Fireworks AI.
Fast inference for open models — built by the ex-Meta PyTorch leadership.
What it is.
Redwood City inference platform founded by the founder, the former engineering leader who shipped PyTorch at Meta. Fireworks ships its own inference runtime — custom kernels, speculative decoding, quantisation — to beat raw vLLM throughput on the same hardware. Backed by Sequoia and Benchmark with over $100M raised by mid-2025.
Where it fits.
Engineering teams where inference latency directly affects product UX. Agentic workflows that loop through dozens of LLM calls per user action benefit most. FireFunction-V2 is a quiet hit with builders who need OpenAI-grade function-calling quality on open weights. Multi-region deployments handle EU and APAC traffic without round-tripping to US-East.
- Best-in-class throughput per dollar on open-weights serving
- FireFunction models close the function-calling gap with GPT-5
- Production tooling for canary deploys and traffic shifting
- Frontier-closed models not available — open-weights only
- Steeper learning curve than Together for first-time buyers
Frequently asked.
How does Fireworks compare to Together AI?
Fireworks pushes harder on inference engineering — custom runtime, quantisation, speculative decoding. Together pushes harder on model breadth and fine-tuning simplicity. Most buyers run both and route by workload.
What is FireFunction?
Fireworks' specialised function-calling model series. FireFunction-V2 matches GPT-5 on tool-use benchmarks at a fraction of the per-call price.
Does Fireworks support on-prem deployment?
Yes on Enterprise. The inference runtime can be licensed to run inside customer VPCs or air-gapped environments for regulated buyers.
