Understanding Cold Starts in AWS Lambda

Introduction

A cold start is the extra, behind-the-scenes work AWS Lambda performs whenever it must build a fresh execution environment to run your function because no warm environment is available. That seemingly small preparation hides several distinct steps: AWS allocates an isolated microVM/container (Firecracker), attaches required networking (including VPC plumbing when applicable), starts the language runtime (Node.js, Python, Java, .NET, etc.), mounts and loads your deployment package and any layers, initializes Lambda extensions, and runs all global/static initialization code in your function package. Only after those steps are completed does AWS Lambda invoke your handler. Collectively, these backend activities are called the INIT phase, and the time they consume is what we observe as cold-start latency.

Key Features of Strands

• In on-demand execution environments, AWS Lambda provisions isolated containers only when needed, which conserves cost but can cause initialization delay.
• INIT vs INVOKE phases, cold starts add an INIT phase (runtime start + global/static init) before the normal handler INVOKE.
• Concurrency-driven scaling, each concurrent request gets its own environment; sudden scale-outs often create many new environments (and thus cold starts).
• AWS mitigation features, such as built-in controls like Provisioned Concurrency and SnapStart, reduce or remove the INIT penalty for reserved or snapshotted instances.

Benefits of Cold Start

• Improved latency predictability means fewer unexpected spikes in request latency.
• Better user experience, interactive APIs, and frontends remain responsive.
• Controlled costs, chose targeted provisioning only for critical routes instead of over-provisioning whole systems.
• Operational clarity, measuring INIT Duration and tail percentiles, gives concrete signals on which to act.

Use Case

• Real-time APIs (e.g., login systems, chat applications).
• Interactive chatbots, gaming backends, and trading systems.
• Low-traffic, sporadic functions (where idleness makes cold starts frequent).
• Bursty workloads that cause many environments to be created quickly.

How to Detect Cold Start

You can spot cold starts by monitoring execution duration in Amazon CloudWatch. Cold starts are occasional spikes, especially when functions haven’t been used for a while. Logging the first request in a fresh environment also helps confirm it.

Implementation Patterns (how to control backend INIT)

• Do nothing (default)
  Let AWS Lambda manage environments on demand. INIT happens on scale-up or after idle time. Simple, cheapest, but cold starts occur.
• Provisioned Concurrency

The backend keeps N fully initialized environments ready, and AWS Lambda pre-runs INIT, so requests hit INVOKE directly. Good for low-latency SLAs; costs scale with provisioned capacity.

• SnapStart (snapshot & restore)

AWS Lambda snapshots an initialized Firecracker microVM for supported runtimes when you publish a version and restores from that snapshot for new instances. Backend INIT work is done at publish time, not at each cold start. Particularly effective for Java.

• Minimize backend INIT work

Move heavy initialization out of the global scope or make it lazy; reduce package size and layers so less is loaded in INIT. Works across runtimes

Minimal End-to-End (backend flow on a cold start)

1. Invoke arrives with no warm environment → AWS Lambda decides to create an environment.
2. Allocate Firecracker microVM/container and attach network (Hyperplane ENI if VPC).
3. Boot runtime (language-specific) and mount code/layers. Extensions initialize.
4. Runtime signals readiness (INIT completes) and AWS Lambda invokes the handler (INVOKE). Subsequent calls to that environment skip INIT.

Technical Challenges and Optimizations

1. Cost vs latency: Provisioned Concurrency guarantees latency but costs more; tune quantity to traffic patterns.
2. Runtime variance: Java/.NET often have longer INITs; SnapStart helps but requires versioned deployment and care with initialization side effects.
3. Measuring cold starts: Use the Init Duration value in AWS Lambda reports / Amazon CloudWatch Logs to quantify INIT overhead and correlate with tail latencies.
4. Idempotency & resilience: Design for retries and transient failures during scale-outs. Follow AWS Lambda best practices for observability and error handling.

Conclusion

Cold starts are an expected trade-off from AWS Lambda’s on-demand, pay-per-use model. By measuring INIT Duration, picking appropriate runtimes, shrinking init work, and using AWS features like Provisioned Concurrency or SnapStart where needed, you can make latency predictable for the paths that matter while keeping costs under control. Start by measuring, then apply the smallest mitigations that meet your SLA.

Drop a query if you have any questions regarding AWS Lambda and we will get back to you quickly.

About CloudThat