Together AI API Errors in the UI: What to Show the User When the Call Fails (2026)

Almost everything written about handling a failed Together AI call is addressed to the system: how many times to retry, how long to wait, when to stop calling, where to send the traffic instead. All of it is graded on whether a response eventually arrives. None of it covers the case that decides whether you keep the customer — the moment the machinery has done everything it can, and a human being is looking at whatever string you decided to put on the screen. That string is the last thing standing between a recoverable incident and a churned account, and in most products it was written in ninety seconds and has not been read since.

30-second triage: users reporting errors right now? Check live Together AI status before touching your code — if the provider is degraded, the fix this hour is the message you show, not the integration. Everything below is how to write that message so it does not cost you the account.

Six failures, six different things the user needs to hear

A single generic error string is a decision to answer all six of these the same way, and they have nothing in common except the HTTP layer. What separates them is not the cause — it is what the person can do next, which is the only part of an error message that has any value.

Failure class	What the user needs to know	Action to offer
Provider outage or 5xx	Not their fault, not their input, temporary	Retry later; notify when it works; keep the draft
Rate limit or capacity	It will work shortly; how long	A visible countdown, control disabled until it ends
Timeout on a long request	Whether it might still complete	Retry with a smaller scope, or wait and refresh
Stream cut mid-answer	The visible text is incomplete	Continue, regenerate, or keep as-is — marked
Refused or filtered content	The input, not the service, is the reason	Edit and resubmit; never a bare retry
Your own quota or billing	This is an account state, not a fault	Upgrade, wait for reset, or contact the workspace owner

The fifth row is the one most often merged into the generic case, and it is the most expensive mistake in the table: a refusal shown as “temporary error, please try again” sends the user into a loop that cannot succeed, burns spend on every attempt, and ends in a support ticket that would never have existed if the first message had said which part of the input was the problem.

Four things that must never reach the screen

The instinct when an integration fails is to surface more detail. Almost all of the available detail is either unusable by the reader or actively harmful, and one item on this list is the only exception.

The raw provider error string. It is written for a developer reading a log, it changes without notice, and it frequently embeds internal identifiers, model names and occasionally a fragment of the request. Map it to a class in your own vocabulary and show the class.
Any part of the prompt or context. Echoing the request back into an error surface is how retrieved documents belonging to another tenant end up rendered in someone's browser. See the Together AI multi-tenant guide for the other channels that leak the same way.
Stack traces and status codes. A person who can act on 503 already knows the service is down. For everyone else it is noise that makes the product look unfinished.
Nothing at all. A spinner that stops, an empty panel or a button that silently does not respond is worse than any message, because the user cannot even tell that a failure occurred. The one detail worth exposing is a correlation id in small text — useless to them, decisive for your support queue.

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One mapper, one vocabulary, one place to change the copy

The failure that produces inconsistent error copy is architectural, not editorial: the message is written at the call site, so every feature that touches Together AI invents its own wording, and no one can audit what users are being told. Collapse every transport and provider error into a small enum first, and let the interface own the words.

# Every Together AI failure lands in exactly one of these classes.
#   UNAVAILABLE   provider 5xx, connect error, breaker open
#   BUSY          429 / capacity      -> carries retry_after
#   TOO_SLOW      read timeout, deadline exceeded
#   TRUNCATED     stream ended early, length finish_reason
#   REJECTED      input refused or filtered  -> NOT retriable
#   ACCOUNT       your quota, billing, plan limit
#
# Each class maps to one copy id, one action, one retriable flag.
# The three fields teams forget to attach:
#   correlation_id  -> printed on screen, joins to your logs
#   retry_after_s   -> the countdown, not a guess
#   degraded_from   -> which rung answered, if any did
#
# Rule: the call site returns a class. It never returns a string.
# If a provider error you have not seen arrives, it is UNAVAILABLE
# and it pages you -- an unknown error must never fall through to
# the raw message, which is how vendor text reaches production UI.

Two properties fall out of this shape for free. Copy becomes reviewable, because every string a user can see lives in one file. And adding a provider or a fallback rung stops being a copy migration, because nothing downstream of the mapper knows which service answered — only which class of thing happened.

Streaming: the answer that stopped halfway

A truncated stream is the only failure in this list the user watched happen. They saw text arrive and then stop, which means every option that involves pretending it did not is off the table — clearing the panel reads as a bug and destroys work they may have been reading. Keep the partial text, mark it visibly as incomplete, and offer continue or regenerate.

The part that gets skipped is downstream of the screen. A partial response has to be recorded as incomplete in your data model, or it silently becomes a first-class artefact: copied, exported, counted as a completed unit for billing, added to an evaluation set, and — worst of the five — appended to the conversation history sent back on the next turn, where it produces a second bad answer with no error attached to it at all. The mechanics of detecting the cut are covered in the Together AI streaming errors guide; this is what to do with the fragment once you have it.

A per-model incident often shows up as streams that start normally and cut consistently at the same point — a pattern users read as your product being flaky. Naming the affected capability in the incomplete marker turns a mysterious defect into a comprehensible one.

The Together AI trap: incidents are per-model, so “we are down” is the wrong sentence

Together AI serves a large catalogue of models on shared infrastructure, and degradation is usually scoped to a model or a class of endpoint rather than to the platform. That makes the most common piece of AI error copy in existence — some variation of “the service is unavailable” — factually wrong most of the time: the service is fine, and one model is not. The message that matches reality is that the specific capability behind this feature is unavailable, followed by an offer to continue on the alternative you already configured. That offer creates a second obligation, and it is the one teams skip: if the answer is produced by a different model, the user has to be able to see that, because they are the only person who can judge whether a substitute is acceptable for what they are doing. Render the model or mode on the result and store it on the record.

