AWS Lambda SnapStart: Eliminating Cold Starts with Firecracker

Introduction

Serverless computing has revolutionized how applications are built and deployed, but it hasn’t been without its challenges. One of the biggest pain points in AWS Lambda has been cold start latency, particularly for runtimes like Java, which require significant initialization time. AWS first tackled cold start issues by optimizing VPC networking, and now, with the introduction of AWS Lambda SnapStart, they’ve taken a major leap forward.

By leveraging Firecracker microVMs, SnapStart allows AWS to capture a memory snapshot of a fully initialized Lambda execution environment and restore it on demand. This means that instead of initializing functions from scratch, AWS can restore them from a preloaded snapshot, eliminating the bulk of cold start overhead.

This blog dives deep into how AWS Lambda SnapStart works, its benefits, limitations, and how Firecracker plays a crucial role in making serverless workloads even faster. We’ll also cover best practices for implementing SnapStart, including how to handle uniqueness constraints and optimize function performance.

Understanding Cold Starts in AWS Lambda

Before diving into SnapStart, it’s essential to understand what a cold start is and why it matters.

When an AWS Lambda function is invoked, AWS spins up an execution environment to run the function. This involves loading the runtime, initializing dependencies, and executing the function handler. This process is nearly instantaneous for languages like Python and Node.js, but for runtimes like Java, which require significant initialization, it can take several seconds.

There are two types of Lambda invocations:

1. Warm starts – If a function has been recently invoked, AWS keeps the execution environment alive for a short period, making subsequent requests fast.
2. Cold starts – If no instance of the function is available, AWS needs to create a new environment, which leads to additional latency.

For real-time applications, such as API-driven services, machine learning inference, or financial transactions, cold starts can be a major bottleneck. AWS Lambda SnapStart directly addresses this issue by caching initialized environments and reusing them efficiently.

How AWS Lambda SnapStart Works

With SnapStart, AWS changes the way execution environments are initialized. Instead of starting from scratch on each invocation, AWS:

1. Initializes the function once – When you publish a new version of your Lambda function, AWS runs the function initialization process, loading dependencies, initializing the runtime, and preparing execution state.
2. Creates a snapshot – AWS then takes a snapshot of the fully initialized environment, including memory and disk state.
3. Caches the snapshot – The snapshot is encrypted and stored in a cache for rapid retrieval.
4. Restores from snapshot – When a new execution environment is required, AWS restores the environment from the preloaded snapshot instead of initializing it from scratch, drastically reducing startup latency.

This entire process is made possible by Firecracker, a lightweight virtualization technology purpose-built for serverless computing.

What is Firecracker and Why Does It Matter?

Firecracker is an open-source virtualization technology designed by AWS to power Lambda, Fargate, and other serverless offerings. Unlike traditional virtualization solutions like QEMU or Xen, Firecracker is built for speed and security, using microVMs to provide strong isolation with minimal overhead.

Key advantages of Firecracker include:

• Faster startup times – Traditional VMs can take seconds to boot, while Firecracker microVMs start in milliseconds.
• Lightweight footprint – Firecracker VMs require minimal resources compared to full-fledged VMs, making them ideal for ephemeral serverless workloads.
• Security by design – Built in Rust, Firecracker provides strong isolation while minimizing attack surfaces.

SnapStart leverages Firecracker to restore Lambda functions from snapshots almost instantaneously, effectively eliminating cold starts for supported runtimes.

SnapStart vs. Provisioned Concurrency: Which One Should You Use?

Before SnapStart, the recommended way to eliminate cold starts was Provisioned Concurrency. Let’s compare the two approaches:

Provisioned Concurrency is more expensive but guarantees instant responses, whereas SnapStart provides significant improvements at a lower cost. For most Java-based applications, SnapStart is the better choice.

SnapStart Limitations and Compatibility Considerations

While SnapStart is a game-changer, it comes with some important caveats:

1. Not all runtimes are supported – As of now, SnapStart is available for Java 11+, Python 3.12, and .NET 8, meaning Node.js, Ruby, and container-based Lambdas aren’t supported.
2. Snapshot reuse may cause issues – Functions relying on UUIDs, randomness, or unique state during initialization may see unintended behavior.
3. No support for provisioned concurrency or EFS – SnapStart cannot be used alongside Amazon EFS or ephemeral storage beyond 512MB.

To mitigate these issues, ensure that:

• Any unique state generation happens inside the function handler, not during initialization.
• Network connections are re-established before each invocation.

Best Practices for Using AWS Lambda SnapStart

If you’re planning to enable SnapStart for your Lambda functions, follow these best practices to avoid common pitfalls:

1. Move Database Connections Outside the Function Handler

If your Lambda function connects to a database (e.g., Amazon RDS, DynamoDB, or MongoDB), move the connection logic outside the function handler. This ensures that:

• Connections persist across invocations, reducing latency.
• Each request doesn’t create a new database connection, which can lead to connection exhaustion.

2. Handle Randomness and Unique Values Properly

Any function that generates UUIDs, random values, or timestamps during initialization may see duplicate values across restored instances.

• Generate unique values inside the function handler, not during initialization.
• If using cryptographic randomness, refresh entropy sources after each invocation.

3. Monitor Performance with AWS CloudWatch

Track cold start frequency, latency, and errors using AWS CloudWatch. Key metrics to monitor include:

• Init Duration – The time taken to initialize your function before snapshotting.
• Duration – The execution time per request.
• Errors – Ensure no issues arise from snapshot restoration.

Real-World Use Cases for AWS Lambda SnapStart

1. High-Traffic APIs

SnapStart is perfect for high-scale REST or GraphQL APIs where low latency is critical. Functions can handle bursts of traffic without experiencing slow cold starts.

2. Financial Transactions

Banks and fintech companies using Java for fraud detection, real-time trading, or transaction processing benefit from SnapStart’s instantaneous cold start performance.

3. Machine Learning Inference

For ML models deployed as Lambda functions, SnapStart helps reduce inference times, ensuring fast response times for AI-driven applications.

Conclusion

AWS Lambda SnapStart is a major advancement in serverless performance, effectively eliminating cold starts for supported runtimes. By leveraging Firecracker microVMs, AWS has made it possible to restore execution environments in milliseconds.

While SnapStart isn’t a one-size-fits-all solution, it’s a game-changer for Java, Python, and .NET-based workloads. If you run latency-sensitive serverless applications, enabling SnapStart could provide huge performance gains with minimal extra cost.

AWS Lamda cold starts are much faster with SnapStart!