Together AI Free Tier and Credits: What Runs Free, and For How Long (2026)

Together AI hosts hundreds of models rather than a handful of its own, and that shapes its free offering in a way that catches people out. On a single-model provider, 'the free tier' is one set of limits. Here it is two unrelated mechanisms that developers routinely conflate: a starting credit balance you spend down across the whole catalog, and a subset of endpoints designated free-to-call with their own separate, much tighter limits.

Those two fail in completely different ways. Credits run out silently and every model stops at once. A free-designated endpoint rejects you while everything else on the same key keeps working perfectly — which looks nothing like a quota problem and everything like a broken model. Knowing which one you are on determines whether the fix is a card, a model swap, or waiting.

There is a third Together-specific wrinkle worth flagging up front: the catalog moves. Models are added, retired and re-tiered faster than at providers who train their own line, and a model that was free-to-call last quarter may not be this quarter. Free-tier code that hard-codes a model string is the most common source of 'it worked last month' on this platform.

Credits and Free Endpoints Are Not the Same Thing

Start by identifying which mechanism you are actually using, because every diagnosis downstream depends on it. If your account has a starting credit balance, you are paying — just not out of pocket yet. Calls are metered at the normal per-token rate for whichever of the hundreds of models you called, the balance decreases, and when it reaches zero everything stops at once, across every model, with no gradual degradation to warn you.

If instead you are calling an endpoint that Together designates as free to use, no balance is consumed, but you are on a distinct and considerably tighter set of rate limits, and typically on a shared capacity pool where your latency depends on everyone else's traffic. This is the mechanism that produces the confusing failure mode: one model rejecting while the rest of the catalog answers normally on the same key.

The practical instrumentation is to log which mechanism served every call, alongside the model string. Without that, a production incident becomes an archaeology exercise — and on a platform with this many models, 'which endpoint class was that request on?' is a question your logs should answer in one query rather than one afternoon.

Credits Drain at Wildly Different Rates

A fixed credit balance across a catalog with a hundred-fold spread in per-token price is not a fixed amount of usage. The same balance is weeks of evaluation on a small open model and an afternoon on a large one, and because the console shows a currency figure rather than a token figure, teams consistently misjudge the runway. The number to track is not credits remaining but credits-per-day at your current traffic mix, which is the only version that projects a date.

Two things accelerate the drain more than teams expect. Long contexts, because input tokens are tokens and a retrieval-augmented prompt can be an order of magnitude larger than the question that triggered it. And retries, because a failed call that returned tokens before failing has still consumed them — an aggressive retry policy against a flaky window can burn a meaningful share of a starting balance in minutes.

Dedicated endpoints are the other case where the token mental model stops applying entirely: held hardware is billed on time rather than on tokens, so an idle dedicated endpoint costs the same as a busy one. Spinning one up 'just to test' and forgetting it is the single most common way a starting balance disappears without a single interesting request being served.

Free-Designated Models Churn

The free subset of a hundreds-model catalog is a moving target by construction. New models arrive, older ones are retired, and the tier a given model sits in is a business decision that can change between quarters. Code that hard-codes a free model string is therefore code with an expiry date you do not control, and the failure it produces — a model-not-found or an unexpected charge — typically lands weeks after anyone last touched that file.

Two defences, both cheap. Resolve model identifiers from configuration rather than from a literal in the call site, so a swap is a config change rather than a deploy. And treat a model-not-found response as a first-class error path with a defined fallback, not as an exception that bubbles into a 500 — on this platform it is a routine operational event, not a bug.

This is also why evaluation results on this platform go stale faster than elsewhere. A benchmark you ran against a free endpoint six months ago may be describing a model that has since been retired, or one now served from a different capacity class with different latency. Re-run before you commit an architecture to it.

Free-Tier Rejection or Actual Outage?

The three failure modes here — exhausted credits, free-endpoint rate limiting, and a genuine incident — are separable in about a minute if you know what to look for, and are otherwise indistinguishable from a graph of rising errors.

Exhausted credits fail everywhere at once, immediately, with a billing-shaped error, and they do not recover on their own. Free-endpoint rate limiting fails on one model while others on the same key succeed, tracks your own traffic peaks, and clears when the window resets. A genuine incident on api.together.xyz is indifferent to your traffic, produces generic upstream errors and timeouts rather than anything account-specific, and affects models you have not touched.

