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Trains and serves open and custom language models with per-token API pricing.
Fireworks provides infrastructure for training and running specialized language models: guided fine-tuning through custom reinforcement-learning loops, inference optimized for both open-source and proprietary models, and a library of pre-optimized models ready to call. It targets developers and enterprises building products like code assistants, conversational AI, agentic systems and multimodal applications, with customers named on the site including Cursor, Vercel, Notion and Sourcegraph. Access is through an OpenAI- and Anthropic-compatible API, with serverless pay-per-token pricing, on-demand dedicated deployments, and reserved capacity for guaranteed throughput. It was founded in 2022 by Lin Qiao, formerly head of PyTorch at Meta, along with several other ex-Meta engineers, and is based in the United States.
Serverless inference is billed per million tokens by model size: roughly $0.10/1M for models under 4B parameters up to $0.90/1M for models over 16B. New accounts get $1 in free credits; there is no ongoing free tier, so no single flat plan price applies.
Use tool ↗Fireworks is a credible, well-funded infrastructure player founded by the former head of Meta's PyTorch team, with real production customers like Cursor and Notion rather than just logo placement. Per-token pricing that scales with model size keeps small-model inference cheap, but there's no lasting free tier beyond a small starter credit, so this is squarely a pay-as-you-go developer tool rather than something to try casually.
Watch out: The $1 starter credit is enough for roughly 1 million tokens on a large model, which is easy to burn through quickly once testing moves beyond a few requests.
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