Fireworks AI
Usage BasedFrom the creators of PyTorch.
Published 2 October 2026
Scores
Popularity3/5
More than $1B in annualized revenue and customers like Cursor and Notion put it at the front of the open-model inference market, yet it is a back-end vendor whose name rarely reaches developers outside AI engineering.
Learning Curve1/5
Signing up, buying credits, and pointing an OpenAI client at the Fireworks base URL is all it takes to call a model; dedicated deployments and fine-tuning add a few concepts on top.
Flexibility4/5
Serverless, batch, per-minute GPU deployments, reserved throughput, and three kinds of fine-tuning cover most production needs, though the serverless catalog is narrower than the largest aggregators.
Performance5/5
The FireAttention engine posts some of the highest output speeds measured for large open models on GPUs, which is why latency-bound coding and search products build on it.
Portability4/5
Every model it serves is open-weight and the API is OpenAI-compatible, so workloads can move to Together AI or self-hosted vLLM, while its speed optimizations stay on Fireworks infrastructure.
About Fireworks AI
Fireworks AI is a hosted inference platform for open-weight models, founded in 2022 by Lin Qiao, who previously led PyTorch at Meta, and six co-founders, most of them from the same team. It serves models such as DeepSeek, Kimi, Qwen, Llama, GLM, and gpt-oss, plus image, audio, and embedding models, through an OpenAI-compatible API, so applications built for OpenAI's client libraries can switch with a new base URL and key.
The company's focus is serving speed in production. Its FireAttention engine uses custom CUDA kernels, speculative decoding, and quantization tuned per model, and it regularly posts among the highest output speeds measured for large open models. That has made it a common back end for products where latency matters: coding assistants, search, and agent workloads, with customers that include Cursor, Notion, Sourcegraph, and Uber.
There are three ways to run a model. Serverless endpoints bill per token for popular models, with Priority and Fast modes for latency-sensitive traffic and batch jobs at half price. On-demand deployments rent dedicated GPUs, from H100 to GB300, by the second and can host custom or fine-tuned weights, and reserved throughput fixes capacity for steady production traffic. Fine-tuning covers LoRA and full-parameter supervised training plus reinforcement fine-tuning, with the result deployable on the same platform.
Billing draws from prepaid credits, and spending tiers raise rate limits as an account grows. Fireworks raised its Series D in July 2026 at a $17.5B valuation, after annualized revenue passed one billion dollars.
Key Features
- Serverless per-token APIs for popular open-weight text, vision, image, and audio models
- OpenAI-compatible API with Python and JavaScript SDKs
- FireAttention serving engine with custom kernels and speculative decoding
- On-demand GPU deployments from H100 to GB300, billed per second
- Reserved throughput for steady production traffic
- Batch inference at half the serverless rate
- LoRA, full-parameter, and reinforcement fine-tuning
- Deploys custom and fine-tuned weights on dedicated GPUs
Pros
- Consistently among the fastest providers for large open models in independent speed benchmarks
- Production track record with latency-sensitive customers such as Cursor and Sourcegraph
- Fine-tuned and custom models deploy on the same platform as the stock catalog
- OpenAI-compatible API keeps the switching cost from other providers low
Cons
- Only a $1 starter credit, so there is no real free tier for experimentation
- Pricing is spread across serverless, on-demand, and training pages, which makes costs hard to estimate
- Dedicated GPUs cost about twice Together AI's H100 cluster rate and well above GPU-only clouds
- Smaller serverless catalog than Together AI, so niche models need a dedicated deployment
Fireworks AI Pricing
Usage Based- · Per H100 or H200 GPU-hour, billed by the second
- · B200 at $13, B300 at $15, and GB300 at $20 per hour
- · Per-token billing from prepaid credits, rate set per model
- · From $0.10 per million tokens for models under 4B parameters
- · Priority and Fast modes at higher per-token rates
- · $1 in starter credits for new accounts
- · 50% of the serverless rate on input and output tokens
- · For asynchronous jobs
- · From $0.50 per million training tokens for LoRA on models up to 16B parameters
- · LoRA and full-parameter supervised and preference training, plus reinforcement fine-tuning
- · Fine-tuned models serve at base-model prices
- · Reserved throughput, custom rate limits, and dedicated support
- · Quoted by sales
Tools Related to Fireworks AI
Integrates with Fireworks AI(1)
LiteLLM ships a Fireworks AI provider under the fireworks_ai/ model prefix, which lets one gateway send traffic to Fireworks AI's low-latency endpoints and fall back to other providers.
Alternatives to Fireworks AI(3)
Together AI and Fireworks AI both serve open-weight models through OpenAI-compatible APIs with fine-tuning and dedicated GPUs. Together AI has the broader catalog and rents raw GPU clusters; Fireworks AI focuses on serving speed for latency-sensitive products. Pick Together AI for breadth and training, Fireworks AI for the fastest responses.
Fireworks AI serves open-weight models on GPUs with a custom engine and adds fine-tuning and dedicated deployments for custom weights; Groq runs a smaller catalog on its own LPU chips. Pick Fireworks AI to tune and host your own model, Groq for the lowest latency on stock models.
Fireworks AI is a hosted inference platform tuned for low latency, with fine-tuning and dedicated GPUs; Hugging Face is the hub where open models are published, with its own inference endpoints. Pick Fireworks AI for production speed, Hugging Face for discovery and model breadth.