Fireworks AI

Fireworks AI — Fast open-weight inference with strong structured-output support. Fireworks competes with Together on the same ground — serverless open-weight inference, dedicated deployments, fine-tuning — and differentiates on latency engineering and constrained decoding, with grammar and JSON-schema enforcement that is more reliable than most hosts. A reasonable default when your open-weight workload depends on output that must parse every time. Founded 2022. Venture-backed.

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. 16 of 27 tracked fields carry a value for Fireworks AI, and 3 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. Fireworks 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 Fireworks AI alternatives for the other rivals, each compared the same way.

Founded

2022

Funding

Venture-backed

Fields we track

16 of 27

Last verified

2026-01-15

Hard to separate from Together on paper; the practical split is that Fireworks invests more in constrained decoding and latency tuning. If your pipeline breaks when a response fails to parse, its grammar enforcement is the more reliable of the two. Benchmark both on your own workload — the difference is real but small.

Schema output

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

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

AU region

○No8th of 17

Inferredverified 2026-01-15

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

• Open-weight workloads where output must validate against a schema every time.
• Latency-sensitive serving without moving to custom silicon.
• Fine-tuned open models served on managed infrastructure.

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

• Effort control. Fireworks 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. 11 of the 27 fields we track for Fireworks AI are still blank: Context window, Max output, TTFT p50, Output tok/s, $/M input, $/M output, $/M cache read and Batch discount, and 3 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.