Best alternatives to Fireworks AI in 2026

Running a large language model yourself means renting expensive graphics cards, keeping them busy enough to be worth the cost, and tuning a lot of infrastructure just to answer questions quickly. Fireworks AI removes that whole layer of work: you send a request to an API, an open-source model answers it on infrastructure Fireworks already built and optimized for speed, and you pay only for what you use. Fireworks AI is a cloud inference platform offering 40+ optimized open-source language models through OpenAI- and Anthropic-compatible APIs, so switching from a proprietary model provider is usually a small code change rather than a rewrite. It supports serverless per-token billing for pay-as-you-go usage, on-demand and reserved dedicated GPU deployments for predictable heavy workloads, and fine-tuning (supervised, preference-based and reinforcement learning methods, including cheaper LoRA-based tuning) for teams that need a model adapted to their own data. Pricing for serverless inference starts around $0.10 per million tokens for smaller models and scales up by model size, dedicated GPUs run roughly $7-$12/hour depending on the hardware tier (H100 through B300), and fine-tuning is priced per million training tokens starting near $0.50/1M for LoRA. The company raised a large Series D round in mid-2026, reflecting how central inference infrastructure has become to the AI stack.