Together AI API Timeout Error: Why Requests Hang and How to Fix Them (2026)

Together AI timeouts have a signature that is easy to recognise once you have seen it: the first request to a given model is enormously slow or dies outright, and every request after it is fine. That is not an outage. That is a model being loaded.

A timeout is not a response. Nothing came back, so nothing tells you whether the request reached Together AI, whether it was processed, or whether something in between gave up first. Every other error hands you a status code to search for; this one hands you silence. That is why teams lose hours to it — the debugging instinct is to check the provider's status page, which is the layer least likely to be responsible.

60-second triage: run the same call from your terminal with a deliberately long --max-time. If curl returns a completion, Together AI is healthy and something in your stack killed the request — keep reading. If curl hangs or returns 5xx as well, check live Together AI status and the error code reference instead.

Four layers can kill the request, and only one is Together AI

Before changing any value, find out who enforced the limit. The shortest timeout in the chain wins, and it is almost never the one you configured — it is the one you inherited from a framework default or a hosting platform.

Layer	Typical limit	How it looks
Your HTTP client / SDK	Often a default you never set	An abort or `ETIMEDOUT` raised inside your own code
Serverless function or gateway	10s, 29s, 30s, 60s — round numbers	A platform-generated 504, and your handler's logs stop mid-execution
Proxy, CDN or load balancer	Idle timeout, resets on bytes	Non-streaming calls die, streaming calls survive
Together AI itself	Genuine slowness or an incident	curl fails too, from every machine, at the same time

The round numbers in that second row are the most useful clue on this page. A request that dies at almost exactly 10, 29, 30 or 60 seconds was killed by infrastructure, not by a model. A timeout that only ever happens on the first call after a quiet period, and never under sustained traffic, is a cold start — not an incident, and not something a status page will ever show.

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Prove it with curl before you change code

Take your application entirely out of the picture. This runs the same request with a long ceiling and prints a full timing breakdown, so you can see where the seconds actually went:

curl -sS -o /dev/null --max-time 120 \
  -w "dns=%{time_namelookup}s connect=%{time_connect}s tls=%{time_appconnect}s firstbyte=%{time_starttransfer}s total=%{time_total}s\n" \
  https://api.together.xyz/v1/chat/completions \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"meta-llama/Llama-3.3-70B-Instruct-Turbo","messages":[{"role":"user","content":"ping"}]}'

Read it from the left. A large connect or tls figure is a network or handshake problem and has nothing to do with the model. A small connect with a large firstbyte means the connection was established instantly and Together AI spent the time thinking — that is real processing latency, and the answer is a longer budget or a smaller request, not a retry. If the whole thing completes well inside your application's configured timeout, the request never had a chance and your own limit is the bug.

Why the first Together AI request times out and the rest do not

Together serves a very large catalogue of open models. Keeping every one of them resident on GPUs at all times would be economically impossible, so less-trafficked models are loaded on demand. When your request is the one that triggers the load, you wait for weights to be placed on a GPU before generation starts — and from your client's perspective that is simply a connection producing no bytes.

This is why the failure is so often reported as intermittent and unreproducible. The developer times out, retries, and the second attempt returns in two seconds because the model is now warm. The bug "disappears", then reappears in production at 4am when traffic to that model has been quiet long enough for it to be evicted. Popular Turbo endpoints such as meta-llama/Llama-3.3-70B-Instruct-Turbo are almost always warm; niche fine-tunes and larger or rarer models are the ones that bite.

The practical consequence is that a single timeout value cannot serve both cases. Sizing it for the warm path guarantees cold-start failures; sizing it for the cold path means genuinely stuck requests hold connections for a minute. The answer is not a bigger number — it is to distinguish the two, either by keeping the model warm with a periodic ping, by pinning a dedicated endpoint for latency-sensitive traffic, or by treating the first call as a separate, longer-budgeted operation.

Set two timeouts, not one

A single overall deadline is the wrong shape for this problem. Connecting should be fast and failing to connect should fail immediately; generating can legitimately be slow. Collapsing both into one number means either network failures hang for the full budget, or slow-but-healthy requests get killed. Split them:

// Short connect budget, generous read budget.
async function callTogether(body: unknown) {
  const ac = new AbortController();
  const connectGuard = setTimeout(() => ac.abort(new Error('connect-timeout')), 5_000);
  const readGuard = setTimeout(() => ac.abort(new Error('read-timeout')), 90_000);

  try {
    const res = await fetch('https://api.together.xyz/v1/chat/completions', {
      method: 'POST',
      signal: ac.signal,
      headers: {
        Authorization: `Bearer ${process.env.TOGETHER_API_KEY}`,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify(body),
    });
    clearTimeout(connectGuard); // headers arrived — connection is alive
    if (!res.ok) throw new Error(`upstream ${res.status}`);
    return await res.json();
  } finally {
    clearTimeout(connectGuard);
    clearTimeout(readGuard);
  }
}

Then make sure the platform agrees with you. A 60-second client timeout inside a function capped at 10 seconds is decoration — the platform kills the invocation first and your carefully chosen value never applies. Raise the function's duration limit to sit above the client timeout, or move the call out of the request path entirely.