Six screens where the copy is the whole bug

In every row below the integration behaved exactly as designed. The defect is entirely in what the user was told, and each one has a measurable cost attached.

What the user sees	What actually happened	What it costs
“Something went wrong. Try again.”	Input was refused — retrying can never work	A retry loop, spend on every attempt, a ticket
Spinner that never resolves	Request failed; no error path in the component	The user assumes the product is broken and leaves
Empty results list	Search failed — not a genuine zero-result query	“Your product has no data on this”
A normal-looking answer	Fallback rung answered; quality quietly lower	Trust in an answer nobody flagged as degraded
“Together AI is currently unavailable”	Your own quota or billing limit was reached	A support thread with the wrong company
Answer disappears mid-sentence	Stream cut; the component cleared its buffer	Lost work, and a bug report you cannot reproduce

Five of these six are invisible to every dashboard you own. The error rate is flat in four of them, and in the third and fourth rows the request succeeded outright. The only instrument that finds them is someone walking the failure paths on purpose — which is why they should be in your test suite as rendered states, not left to be discovered by a customer. The Together AI API testing guide covers simulating each failure class against your own code.

Degradation is an annotation, not an error

When a fallback rung answers, a cache serves a stale result or retrieval is switched off to keep the feature alive, nothing failed — the user got a real answer of lower confidence. Interrupting them with an error is wrong, and saying nothing at all is worse, because the answer looks identical to a healthy one and will be trusted as such. The correct treatment is a small, permanent, non-modal marker on the result itself: which mode or model produced it, how many sources it used, how old the cached copy is. It costs nothing when everything is healthy, and it is the difference between a user who can calibrate and a user who was quietly misled. Which rung answered is decided in the Together AI fallback ranking guide; this page is about admitting it on screen.

Two rules keep it honest. The marker is attached to the answer, not to a session-level banner that users dismiss and never see again. And the same field that renders the marker — the rung id or mode — is stored on the record, so that six weeks later you can answer the question of how many answers in your product were produced by the degraded path. Teams that render the marker without storing the field can never audit their own quality.

Frequently Asked Questions

What should a user actually be told when the Together AI API fails?

Three things, in this order: what state their work is in, whether it is worth waiting, and what they can do now. Everything else is decoration. "Something went wrong" fails all three, which is why it generates a support ticket almost every time it is shown — the user cannot tell whether their input was lost, whether pressing the button again will help, or whether the problem is theirs. A message that says the request did not complete, that their text is still in the box, and that retrying in a moment is likely to work answers the questions a person is actually asking. Note that none of that requires naming Together AI or exposing a status code.

Should the error message name Together AI as the cause?

Usually not, and the reason is not diplomatic. From the user's point of view your product failed, and naming a vendor they have no relationship with adds a fact they cannot act on while implying the outage is someone else's problem to fix. It also ages badly: the day you add a second provider or a fallback rung, every screen that hard-codes a vendor name is wrong, and you will not find them all. Keep the provider identity in your logs, your status page and your support tooling where it is genuinely useful, and keep the user-facing copy about the capability that is unavailable rather than the company behind it.

When the stream dies halfway through, do we keep the partial answer on screen?

Keep it, and mark it. Discarding text the user already watched appear feels safe and reads as a bug — they saw it, they may have been reading it, and it vanishing is a worse experience than an obviously truncated answer. The real obligation is downstream: a partial response must be flagged as incomplete in your data model so it is excluded from copies, exports, evaluation sets, conversation history sent back to the model, and anything billed as a completed unit of work. Teams that get the visual part right and skip the data-model part end up with truncated answers silently becoming context for the next turn, which produces a second bad answer with no error attached to it.

Is a retry button safe to show?

Only if pressing it cannot quietly cost the user or you twice. Give the action an idempotency key derived from the original request so a duplicate submission is recognised rather than re-billed, disable the control while a retry is genuinely in flight, and show the wait when the failure was a rate limit — an enabled button during a backoff window invites a person to generate exactly the load that is causing the failure. Where the operation has already consumed credits or a quota unit, say so before the retry rather than after it. For anything expensive or long-running, an explicit "try again" the user chooses is safer than an automatic retry they cannot see.

How do we handle a degraded answer that returned successfully?

Show the degradation as a property of the answer rather than as an error, because it is not one. A response produced by a fallback model, with fewer sources, from a stale cache or with retrieval switched off is a real answer of lower confidence, and the user is the only one who can decide whether that is good enough for what they are doing. A small, permanent, non-modal indicator on the result — the model or mode used, the number of sources, the cache age — costs nothing when everything is healthy and is the only thing standing between a quietly worse answer and a user who trusted it. Errors interrupt; degradation should annotate.

What should support see that the user does not?

A correlation id, and it should be on the screen. Generate one identifier per user-visible operation, log it alongside the provider request id, attempt number, model, feature and timestamp, and render it in small text under the error. That single string turns "it broke this morning" into an exact row, and it is the one piece of internal detail worth exposing, precisely because it is meaningless to anyone who is not you. Raw provider error strings, stack traces, HTTP status codes and any part of the prompt are the opposite: they leak implementation detail, occasionally leak another user's context, and tell the person reading them nothing they can use.

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Together AI API Errors in the User Interface