How to Handle Claude API Timeouts

A Claude API timeout happens when a request takes longer than your client, proxy, or server is willing to wait for a response — not necessarily because something is broken. The fix depends on where the timeout is actually occurring: your HTTP client's default timeout, a reverse proxy or load balancer in front of your app, a serverless function's execution limit, or Anthropic's own processing time for long or complex completions.

The short answer: increase your client-side timeout to a value proportional to your expected output length, use streaming for anything that generates more than a few hundred tokens, and add retry logic with exponential backoff for the requests that genuinely fail. Below is the reasoning behind each of those, plus code you can drop into a real project.

Why Claude API requests time out

Large language model responses aren't instant. Generation time scales with output length, and a request asking for a 4,000-token response can legitimately take 30–60 seconds or more, especially with larger models or complex reasoning tasks. Common causes of timeouts fall into a few buckets:

• Client default timeouts — many HTTP libraries default to 10–30 seconds, which is too short for non-streamed long completions.
• Serverless execution limits — platforms like AWS Lambda, Vercel Functions, or Cloudflare Workers cap how long a single invocation can run.
• Reverse proxy timeouts — nginx, load balancers, or API gateways often have their own idle/read timeout settings independent of your application code.
• Network instability — dropped connections mid-stream, especially on mobile networks or unstable Wi-Fi.
• Actual overload — during high-traffic periods, response latency can increase, pushing borderline requests over your timeout threshold.

Diagnosing which of these applies is the first step. If short prompts also time out, the problem is almost never generation time — it's your client, proxy, or network config.

Set client-side timeouts that match your workload

Don't use a single global timeout for every request. A short classification prompt returning 50 tokens should time out fast if something's wrong; a long-form generation task needs much more headroom.

import fetch from "node-fetch";

async function callClaude(prompt, { maxTokens = 1024, timeoutMs = 60000 } = {}) {
  const controller = new AbortController();
  const timer = setTimeout(() => controller.abort(), timeoutMs);

  try {
    const res = await fetch("https://api.anthropic.com/v1/messages", {
      method: "POST",
      headers: {
        "x-api-key": process.env.ANTHROPIC_API_KEY,
        "anthropic-version": "2023-06-01",
        "content-type": "application/json",
      },
      body: JSON.stringify({
        model: "claude-3-5-sonnet-latest",
        max_tokens: maxTokens,
        messages: [{ role: "user", content: prompt }],
      }),
      signal: controller.signal,
    });
    return await res.json();
  } finally {
    clearTimeout(timer);
  }
}

A rough rule of thumb: allow at least 1–2 seconds per 100 output tokens requested, with a floor of 30 seconds and no hard ceiling for very long outputs unless your infrastructure requires one.

Use streaming to avoid timeouts entirely

The most effective fix for timeout-related failures on long completions isn't a bigger timeout — it's switching from a single blocking request to a stream. With streaming, you receive tokens as they're generated, so your connection stays active and you can show partial output immediately instead of waiting for the full response.

This also sidesteps serverless execution limits in many cases, since you're processing data incrementally rather than holding a connection open with no activity until the very end.

const res = await fetch("https://api.anthropic.com/v1/messages", {
  method: "POST",
  headers: {
    "x-api-key": process.env.ANTHROPIC_API_KEY,
    "anthropic-version": "2023-06-01",
    "content-type": "application/json",
  },
  body: JSON.stringify({
    model: "claude-3-5-sonnet-latest",
    max_tokens: 2048,
    stream: true,
    messages: [{ role: "user", content: "Write a detailed report on..." }],
  }),
});

const reader = res.body.getReader();
const decoder = new TextDecoder();
while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  process.stdout.write(decoder.decode(value));
}

If you're proxying requests through SubToAPI, the same pattern works against https://api.subtoapi.app/v1/messages with stream: true and your sub_live_ key — see the streaming docs for a full walkthrough of consuming server-sent events in a browser or backend context.

Check every layer, not just your code

If streaming and client timeouts are configured correctly and you're still seeing failures, look upstream:

• Reverse proxies: nginx's default proxy_read_timeout is often 60 seconds — raise it for endpoints that stream or generate long output.
• Load balancers: AWS ALB idle timeout defaults to 60 seconds and silently drops idle connections; increase it for LLM traffic.
• Serverless limits: Vercel's default function timeout and Lambda's configured timeout need to be explicitly raised, or you need to switch to a streaming-compatible runtime (edge functions, for example).
• CDNs: some CDNs buffer responses and won't forward partial streamed chunks until the full response is buffered, defeating the purpose of streaming.

Retry only what's safe to retry

Not every timeout should trigger a retry. If a request timed out on the client side but Claude actually processed it and generated a response, blindly retrying can waste tokens and money, and in agentic workflows with tool calls, can cause duplicate side effects.

A safer approach:

• Retry timeouts on read-only or idempotent operations automatically.
• For anything with side effects (writing to a database, calling an external API via tool use), use an idempotency key on your own side to detect and discard duplicate completions.
• Use exponential backoff (1s, 2s, 4s, capped around 30s) with a maximum of 3–4 attempts, not unbounded retries.

Where SubToAPI fits in

If you're building on top of Claude and want timeout handling, retries, and usage visibility without maintaining that infrastructure yourself, SubToAPI turns your existing Claude access into a standard HTTPS API with sub_live_ application keys, built-in streaming support, and per-key usage metadata so you can see which requests are slow or failing before they become support tickets. Check pricing or start with the quickstart guide to see the request/response shape before integrating.

FAQs

What's a reasonable timeout value for the Claude API? For non-streamed requests, scale it with max_tokens — roughly 1–2 seconds per 100 tokens with a 30-second floor. For streaming, use a shorter connect timeout (10–15s) but a much longer or no read timeout, since the connection should stay active with incoming chunks.

Does streaming eliminate timeouts completely? No, but it eliminates the most common cause: a proxy or client killing an idle connection while waiting for a large blocking response. You can still hit network drops or execution limits, so pair streaming with reconnect logic for production use.

Should I retry every timed-out Claude API request? Only for idempotent, read-only requests. For requests with side effects — tool calls, database writes, sending messages — use an idempotency key and check whether the original request actually succeeded before retrying automatically.