Streaming turns a total failure into a partial one

The single highest-leverage change is to stop waiting for the whole response. With stream: true the first bytes arrive quickly, which keeps every idle timer in the chain — proxies, load balancers, serverless runtimes — from concluding the connection is dead. Those idle timers are what kill most non-streaming calls long before the total duration cap is reached.

It also changes what failure costs. A buffered request that dies at the deadline returns nothing at all, and you pay for tokens the user never saw. A stream that breaks after several hundred tokens has already delivered something useful and gives you a point to resume from.

With streaming, replace the total deadline with an idle deadline that resets on every chunk. A request that has produced tokens two seconds ago is healthy no matter how long it has been running; one that has produced nothing for thirty seconds is stuck regardless of how recently it started:

// Idle timeout — resets whenever a chunk arrives.
const reader = res.body!.getReader();
let idle = setTimeout(() => reader.cancel('stalled'), 30_000);

while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  clearTimeout(idle);
  idle = setTimeout(() => reader.cancel('stalled'), 30_000);
  yield value;
}
clearTimeout(idle);

Retries that help, and retries that make it worse

Timeouts are the most dangerous class of error to retry blindly, because you do not know what happened. The request may have failed instantly, or it may have been fully processed with the response lost on the way back — and retrying the second case pays for the work twice while adding load to a system that was already struggling.

Retry on zero bytes received. Nothing was generated, so nothing is duplicated. This is the safe case.
Do not blindly retry a mid-stream failure. Tokens were produced and billed; resume or degrade instead.
Always use jitter. Fixed backoff synchronises every client into waves that arrive together and re-fail together.
Cap attempts at two or three, then fail loudly. Unbounded retries turn a provider blip into a self-inflicted outage.
Put a circuit breaker in front. When failures cross a threshold, stop calling for a cooling period — see the Together AI failover guide.

And measure the right number. Averages hide timeouts completely: a p50 of 900 milliseconds tells you nothing about the 1% of requests sitting at your ceiling. Alert on p99 and on the timeout rate itself, because that tail is the entire population of users experiencing the bug.

Frequently Asked Questions

Why does my first Together AI request time out but the retry succeeds?

Because the first request paid for loading the model and the second one did not. Together serves a large catalogue of open models and cannot keep every one resident on GPUs, so infrequently used models are loaded on demand. Your request triggers that load, waits through it with no bytes on the wire, and hits your client timeout; by the time you retry, the weights are in place and the call returns in seconds. This is why the problem looks intermittent and refuses to reproduce during debugging. Keep the model warm with a low-cost periodic request, use a dedicated endpoint for latency-sensitive traffic, or give the first call a deliberately longer budget than subsequent ones.

How do I tell a Together AI cold start from a real outage?

Look at whether it correlates with traffic or with time. A cold start affects the first request after an idle period, resolves on retry, and is specific to one model — other models on the same account respond normally throughout. An outage affects every model at once, does not resolve on retry, and returns 5xx or connection errors rather than a long silence followed by success. Test a known-hot model such as a popular Turbo endpoint in the same moment your rare model is hanging: if the hot one answers, Together is healthy and you are watching a load, not an incident.

What timeout should I use with Together AI?

Use two. Latency-sensitive calls to models you know are warm can sit at 30 seconds, which is generous for a Turbo endpoint. Calls that may cold-start need considerably more headroom — 90 seconds or more for large or rarely used models — and should not be made inside a request-scoped serverless function with a shorter cap, because the platform will kill them regardless of what your client says. Always set a short connect timeout of a few seconds separately, so genuine network failures fail immediately instead of consuming the long read budget you provisioned for loading.

Does streaming help with Together AI timeouts?

It helps once generation starts and does nothing before that. Streaming resets idle timers in proxies and runtimes as soon as chunks flow, which prevents a whole class of premature disconnects, and it gives users visible progress instead of a spinner. But during a cold start there are no chunks to send — the connection is silent while weights load — so an idle timeout shorter than the load time will still fire. Combine streaming with a first-byte timeout that is explicitly sized for loading, and a shorter idle timeout that applies only after the first chunk has arrived.

Can I stop Together AI models from going cold?

You can make it much less likely without eliminating it on shared infrastructure. A cheap keep-warm request on a schedule tuned to the eviction window keeps frequently needed models resident, and costs a trivial fraction of what the failed user requests cost you. For traffic that genuinely cannot absorb a cold start, a dedicated endpoint reserves capacity so the model is always resident and latency stops depending on what everyone else is doing. Failing that, design around it: queue the work, return immediately, and deliver the result asynchronously rather than holding an HTTP request open through a load.

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Together AI API Timeout Error: Why Requests Hang