Claude API Request Timeout Handling: A Practical Guide

Claude API request timeouts happen when a completion takes longer than your client (or an intermediate proxy) is willing to wait. The fix isn't just "increase the timeout" — it's understanding why long-running generations time out in the first place and building retry, streaming, and fallback logic that handles the failure gracefully instead of just failing louder.

This matters because timeout errors in LLM APIs are different from timeouts against a typical REST backend. Generation time scales with output length and model load, not with payload size, so a request that worked fine yesterday can time out today simply because the model generated more tokens or the queue was busier. Below is a breakdown of where timeouts come from and how to handle each case.

Why Claude API requests time out

Long generations. A request asking for a 4,000-token response can legitimately take 30-90+ seconds. If your HTTP client, load balancer, or serverless function has a default timeout of 10-30 seconds, it will kill the connection before Claude finishes — even though the request would have succeeded given more time.

Non-streaming requests on large outputs. Without streaming, your client waits for the entire response body before returning anything. This is the single biggest cause of avoidable timeouts: you're blocking on total generation time instead of receiving tokens incrementally.

Network-layer timeouts you didn't set yourself. Load balancers (ALB defaults to 60s), API gateways, reverse proxies (nginx proxy_read_timeout), and serverless platforms (Vercel functions, AWS Lambda) all have their own timeout ceilings that override whatever you set in your HTTP client code.

Transient infra issues. Occasionally a request just stalls — a dropped connection, a slow upstream hop — unrelated to generation length. These need retry logic, not longer timeouts.

Fix 1: use streaming for anything non-trivial

If a response can run more than a few hundred tokens, stream it. Streaming returns tokens as they're generated, so your connection stays active and you get partial output immediately instead of waiting for the full completion.

const response = 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-opus-4",
    max_tokens: 2048,
    stream: true,
    messages: [{ role: "user", content: "Summarize this contract in detail." }],
  }),
});

const reader = response.body.getReader();
const decoder = new TextDecoder();
let fullText = "";

while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  fullText += decoder.decode(value);
}

Streaming doesn't eliminate timeouts on its own — your client or proxy can still cut the connection — but it turns a single long blocking call into a steady trickle of bytes, which keeps most idle-connection timeouts from triggering and lets you show progress to users. See /docs/streaming for the full event format.

Fix 2: set client timeouts above expected generation time, not below it

A common mistake is leaving the HTTP client's default timeout in place (often 5-10 seconds) and then being surprised when long completions fail. Set an explicit, generous timeout based on your max_tokens value:

const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(), 120_000); // 120s

try {
  const response = 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: 4096,
      messages: [{ role: "user", content: "Generate a detailed test plan." }],
    }),
    signal: controller.signal,
  });
  clearTimeout(timeout);
} catch (err) {
  if (err.name === "AbortError") {
    console.error("Request timed out after 120s");
  }
}

Rough rule of thumb: budget at least 1-2 seconds per 100 output tokens requested, plus a margin for queueing, and round up generously.

Fix 3: check every layer, not just your code

If you've set a 120-second client timeout and requests still die around 60 seconds, the client isn't the bottleneck. Check, in order:

• Reverse proxy / nginx: proxy_read_timeout, proxy_connect_timeout
• Load balancer: AWS ALB idle timeout (60s default), GCP load balancer backend timeout
• Serverless function limits: Vercel (10-60s depending on plan), Lambda (900s max, but often configured lower), Cloudflare Workers
• API gateway: AWS API Gateway caps at 29 seconds regardless of what you configure downstream

Each of these needs to be raised independently — fixing only the client-side timeout while a load balancer still cuts off at 60 seconds will produce intermittent, hard-to-diagnose failures.

Fix 4: retry with backoff, but only for the right errors

Not every timeout should be retried blindly. A request that timed out because it was legitimately generating 8,000 tokens will time out again on retry if you don't also adjust max_tokens or switch to streaming. Retries make sense for transient network stalls, not for structurally long requests.

async function callWithRetry(payload, maxRetries = 3) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      return await callClaudeAPI(payload);
    } catch (err) {
      const isTimeout = err.name === "AbortError" || err.code === "ETIMEDOUT";
      if (!isTimeout || attempt === maxRetries - 1) throw err;
      await new Promise((r) => setTimeout(r, 1000 * 2 ** attempt));
    }
  }
}

Fix 5: reduce the chance of timing out in the first place

• Lower max_tokens when you don't need a long answer — shorter requested outputs finish faster and time out less often
• Split very large tasks (long documents, multi-step generations) into smaller sequential calls
• Use streaming by default for any user-facing feature, not just the ones you expect to be slow
• Log actual response times per request so you can set timeouts based on real data instead of guesses

If you're routing Claude access through SubToAPI, requests go through the same /v1/messages endpoint with streaming and standard HTTP semantics, so the timeout handling above applies directly — you don't need a separate SDK or proxy-specific retry logic. Usage metadata returned with each response also makes it easier to correlate slow requests with token counts when you're tuning timeout budgets. Check /docs/messages for request/response details or /docs/quickstart to get an API key running in a few minutes.

questions

What's a safe default timeout for Claude API requests? There's no single safe number because it depends on max_tokens and whether you're streaming. For non-streaming calls, set the client timeout to at least 2x your worst-case expected generation time (often 90-180 seconds for large outputs). For streaming, you can usually use a shorter per-chunk idle timeout instead of one long total timeout.

Should I retry every timeout automatically? No. Retry transient network timeouts with exponential backoff, but if a request times out because it's generating a genuinely long response, retrying with the same parameters will likely time out again — reduce max_tokens or switch to streaming instead.

Why does my request time out even after raising the client timeout? Something upstream of your code — a load balancer, API gateway, or serverless platform — likely has its own shorter timeout ceiling that overrides your client setting. Check every layer in the request path, not just your HTTP client configuration.