">7. Animoto</h4>
Animoto is a simple application that is meant to be utilized in the process of creating simple videos that are in the form of slideshows. It turns images, texts and short videos into seamless and beautiful videos. The AI assists in transitions, rhythm and background music selection. It can best be used in delivering quick content, event highlights, and simple social updates.
<img src="https://cdn-images-1.medium.com/max/1600/1*aeRtvc3lgU7WLd2MNgPlxA.png" alt="">
Advantages:
<ul>
<li>Extremely simple drag-and-drop interface</li>
<li>Offers auto-timing and music sync</li>
<li>Ideal for educators, beginners, and small businesses</li>
<li>Supports direct sharing to social media platforms</li>
</ul>
Disadvantages:
<ul>
<li>Limited customization options</li>
<li>Output looks basic compared to advanced tools</li>
</ul>
<h3 id="comparison-table-best-free-online-ai-video-generators">Comparison Table: Best Free Online AI Video Generators</h3>
<img src="https://cdn-images-1.medium.com/max/1600/1*zugeStvg_SH8rMpnp5dWdw.png" alt="">
<h3 id="conclusion">Conclusion</h3>
Choosing the right AI video generator depends on personal goals, workflow, and the kind of content being produced. Some are more of a storyteller and some are more of an automator or collaborator. They have been tested and compared, and one platform was of the same quality, creative, and easy to use at all times CapCut.
CapCut remains the most helpful one in 2025. Its AI avatar and YouTube Shorts maker capabilities make it ideal for both individuals and brands. The most excellent overall experience is the automation, creative flexibility and the intuitive design in terms of free video generation.
The needs of all people are diverse, but in the case of generating a quality AI-driven video quickly, CapCut will be the first tool.
<h3 id="faqs">FAQs</h3>
Which free online AI video generator is best for beginners in 2025?
CapCut is the best appropriate one. It automates video creation through AI avatars, templates, and instant video generation. The friendly interface allows full projects even without technical expertise.
Can I use CapCut’s AI avatar feature to make talking videos?
Yes. CapCut offers over 100 AI avatars for text-to-speech and talking video generation. The users are even able to produce their own avatars to ensure that they receive voiceover content that is customized.
Are these AI video generators truly free?
Most platforms, including CapCut, FlexClip, and Lumen5 offer free plans. However, these advanced functions as 4K export or high-quality templates often require upgrade.
Which AI video generator works best for social media and YouTube Shorts?
CapCut is the most appropriate choice in terms of social and short-form content. Its YouTube Shorts maker and instant AI editing tools make it ideal for quick, visually engaging posts.

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