Fireworks AI
Fast production inference for open models — serverless and dedicated GPU deployments, fine-tuning, and an OpenAI-compatible API on a proprietary serving stack.
Fireworks AI is a production inference platform for open models. It serves 100+ models (text, vision, audio, image, embeddings) over an OpenAI-compatible API — as pay-per-token serverless or dedicated GPU deployments — and adds fine-tuning. Its proprietary serving stack targets low latency and high throughput for open-source architectures.
Fireworks AI is a production inference platform for open models. It hosts 100+ models — spanning text, vision, audio, image generation, and embeddings — and serves them over an OpenAI-compatible API (with Anthropic compatibility too), so existing application code can often switch by changing the base URL and model name.
The platform's differentiator is speed at production scale. Models run on Fireworks' proprietary serving stack, built specifically for open-source model architectures (its blog details the FireAttention kernels behind it), aimed at low first-token latency and high throughput rather than the generic performance of a self-hosted server. You can run inference as pay-per-token serverless (no GPU provisioning, good for prototyping and bursty traffic) or as dedicated GPU deployments with autoscaling for steady production load.
Beyond inference, Fireworks offers managed fine-tuning — supervised and reinforcement approaches — with fine-tuned models served back on the same inference stack. Production features include function calling, structured JSON output, and batch inference, which makes it a common building block for agentic and RAG systems.
Fireworks is a hosted, closed-source service. It is a direct alternative to other open-model inference providers like Together AI, Groq, Baseten, and Replicate; for choosing between hosting your own model and using an API like this, see self-host vs. API LLM.
Frequently asked questions
- What is Fireworks AI?
- Fireworks AI is a production inference platform for open-source models. It hosts 100+ models across text, vision, audio, image, and embeddings and serves them over an OpenAI-compatible (and Anthropic-compatible) API, either as pay-per-token serverless endpoints or as dedicated GPU deployments. It also offers managed fine-tuning. Its proprietary serving stack, including the FireAttention kernels described on its blog, is built for low latency and high throughput on open-model architectures.
- Is Fireworks AI free?
- Fireworks AI is freemium. New accounts get a small amount of free credits to start, after which it is pay-as-you-go: serverless is billed per token, dedicated deployments are billed per GPU-second/hour, and fine-tuning and batch inference are priced separately. Enterprise and reserved-capacity plans are available.
- How do I use Fireworks AI?
- Sign up, create an API key, and call the OpenAI-compatible endpoint — point an OpenAI SDK at the Fireworks base URL and set the model name, so most existing code works with minimal changes. Use serverless for prototyping and bursty traffic, switch to a dedicated deployment for steady production load, and fine-tune a base model when you need it specialized.
Filed under
inference · open-models · fine-tuning · serverless · gpu
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