Fireworks AI

Fireworks AI, founded by creators of PyTorch, is an AI infrastructure platform for training and serving large language models — serverless per-token inference, dedicated on-demand deployments, and reserved capacity, with access to open and proprietary models. Who it's for: companies and developers who need production LLM inference or training infrastructure without building and maintaining their own GPU serving stack. Strengths: being founded by PyTorch's own creators lends real, checkable technical credibility, and offering serverless, on-demand, and reserved-capacity tiers together means teams can match infrastructure commitment to actual usage patterns rather than over-provisioning. Nexus, a query router aimed at cutting AI coding spend, is a concrete, specific cost-optimization feature rather than a vague claim. Weaknesses and limitations: pricing is real and published per-token (e.g., $0.07–$15 per million tokens depending on model), but understanding true cost requires estimating actual token volume across whichever specific models are used. Real-world scenario: a company needs to serve an open-source LLM in production without building and maintaining their own GPU inference infrastructure, paying per token rather than provisioning fixed capacity upfront.