Fireworks.ai

Fireworks.ai is a developer tool. Fireworks is a training and inference platform for open models, letting teams fine-tune or run reinforcement learning on their own data and then deploy checkpoints to OpenAI and Anthropic compatible endpoints. It combines a guided training path, config-led runs, custom RL code, serverless and dedicated inference, and a broad library of open weights models.

Fireworks is a training and inference platform for teams that want to build on open models rather than rent a closed API. The company, which traces its lineage to the creators of PyTorch, positions the product around a simple idea: take a leading open source model, fold in your own data through training or reinforcement learning, and end up with a model that is tuned to your workload and stays under your control. Every checkpoint produced by a training run can be pushed to production serving in seconds, so the loop between experimentation and a live endpoint stays short. The platform splits into two halves. On the training side there are three levels of involvement: a guided path where you describe the task, review a plan and cost estimate, approve the run and receive a trained model; a configuration-led path for teams that already know their model, data and method and just want scheduling and the production handoff handled; and a fully code-level path where you write your own loss function, trainer and RL loop on Fireworks GPUs with rollout serving and weight sync. On the inference side, serverless endpoints bill per token with Priority and Fast tiers and are compatible with the OpenAI and Anthropic APIs, while On-Demand and Reserved deployments add dedicated capacity, multi-region options and higher quotas. In practice this suits engineering teams building products on top of LLMs: coding assistants, agent platforms, search and RAG backends, and vertical applications where a tuned model beats a generic one. A model library covers popular open weights releases such as gpt-oss-20b, Kimi K3, DeepSeek-V4-Flash, GLM 5.3 and Minimax M3, callable with a single line of code. Fireworks Nexus, a newer offering, adds a routing layer that can send each request to the best open or closed model and is pitched as a drop-in replacement for closed-model APIs. Compared with raw GPU rental or a pure inference vendor, Fireworks bundles the training stack, the serving engine and the production handoff in one place. The tradeoff is that it assumes real engineering capacity: this is infrastructure for people who ship models, not a no-code playground.