Fireworks AI — AI Glossary

Reference details

Related terms

• Together AI
• Groq
• OpenAI-Compatible API

How it works

1. Open-weight models are hosted behind an API shaped like the common provider convention, so client code ports with minimal change.
2. Serving is optimised at the kernel level, and techniques such as speculative decoding raise tokens per second without altering the model.
3. Grammar-constrained decoding restricts the sampler to tokens a schema permits, so structured output is enforced during generation rather than validated afterwards.
4. Capacity comes either serverless, billed per token with cold-start variance, or dedicated, reserved and billed for the reservation.
5. Because the models are open weights, the same checkpoint can be self-hosted later — which is what distinguishes this class from a proprietary provider.

Example: Why constrained decoding beats validate-and-retry

Asking for JSON and validating afterwards fails some share of the time; at a 5% invalid rate with one retry, roughly 1 in 400 requests still fails after both attempts, and the retries cost 5% extra tokens plus a doubled latency on those requests. Constraining the sampler to the schema makes invalid output unreachable rather than unlikely, so the failure rate is structural zero and there are no retries to pay for. The difference is where the constraint lives: in the decoder, or in a check after the fact.

Commonly confused with

Sits in the same category as Together, Replicate and Groq — hosted open-weight inference behind an OpenAI-compatible API — with serverless and dedicated deployment as the axis it emphasises. Serverless means paying per token with cold-start variance; dedicated means paying for reserved capacity with predictable latency. That choice, not the vendor, is usually what determines whether the deployment meets its latency target.

When to use it

Reach for it when:

• Serving open-weight models without operating accelerators.
• Structured output must be guaranteed rather than validated, where grammar constraints apply.
• Keeping the option to self-host the same weights later.

Reach for something else when:

• A frontier proprietary model is required, which is not open-weight.
• Data residency or private networking requirements that a hosted API cannot meet.
• Steady high volume where self-hosting is cheaper than per-token billing.

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