Why Your Together AI API Stream Breaks

1. Cold starts show up as a long silent gap before the first chunk

Together AI serves a very wide catalogue of open models, and a less-popular model may need to be brought up before it can generate. From the client side that looks like a stream that opened and then went quiet — which is exactly what a naive idle timeout kills. Give the first chunk a longer deadline than subsequent chunks, or pin the models you care about to dedicated endpoints so warm-up is not on the request path.

2. Model identifiers are long and easy to get subtly wrong

A mistyped model string on Together AI fails the request before any stream exists, so your client reports "streaming stopped immediately" rather than a model error. Read the response status before assuming the transport broke: a 4xx never produced a stream in the first place.

3. Serverless platforms cap streamed responses

Most managed function runtimes enforce a maximum execution duration that applies to the whole streamed response, not to time-to-first-byte. A long Together AI answer can therefore be killed by your own host rather than by the API. Check the platform limit before you tune anything in the client.

1. Log the finish reason on every stream

One line of logging separates "the model stopped because it hit your token cap" from "the connection died". Without it, every truncation looks like a network fault and you debug the wrong layer.

2. Buffer partial lines in the parser

Chunks do not respect event boundaries — a single read can end mid-line. Accumulate into a buffer, split on the delimiter, and keep the trailing fragment for the next read. Hand-rolled parsers that skip this produce intermittent, unreproducible corruption.

3. Split your timeouts in two

A generous first-chunk deadline plus a tight inter-chunk idle deadline catches dead connections quickly without killing healthy long answers. A single total timeout cannot do both.

4. Turn off buffering along the whole path

Proxy buffering, gzip on the event stream, and framework response wrappers each independently defeat streaming. Fix them at every hop — one buffering layer anywhere is enough to make the API look broken.

5. Keep the partial answer on failure

When a stream dies mid-response you already hold most of the value. Render what arrived, mark it incomplete, and continue from there instead of throwing it away and restarting from an empty screen.

6. Fail over rather than retry

Re-sending the same request to an endpoint that just dropped you tends to drop you again, and the user watches the answer restart. Route the continuation to a second provider on the same OpenAI-shaped interface instead.

Why does my Together AI stream stop halfway through?

A stream that ends early without an error is almost never a dropped packet. In order of likelihood: your own max_tokens cap was reached (check the finish reason before anything else), an idle timeout on a proxy, load balancer or serverless platform closed a connection that had gone quiet, or the client library treated a partial chunk as the end of the response. A genuine upstream failure normally surfaces as a 5xx before the stream opens, or as a connection reset that your HTTP client raises as an exception. Log the data: [DONE] sentinel and the finish reason on every stream so you can tell truncation from disconnection.

Why does the Together AI stream arrive all at once instead of token by token?

That is buffering, not streaming, and it is happening between Together AI and your user rather than inside the API. The usual culprits are an nginx or CDN layer that buffers proxied responses, gzip compression applied to the event stream, or a serverless function that returns a whole response body instead of a streamed one. Send the response with no transform compression, disable proxy buffering on the route, and make sure every hop in the chain forwards chunks as they arrive. Testing with curl -N against the API directly proves whether the API side is streaming correctly.

Is a broken Together AI stream the same as Together AI being down?

No, and conflating them wastes hours. An outage fails the request before a stream exists — you get a 5xx, a connection refusal or a timeout on the initial call, and it affects every request. A streaming bug produces a request that starts successfully and then behaves wrongly: truncated output, buffered delivery, or a hang with no bytes. If a plain non-streaming call to the same endpoint succeeds, the API is up and the problem is in your streaming path. Check status.together.ai or live Together AI monitoring to rule out an incident first.

What timeout should I set on a Together AI stream?

Use two separate timeouts rather than one. A first-chunk deadline covers the time before generation begins and should be generous, because that window includes queueing and any retrieval or warm-up work. An idle timeout between chunks should be much tighter, because once tokens are flowing a long silence genuinely means something broke. A single total-request timeout is the worst of both: it kills healthy long answers and lets dead connections hang.

Can I retry a Together AI stream that failed mid-response?

You can, but not blindly. Re-sending the original request regenerates from scratch, so the user sees the answer restart and you pay for both calls. The better pattern is to buffer what you have already received, and on failure either finish the answer with a second provider or re-prompt with the partial text as context so the continuation is coherent. Reserve a clean full retry for streams that failed before the first content chunk, where nothing has been rendered yet.