Claude API Lambda Function Cold Start Guide

Why Claude API Calls Are Slow on Lambda Cold Starts

If your AWS Lambda function calling the Claude API feels fast on warm invocations but takes 1–3 extra seconds on the first request, that's a cold start — not a Claude API latency problem. Cold starts happen when AWS has to provision a new execution environment: download your deployment package, start the runtime, run your module-level code, and then execute your handler. Any SDK initialization, TLS handshake setup, or dependency loading you do before the Claude request even goes out adds to that delay.

The fix isn't one thing — it's a combination of reducing what runs before your first API call, reusing connections across invocations, and, if latency is business-critical, using provisioned concurrency so cold starts don't happen on the request path at all. Below is what actually moves the needle, in order of effort.

What Actually Causes the Delay

A cold start calling an LLM API has three distinct cost centers:

1. Lambda environment init — downloading and unpacking your deployment package or container image. Larger bundles (heavy SDKs, unused dependencies) take longer.
2. Runtime/module init — anything at the top of your file: SDK client construction, loading credentials, parsing config.
3. The actual Claude API call — DNS resolution, TLS handshake, and the model's own generation time. This part is the same whether it's a cold or warm start, but on a cold Lambda, you're paying for a new TLS handshake because there's no reused connection.

Most developers blame "the Claude API" for slowness that's actually steps 1 and 2. You can't do much about step 3 beyond choosing a faster model or streaming the response, but steps 1 and 2 are fully in your control.

Fix 1: Initialize Clients Outside the Handler

The single biggest win is moving SDK/HTTP client creation to module scope so it only runs once per container, not once per invocation:

// GOOD: client created once, reused across warm invocations
const https = require("https");
const agent = new https.Agent({ keepAlive: true, maxSockets: 10 });

exports.handler = async (event) => {
  const response = await fetch("https://api.subtoapi.app/v1/messages", {
    method: "POST",
    agent, // reuse TCP/TLS connection on warm invocations
    headers: {
      "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "claude-sonnet-4",
      max_tokens: 500,
      messages: [{ role: "user", content: event.prompt }],
    }),
  });
  return await response.json();
};

If you put the https.Agent or your API client inside the handler, you rebuild a TCP connection and redo the TLS handshake on every single invocation — cold or warm. That's a self-inflicted cold start on every request.

Fix 2: Trim the Deployment Package

Every unused dependency in node_modules or your Python site-packages adds to download and unzip time during environment init. Common offenders:

• Full AWS SDK v2 bundled by default — switch to the modular AWS SDK v3 or only import what you use.
• Heavyweight HTTP client libraries when fetch/https would do.
• Unused LLM provider SDKs left over from testing multiple vendors.

For a Lambda whose only job is proxying to an API, a plain fetch call is often faster to cold-start than a full official SDK with its own retry logic, telemetry, and type validation layers.

Fix 3: Use Provisioned Concurrency for Latency-Sensitive Paths

If your Lambda sits behind a user-facing chat UI where even 1 second matters, provisioned concurrency keeps a configured number of execution environments warm and ready:

aws lambda put-provisioned-concurrency-config \
  --function-name claude-proxy \
  --qualifier prod \
  --provisioned-concurrent-executions 5

This costs more (you pay for idle capacity) but eliminates cold starts entirely for the provisioned instances. It's the right call for production traffic with predictable volume; it's overkill for internal tools or low-traffic batch jobs.

Fix 4: Keep Connections Warm with Scheduled Pings — Carefully

A cheap trick is a CloudWatch Events rule that invokes your function every 5–10 minutes to keep a container warm. It works, but has limits: it only keeps one container warm per schedule, it doesn't help under concurrent traffic spikes, and AWS can still recycle the environment. Treat it as a stopgap, not a real fix, and prefer provisioned concurrency for anything with real concurrency requirements.

Fix 5: Stream Responses to Mask Perceived Latency

Cold start adds fixed overhead before anything happens, but if your function streams the Claude response back to the client instead of waiting for the full completion, users see the first tokens sooner — the cold start delay becomes less noticeable relative to total response time. SubToAPI supports streaming over the same HTTPS endpoint you'd use for standard requests; see /docs/streaming for the event format if you're piping it through a Lambda response stream.

Simplifying the Lambda Side

A lot of cold start overhead in LLM-calling Lambdas comes from code that has nothing to do with the actual request: API key rotation logic, retry/backoff wrappers, usage logging, and provider-specific auth. If your Lambda talks to SubToAPI instead of managing raw model credentials, that handler code shrinks to an HTTP call with a static bearer token — fewer dependencies, smaller bundle, faster init. The /docs/quickstart guide shows the minimal request shape, and signing up at /signup gives you a sub_live_... key you can drop straight into process.env without building your own credential-refresh logic.

A Minimal Cold-Start-Friendly Lambda

exports.handler = async (event) => {
  const res = await fetch("https://api.subtoapi.app/v1/messages", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "claude-sonnet-4",
      max_tokens: 300,
      messages: [{ role: "user", content: event.body }],
    }),
  });
  const data = await res.json();
  return { statusCode: 200, body: JSON.stringify(data) };
};

No SDK to download, no client object to construct, one fetch call. This is about as close to zero module-init overhead as you'll get for a function calling Claude.

Questions

Does Claude's model generation time contribute to cold start? No — model inference latency is the same whether your Lambda container is warm or cold. Cold start overhead comes entirely from AWS provisioning the execution environment and your own module-level initialization code, not from the Claude API itself.

Will provisioned concurrency eliminate cold starts completely? It eliminates them for the number of instances you provision. If traffic exceeds that, Lambda still spins up additional cold containers on demand, so size provisioned concurrency to your expected baseline concurrency.

Is a scheduled "warming" ping a reliable fix? It helps for low-concurrency functions but doesn't scale — it typically keeps only one container warm and won't prevent cold starts during traffic bursts. For predictable latency under load, provisioned concurrency is the more reliable option.