Together AI

Together AI — Serverless and dedicated hosting for open-weight models. Together runs open-weight models at scale — serverless endpoints for two hundred-plus models, dedicated GPU instances when you need isolation, plus fine-tuning. The value is a Western contract and a US region in front of models whose origin API you may not want to call, with a per-model price list rather than a single flagship rate. Founded 2022. Late-stage private.

We track it in 1 comparison — LLM APIs — so every claim below is a cell in a table you can open and check rather than an impression. Across those rosters it sits against 17 other tools, and what follows is where it visibly separates from them.

Where it gives ground.

• Effort control — No. Anthropic records Yes.

None of these disqualify it on their own. They are the fields to check against your own requirements before you commit, because they are the ones where a competitor genuinely does better.

Provenance. 17 of 27 tracked fields carry a value for Together AI, and 2 of those cite a document you can open. Last verified 2026-01-15. Every figure keeps its own provenance — measured by us, claimed by the vendor, inferred, or community-reported — and we would rather print a dash than a guess.

Its nearest neighbour in our data is Qwen. Together AI is ahead on Schema output (Yes against Partial) and Open weights (Yes against Partial). Qwen takes Trains on your data (Partial against No) and Effort control (Partial against No). That pattern repeats across the rest of the roster — see Together AI alternatives for the other rivals, each compared the same way.

Founded

2022

Funding

Late-stage private

Fields we track

17 of 27

Last verified

2026-01-15

The pragmatic way to use Chinese open-weight models without sending data to a Chinese endpoint: same weights, US infrastructure, a contract your legal team recognises. The dedicated-endpoint path is the real differentiator once you have steady load — predictable latency and no noisy neighbours.

Schema output

●Yes1st of 18

Inferredverified 2026-01-15

Pinnable versions

●Yes1st of 18

Inferredverified 2026-01-15

Trains on your data

○No7th of 18

Inferredverified 2026-01-15

Effort control

○No12th of 18

Inferredverified 2026-01-15

OpenAI-compat API

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

MCP support

○No6th of 18

Inferredverified 2026-01-15

Open weights

●Yes1st of 18

Inferredverified 2026-01-15

Batch discount

50 %1st of 10

Inferredverified 2026-01-15

Together AI is the right call in these situations, each one drawn from a field we actually record:

• Running DeepSeek or Kimi weights under a Western contract.
• Steady high-volume load that justifies a dedicated GPU endpoint.
• Fine-tuning open-weight models without owning hardware.

Reach for something else when any of the following is a requirement rather than a nice-to-have:

• Effort control. Together AI records No on the LLM APIs table. Anthropic records Yes on the same field. If that is a hard requirement rather than a preference, start elsewhere.

We publish this block because a comparison that only lists what a tool is good at is marketing. Every figure above sits on the same page as its source, and the field definitions are on the category tables if you want to check how we measured them.

What we don't know. 10 of the 27 fields we track for Together AI are still blank: Context window, Max output, TTFT p50, Output tok/s, $/M input, $/M output, $/M cache read and Cache TTL, and 2 more. Those render as dashes rather than as zeroes or assumptions, because an empty cell and a bad cell are not the same thing and only one of them is honest. If you know any of these figures and can point at a document, tell us.