When 15 Minutes Isn't Enough: Overcoming Lambda Timeout Limits

“My Lambda Timeout Is Set to 15 Minutes—Why Isn’t My Process Finishing?”

You’ve maxed out your AWS Lambda timeout to 15 minutes, yet your process still doesn’t complete because the external API takes too long to respond.

Sound familiar?

If you’re wondering, “I already increased the timeout to the max—what else can I do?”, you’re not alone.

I’ve been there myself.

On one project, I had a setup where AWS Lambda was calling a heavy external API for data processing. The average response time was around 10 minutes—but sometimes it stretched to 13 or even 14.

With Lambda’s 15-minute limit, I thought I was safe. But in reality, network latency and service instability pushed us over the edge again and again. All I saw in the logs was:

Task timed out after 900.00 seconds

That was the moment I hit the limits of Lambda.

In this post, I’ll explain how to break free from the constraints of time-bound functions like Lambda by adopting asynchronous processing patterns—and how to design for them effectively.

We’ll walk through the basics of async processing, using Lambda and EC2 as practical examples.

By the end, you’ll see that async design isn’t that hard—and you’ll be equipped to confidently handle workloads longer than 15 minutes.

You’ll gain the ability to build reliable systems without fearing timeouts.

What Is Asynchronous Processing?

Asynchronous processing means "continuing without waiting for other tasks to finish."

By delegating or decoupling tasks, you can avoid issues like timeouts and blocking delays.

Synchronous vs. Asynchronous Processing

Synchronous processing:

The program waits until a task is complete before moving on.
The caller blocks until a response is returned.
Delays in APIs or I/O can cause serious bottlenecks.

# Synchronous example in Python
response = requests.get("https://api.example.com/data")
print(response.json())  # Waits for the response before continuing

Asynchronous processing:

The program continues while tasks are still running
Results are handled once they're ready
Heavy tasks can run on separate threads or processes

# Asynchronous example using Python asyncio
async def fetch_data():
    response = await aiohttp.get("https://api.example.com/data")
    return await response.json()

Quick Summary:

Perspective	Synchronous	Asynchronous
Behavior	Waits until done	Moves on while waiting
Best Use	Lightweight tasks	Heavy or external tasks
Downsides	Easily blocked by slow ops	Slightly more complex design
Example	Direct API call & wait	Offload to queue and process separately

How to Handle Workloads That Exceed Lambda’s 15-Minute Limit

→ TL;DR: Offload to EC2 using an asynchronous architecture!

✅ Step 1: Offload Long-Running Tasks to EC2 via a Queue**

Don’t run the process now—queue it up to run later.

▶ Architecture Overview:

The frontend or Lambda function sends a processing request to SQS.
A worker application on EC2 polls SQS and picks up the request.
The EC2 worker handles the long-running external API call (10, 20 minutes—no problem).
Once done, it stores results in a database or sends a notification.

💡 System Diagram

[Client or Lambda]
        ↓
     [SQS - Task Queue]
        ↓
[EC2 Worker Polls Queue]
        ↓
[Executes Long-Running API Call]
        ↓
[Saves Result / Sends Notification]

✅ Benefits:

No longer restricted by Lambda’s 15-minute limit
EC2 can run for hours if needed
SQS ensures retry on failure
Add more workers for parallel processing

✅ Step 2: Add Request Tracking with Status Visibility

Use request IDs to track processing state.

Generate a unique ID (UUID) when receiving a request.
Use this ID to check status later via an endpoint like GET /status?id=xxx.
EC2 workers update the database with statuses like “completed,” “failed,” or “pending.”

✅ Benefits:

Clients can check the status of their requests
Improves user experience and transparency
Makes debugging and recovery easier

✅ Step 3: Notify on Completion via Webhooks or Alerts

Automate post-processing actions when tasks finish.

Once EC2 finishes processing, it triggers a webhook
Send notifications via Slack, email, or through API Gateway

✅ Benefits:

Clients don’t need to poll for updates
Processing chains can continue automatically
Enables event-driven architecture

Summary: When Lambda Isn’t Enough, Rethink the Design

Problem	Solution	Keywords
Lambda times out at 15 minutes	Offload to EC2 via async processing	SQS / EC2 worker pattern
Users asking “What’s the status?”	Add status tracking with request ID	UUID / status API
Want real-time completion alerts	Use webhooks or SNS notifications	Event hooks / Slack alerts

✅ Mastering Async Design Unlocks True Scalability

Relying solely on Lambda ties you to its 15-minute constraint.

But when you rethink your architecture—by offloading long-running tasks and embracing asynchronous design—you’re free to build resilient, scalable systems without compromise.

Heppoko @heppoko