Fireworks AI

Inference and fine-tuning platform for open-weight models

AI Models & PlatformsFree planOverseas★★★★☆ 4.0

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What is Fireworks AI?

Fireworks AI is an inference platform for open-weight models, aimed at teams that want OpenAI-style APIs without running their own GPUs. It serves text and image models, supports function calling and structured output, and offers fine-tuning with parameter-efficient or full jobs that deploy straight to an endpoint. Dedicated deployments give predictable latency when shared capacity is not enough, and OpenAI-compatible endpoints make migration mostly a matter of changing a base URL and key. Cost is per token or per GPU-hour.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

Hosted inference for popular open-weight text and image models
Fine-tuning jobs with LoRA or full parameter training
Function calling and structured output supported in the API
Dedicated deployments for predictable latency and throughput
OpenAI-compatible endpoints for near drop-in migration
Model library spanning small models up to mixture-of-experts

Pros & cons

Strengths

Open-weight models served without managing any GPUs
Fine-tuned models deploy to an endpoint in the same account
Compatible API keeps migration work small

Watch out for

Renting serving costs more than self-hosting at steady scale
Free credit is limited to evaluation and small tests
Model versions and rates change as the catalogue grows

Best for & use cases

open model hosting, fine-tuning deployment, function calling workloads and api migration

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FAQ

How is it different from a cloud GPU?

You rent serving rather than hardware: no containers to configure, no scaling rules to write, and billing is per token or GPU-hour. Self-hosting on your own instances is cheaper at high steady volume and considerably more work.

Can I fine-tune models there?

Yes, both parameter-efficient tuning and full fine-tuning on supported base models, with the result deployable to an endpoint in the same account. Data preparation and evaluation remain your responsibility.

Do I have to rewrite my code?

Usually not much. The API follows the OpenAI request format, so changing a base URL and key often works, though tool-calling and structured output support varies by model and should be tested per endpoint.