The single fastest discriminator is that cross-model check: call a different model, ideally in a different class, on the same key. Two out of the three explanations are eliminated by the result. External monitoring of the endpoint settles the third, because it probes on a schedule that has nothing to do with your traffic and can tell you the platform was fine while you were spending an afternoon on your own client.

When to Stop Being Free

Free access on Together is best used to answer a question the platform is unusually good at answering: which of these many models is right for this workload? That is a genuine evaluation problem and the credit balance is well shaped for it. It is much less well shaped for running anything continuously, because the free-designated endpoints carry the tightest limits and a shared capacity pool.

Move to paid capacity once you have picked the model, and reconsider the endpoint class at the same time. On this platform the upgrade decision is not just 'start paying' — it is also serverless versus dedicated, which is a genuine architectural choice with a break-even point that depends on your utilisation, not on your token count. Sustained, predictable traffic tends toward dedicated; spiky or exploratory traffic almost never does.

Whichever you choose, keep independent monitoring on api.together.xyz. A platform aggregating this many models has more moving parts than a single-model provider, and 'is this model broken or is the platform down?' remains a live question long after the free tier is behind you.

Frequently Asked Questions

Is Together AI free to use?

Two different things get called free here and they behave differently. New accounts typically start with a credit balance, which means you are metered at normal per-token rates and simply have not paid out of pocket yet — when it hits zero, every model stops at once. Separately, some endpoints are designated free to call and consume no balance, but they carry considerably tighter rate limits and generally sit on shared capacity, so latency depends on other people's traffic. Knowing which mechanism served a given call is the first step in every diagnosis on this platform.

How long do Together AI free credits last?

There is no useful answer in days, because the catalog spans a very wide price range and the same balance is weeks of evaluation on a small model or an afternoon on a large one. Track credits-per-day at your current traffic mix rather than credits remaining — that is the only figure that projects a date. Watch two accelerants in particular: long retrieval-augmented prompts, since input tokens are billed tokens, and retry storms, since a call that returned tokens before failing has still consumed them. A forgotten dedicated endpoint is the fastest way of all, because held hardware bills on time whether or not it serves a request.

Why does one Together AI model fail while others work?

That pattern is the signature of free-endpoint rate limiting rather than an outage or exhausted credits. Free-designated endpoints have their own tighter limits and a shared capacity pool, so a single model can reject you while everything else on the same key answers normally. Exhausted credits would stop every model at once with a billing-shaped error; a platform incident would affect models you have not touched and would not track your traffic peaks. Calling a second model in a different class is the fastest discriminator available.

Do free Together AI models disappear?

Yes, and planning for it is part of using the platform. Together hosts hundreds of models it did not train, so the catalog turns over faster than at a single-model provider: models are added, retired and re-tiered, and which ones are free to call is a business decision that can change. Code with a hard-coded free model string therefore has an expiry date you do not control, and the failure usually lands weeks after anyone last touched that file. Resolve model identifiers from config, and treat model-not-found as a routine error path with a defined fallback.

What happens when Together AI credits run out?

Everything stops at once. Unlike a rate limit, which clears when the window resets, an exhausted balance is a hard stop across every model on the account, it arrives without gradual degradation, and it does not recover on its own. The error is billing-shaped rather than quota-shaped, which is the tell. Because there is no soft-landing, the only real defence is projecting the balance forward at your current burn rate and alerting on a threshold that gives you days of margin rather than hours.

Are Together AI free-tier limits the same as paid?

No, and this is the mistake that costs the most later. Free-designated endpoints carry their own considerably tighter limits and typically run on shared capacity, so both the throughput and the latency profile you observe on free access are unrepresentative of what you will see on paid serverless capacity, and unrepresentative again of a dedicated endpoint. Sizing an architecture from free-tier measurements produces an estimate that is wrong in both directions. Re-measure on the tier you intend to ship on before committing.

When should I move to a paid Together AI plan?

Once free access has answered the question it is good at answering — which of these many models fits this workload — it has done its job, because the free endpoints are the worst place on the platform to run anything continuously. At that point the upgrade is not just 'start paying': it is also a choice between serverless and dedicated capacity, and the break-even between them depends on utilisation rather than token count. Sustained predictable traffic tends toward dedicated; spiky or exploratory traffic almost never does.

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