Together AI alternatives

Why people look for an alternative to Together AI

• On per-token inference their rates sit above DeepInfra on most models, which is the comparison a high-volume text workload eventually runs.
• Video model support is limited, so a stack that generates video is already paying a second vendor.
• Time to availability for a newly released open-weight model varies by model, which matters if adopting new weights quickly is part of how you compete.

Every figure attributed to a provider on this page is that provider’s own published information as of September 2026. Where a provider publishes no number, this page says so rather than estimating one. If something here is out of date, tell us and we’ll correct it — including in a competitor’s favour.

The alternatives, in order of fit

Ordered against the reasons above rather than by preference. Each one carries what it costs you, because an option listed without a trade-off is a recommendation wearing a survey’s clothes.

1. 1

   DeepInfra

   Best for: The most common reason for leaving: per-token cost on text. DeepInfra runs its own GPU fleet at real scale and is frequently the lowest published rate on open text models, with a broader open-model catalogue than ours.

   Trade-off: They publish their own rate only, with no official rate alongside it, so the markup question is one you answer yourself. Video is limited, spending controls are account-level rather than per key, and the time to add a new model varies — so this move fixes cost, not the other two reasons.

   AI Token Router vs DeepInfra, side by side

2. 2

   AI Token Router

   That’s us

   Best for: Leaving over video or over new-model latency. Video generation runs on the same key and the same balance, we add newly released models within 24 hours of the weights dropping, and every rate is printed next to the model's official rate so the saving is stated rather than derived.

   Trade-off: We do not fine-tune and we do not sell dedicated GPU capacity, so if you use either half of Together we replace only part of your stack. Seven of 24 catalogued models are callable today, we are a newer platform with a shorter reference list, and video is billed at a fixed five-second duration during rollout.

3. 3

   Fireworks AI

   Best for: Keeping the training half. Fireworks has a serverless training API on open weights priced per token, a materially larger open-model catalogue, Standard / Priority / Fast tiers so you pick your own latency-cost point, and distribution through Microsoft Foundry / Azure if you are already there.

   Trade-off: Their commercial pricing page carries no serverless rates — you click through to the docs and read across three tiers. Billing is postpaid, so usage accrues and is invoiced after the fact, and the serverless catalogue covers text and vision only: no image, audio or video endpoints.

   AI Token Router vs Fireworks AI, side by side

4. 4

   Nebius Token Factory

   Best for: Replacing Together's enterprise standing with something stronger. SOC 2 Type II, HIPAA, ISO 27001, a zero-retention data mode, a 99.9% uptime SLA, EU data residency in Finland and France, 60+ open models, and volume discounts that scale with monthly usage.

   Trade-off: No public rate card — pricing sits behind a self-service portal and the headline number is not what you pay once discount tiers apply. Onboarding is enterprise-first, and there is no image or video generation at all: text, vision and embeddings only.

   AI Token Router vs Nebius Token Factory, side by side

Where the rates land, model by model

Only the models we can serve today and Together AI carries as well. Our claim is that we price below the model publisher’s official rate — not that we are the cheapest anywhere — so where a provider comes in under us on a model, that row says so.

Rates exclude cached input, which is published per model on the pricing page and dominates any workload with a repeated prefix. This table covers our servable catalogue only — the other providers on this page carry models we do not, at rates we have not verified per model and therefore do not print.

What actually changes in your code

Inference-only migrations are small: change base_url, change the key, change the model id. The parts that do not travel are the platform features — fine-tuning jobs, hosted fine-tuned checkpoints and dedicated endpoint configuration have no counterpart on a plain serverless API, and a fine-tuned model you serve on Together cannot be pointed at another provider without moving the weights first. Check that before you plan the cutover, not after.

The step-by-step version lives in the migration guide.

Questions

Is there a cheaper alternative to Together AI?

On text, DeepInfra is frequently the lowest published rate on open models and sits below Together on most of them. We are 30–50% below each model's official rate on everything we carry, with the official rate printed alongside — check both against your own model mix rather than taking either claim on trust.

Which alternative also does fine-tuning?

Fireworks AI has a serverless training API on open weights, priced per token, and serves the result. Hyperbolic rents on-demand H100 / H200 / B200 capacity if you would rather train on raw GPUs. We do not fine-tune at all.

What replaces Together's dedicated GPU clusters?

Hyperbolic sells on-demand H100s, H200s and B200s with no quota limits and no long-term contract, plus a route through reserved clusters to private cloud. Note that their pricing and inference pages both return 404 and no per-token rate card is published anywhere on their domain, so budget a sales conversation.

Do I have to rewrite my code to move off Together AI?

Not if you move to another OpenAI-compatible endpoint — that is a base URL, a key and a model id. The exception on this list is Replicate, whose prediction API is its own shape, which means rewriting the call sites rather than repointing them.