Claude API Request Timeout Troubleshooting Guide

A Claude API request timeout almost always comes from one of four places: a client-side timeout set too low for the response size, a long non-streaming request that exceeds your HTTP client's default window, network instability between your server and the API, or the model genuinely taking longer than expected on a large context or complex generation. The fix depends on which one you're hitting, and the fastest way to find out is to look at how long the request actually ran before it failed versus your configured timeout value.

This guide walks through the most common causes of Claude API timeouts, how to diagnose which one applies to you, and concrete configuration changes that resolve them in production.

Step 1: Identify where the timeout is coming from

Before changing any code, confirm the timeout isn't simply your HTTP client's default. Most languages ship with conservative defaults:

• Node's fetch has no default timeout, but libraries like axios default to no timeout either — so a hang often means the connection itself is stuck, not a client limit.
• Python's requests has no default timeout at all, meaning a stalled connection can hang indefinitely until the OS kills it.
• Many HTTP clients in Go, Java, and Ruby default to timeouts between 10–30 seconds, which is too short for large generations.

Check your error message. A ETIMEDOUT, ECONNABORTED, or a client-level "timeout exceeded" error (not an API response at all) means the request never completed on the wire — this is a client or network issue. If instead you get an actual HTTP response with a 5xx status or a timeout-specific error body, that's a server-side or gateway timeout, which needs different handling.

Step 2: Raise your client timeout to match realistic generation time

Longer outputs and larger max_tokens values take longer to generate. A 4,000-token response can easily take 30–90 seconds in a non-streaming request. If your timeout is set to 10 or 15 seconds, you will see failures on anything beyond short replies.

const response = await fetch("https://api.example.com/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${API_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "claude-model",
    max_tokens: 4096,
    messages: [{ role: "user", content: "Summarize this document..." }]
  }),
  signal: AbortSignal.timeout(120000) // 120s, not the default
});

As a rule of thumb, set client timeouts to at least 2–3x your expected worst-case response time, not your average case. If you don't know your worst case, test with your largest realistic max_tokens and prompt size first.

Step 3: Switch to streaming for long-running requests

The single most effective fix for timeout errors on long generations is to stop waiting for the full response and stream it instead. Streaming sends tokens as they're generated, so your connection stays active with continuous data rather than sitting idle until the entire response is ready. This avoids both client-side timeouts and most intermediate proxy/load-balancer timeouts, which often kill idle connections well before your application-level timeout fires.

const response = await fetch("https://api.subtoapi.app/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${SUBTOAPI_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "claude-model",
    max_tokens: 4096,
    stream: true,
    messages: [{ role: "user", content: "Write a detailed report on..." }]
  })
});

const reader = response.body.getReader();
// process chunks as they arrive instead of waiting for completion

If you're building on SubToAPI, see /docs/streaming for the exact event format and how to parse incremental deltas. Streaming is also the right call if you're proxying requests through a reverse proxy like nginx or a serverless function — many of these have hard idle-connection limits (commonly 30–60 seconds) that non-streaming long requests will hit regardless of your own client configuration.

Step 4: Check for upstream proxy and load balancer timeouts

If you're running behind nginx, AWS ALB, Cloudflare, or a serverless gateway (Lambda, Vercel functions, Cloud Run), each of these has its own timeout ceiling independent of your application code:

• nginx: proxy_read_timeout defaults to 60s
• AWS ALB: idle timeout defaults to 60s
• Lambda: hard 15-minute execution cap, but API Gateway caps at 29s unless you're using Lambda function URLs or streaming responses
• Vercel serverless functions: 10–60s depending on plan, unless using Edge Functions with streaming

If your timeout error only happens in production and not locally, this is almost always the cause. Raise the relevant proxy timeout, or better, move to streaming so the connection isn't idle long enough to trip these limits in the first place.

Step 5: Add retry logic with exponential backoff

Transient network blips and brief upstream slowdowns happen. Rather than treating every timeout as fatal, retry with backoff for a bounded number of attempts:

async function requestWithRetry(payload, maxRetries = 3) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      return await fetch("https://api.subtoapi.app/v1/messages", {
        method: "POST",
        headers: {
          "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
          "Content-Type": "application/json"
        },
        body: JSON.stringify(payload),
        signal: AbortSignal.timeout(90000)
      });
    } catch (err) {
      if (attempt === maxRetries - 1) throw err;
      await new Promise(r => setTimeout(r, 2 ** attempt * 1000));
    }
  }
}

Don't retry indefinitely — cap attempts at 3–5 and surface a clear error to the user or calling service after that, rather than silently hanging the request pipeline.

Step 6: Reduce request size where possible

If timeouts correlate with specific requests rather than being random, check prompt and context size. Very large contexts increase processing time before the first token is even generated. Trimming unnecessary context, summarizing earlier conversation turns instead of replaying them in full, or lowering max_tokens to what you actually need can meaningfully cut response time and reduce timeout risk, especially on non-streaming calls.

If you're using SubToAPI as your gateway, the /docs/messages reference covers request structure and size limits, and /docs/quickstart has a working setup you can compare your implementation against if you're unsure whether the issue is in your request shape.

FAQs

Why does my Claude API request time out only on long responses? Non-streaming requests wait for the entire generation to finish before returning anything. Long outputs with high max_tokens naturally take longer, so a client or proxy timeout set for short requests fails silently once responses grow. Switching to streaming resolves this in most cases.

Should I increase my timeout or switch to streaming? Increasing the timeout is a quick patch, but streaming is the more reliable fix because it avoids idle-connection limits imposed by proxies and load balancers that you may not control. Use streaming for any request where output length is unpredictable or potentially large.

How many times should I retry a timed-out request? Cap retries at 3–5 attempts with exponential backoff. More than that risks compounding load during a genuine outage and delays surfacing the error to whoever needs to act on it.