Together AI API 500 Internal Server Error: Causes, Safe Retries and the Idempotency Trap (2026)

Most teams file every 5xx into one bucket labelled “the provider is having a moment” and point the same retry policy at all of them. That works for four of the five codes. It does not work for 500, because 500 is the only one that tells you the request got far enough to do something before it failed — and the whole question of whether a retry is safe hangs on that one detail.

30-second triage: run the same call with curl from outside your deployment. 500 there too? It is Together AI — check live Together AI status, then read the idempotency section below before you turn retries back on. Succeeds in curl? Something between your app and api.together.xyz is generating that 500, and the fix is on your side.

Which 5xx you actually received

The status code narrows the cause far more than the message body does. Four of these are worth retrying and the differences between them decide how, which matters more than most retry code assumes.

Code	Did the work start?	Retry?
`429`	No — refused on your own allowance	Yes, after backoff — but fix your pacing
`500`	Possibly — it broke mid-handling	Once or twice, and only if idempotent
`502`	Unclear — a gateway got a bad response	Yes — usually brief and self-clearing
`503`	No — no capacity was available	Yes, with jitter and a budget
`504`	Probably — and it may still be running	Only with an idempotency key

One caveat that catches everyone: a 500 in your logs did not necessarily come from api.together.xyz. Load balancers, service meshes and your own web framework all emit 500 when something breaks inside them, and the code is identical. Log the response headers alongside the status — provider responses carry identifying headers that your own gateway's do not. The full code-by-code breakdown lives in the Together AI API error codes reference.

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The idempotency trap

This is the part that costs money. A 500 is returned after the request was accepted, which means the service may have done some or all of the work before it failed. Retrying is therefore not a free action, and the size of the consequence depends entirely on what the call does.

Plain completions: the risk is cost, not correctness. Tokens generated before the failure are generally billable, so an aggressive retry loop on a long generation is a way to pay three times for one answer.
Streaming calls: worse, because a 200 arrived first. The status line said success, tokens flowed, and then the stream died. Your error handler sees a broken stream rather than a 500, and if it retries from the top you have paid for the same prefix twice.
Anything creating durable state: batch submissions, uploads, fine-tuning runs, or any call your own system records. Retry only behind an idempotency key or a ledger you check before re-sending. Otherwise one 500 becomes two jobs, and nobody notices until the bill.

The practical rule is to classify each call site once, in advance, as retry-safe or not, and encode that at the call site rather than in a global policy. A single fleet-wide “retry all 5xx three times” setting cannot distinguish a throwaway completion from a job submission, and it will treat them the same on the worst possible day.

Reproducing it outside your application

Before you attribute anything, take your own stack out of the path. One call, from a machine that is not your deployment, against the exact endpoint your code uses:

curl -i -s -o /dev/null -w "%{http_code} %{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"}],"max_tokens":1}'

Run it on a short loop rather than once. A single sample cannot distinguish a total failure from a partial one, and partial is far more common: seven 200s and three 500s out of ten is a completely different operational situation from ten out of ten failing, and it calls for a different response. Keep the response headers — the request id in particular is the only artefact that makes a support report actionable.

The Together AI trap: a 500 is per-model, and the status page is per-platform

Together AI is a marketplace serving a very large catalogue of models across shared serverless capacity and dedicated endpoints. That architecture makes the platform-level status signal almost useless for diagnosing a 500: the control plane, billing and the great majority of model endpoints can be genuinely healthy while the one model id your product depends on is failing every request. A green status page next to a wall of 500s is not a contradiction here — it is the expected reading.

So resolve the scope before anything else. Send the same request to a second model id on the same key. If that succeeds, you have a per-model incident, and the correct response is to move traffic to the working model rather than to wait for a platform-wide fix that is not coming because there is nothing platform-wide to fix. Keeping one known-good fallback model id in configuration — tested, not merely written down — converts this from an outage into a config change.

Dedicated endpoints add a second, quieter 500 source. An endpoint that has scaled to zero, is mid-restart, or failed to come up returns 5xx while it settles, and the shape of that failure looks nothing like shared-capacity pressure: it is total for one endpoint and invisible everywhere else, and it clears on the endpoint’s own schedule. Check the endpoint’s state in the console before assuming the model itself is broken.

A retry policy that fits a 500 specifically

The policy you use for 503 is close, but not identical, and the two differences matter. A capacity refusal rewards patience; a mid-handling failure often does not, because the same input can reproduce the same failure indefinitely.

Cap attempts lower. Two attempts, not five. If the second fails the same way, the input is more likely the variable than the clock, and further attempts are just spend.
Back off with full jitter. Randomise across the whole window rather than adding a small wobble, so a fleet of clients does not synchronise into waves.
Budget by ratio, not by count. Allow retries as a fraction of recent successes so the allowance collapses on its own when most calls are failing. The mechanics are in the Together AI retry budget guide.
Trip a breaker on sustained 500s. Repeated identical failures are a signal to stop calling and fail fast, not to keep paying. See the Together AI circuit breaker guide.
Never retry a non-idempotent call without a key. Everything above assumes the first attempt had no effect. Where it might have, that assumption is the bug.

What your product does while it lasts

Retries buy seconds. A failure that runs for twenty minutes needs a product answer, and there are only three honest ones. Decide which applies to each call site before an incident, because these are product decisions and nobody makes good ones at 2am.

Fail over

Route to a second provider behind the same interface. Best for calls where any competent model will do. Needs the fallback kept warm and tested, not merely configured. See the Together AI failover guide.

Degrade visibly

Serve a cached or simpler result and say so. Users forgive a labelled downgrade; they do not forgive a spinner that never resolves.

Queue and defer

For work nobody is watching, accept it, persist it, and drain when the service recovers. Turns an outage into latency instead of an error — but only with an idempotent submission path.

What none of the three tolerate is finding out late. The gap between the first 500 and the moment someone notices is where the damage happens, and it is the one part of this that is entirely within your control.

Frequently Asked Questions

What does a 500 from the Together AI API actually mean?

It means the request was accepted and something went wrong while it was being handled. That is a materially different statement from the other 5xx codes, and the difference is where all the operational consequences live. A 503 says the work never started because there was no capacity for it. A 500 says the work started and then broke — which is why a 500 is the only 5xx you cannot reflexively retry without first asking whether the first attempt might have had an effect.

Is it safe to retry a Together AI 500?

For a plain completion, yes — retry once or twice with jitter, and accept that you may pay for tokens generated on the attempt that failed. For anything that creates durable state — a batch job submission, a fine-tuning run, a file upload, anything your own system records — no, not without an idempotency key or a ledger you check first. The failure happened after the request was accepted, so it may have half-completed, and a naive retry turns one job into two. Streaming makes this worse, not better: a stream that emitted tokens and then died has already been billed for them.

How is a 500 different from 502, 503 and 504?

They point at four different parts of the path. A 500 is the service itself failing mid-handling. A 502 is a gateway receiving something invalid from upstream, which is usually brief and self-clearing. A 503 is a refusal for lack of capacity, where nothing was attempted. A 504 is a timeout at the gateway, and it is the most dangerous of the four to retry because the upstream work may still be running. Treating all 5xx identically is the most common mistake here, and it is exactly how duplicate jobs get created.

How do I tell a Together AI 500 from a failure in my own stack?

Take your application out of the path. Run one curl against the exact endpoint your code calls, from a machine outside your deployment, with a valid key. A 500 there is Together AI. A 200 there while your application still fails means the 500 is being generated between your app and api.together.xyz — your proxy, your service mesh, your API gateway or your own framework — all of which emit 500 in their own voice with an identical status code and a different body. Log the response headers next to the status; the provider stamps identifying headers that your gateway does not.

Together AI’s status page is green but I am getting 500s. Which is lying?

Neither. Together AI serves hundreds of model ids across shared serverless capacity and dedicated endpoints, and platform status reflects the control plane and the aggregate — not the specific model your request named. A single unhealthy serving path produces 100% failures for you and a rounding error for the platform. Retry the identical call against a different model id on the same key: a 200 there confirms the scope is one model, which is a fallback problem rather than an outage.

Do dedicated endpoints return 500 for different reasons than serverless?

Yes, and confusing the two wastes an incident. Serverless 500s track load and tend to be partial — some requests through, some failing. A dedicated endpoint 500 is usually about the endpoint’s own lifecycle: scaled to zero and waking, mid-restart after a config change, or failed to start at all. That failure is total for that endpoint and completely invisible to every other tenant, so no shared signal will ever reflect it. Look at the endpoint’s state in the console first; if it is not running, retries cannot help.

How should I report a persistent Together AI 500?

With the request id, which is the only part of your report that lets anyone find the event on their side. Capture the x-request-id response header on every failure and put it in the ticket alongside the exact UTC timestamp, the model id, the endpoint path and a minimal payload that reproduces it. A report that says "we are seeing 500s" cannot be actioned. One that names a request id and a five-minute window can be traced. Capture the header in your client now — during an incident is the wrong time to discover you never logged it.

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Together AI API 500 Internal Server Error