Fireworks AI

Fireworks AI has built one of the more technically credible inference platforms in the space, unsurprising given a founding team that came out of Meta's PyTorch group, and the claimed throughput of 30 trillion tokens per day suggests genuine production scale rather than a demo-stage service. Its core pitch, frontier performance at open model economics, holds up well: a library of 30+ open and proprietary models including DeepSeek, Qwen3, Kimi K2, and FLUX.1 gives developers meaningful choice without being locked to a single vendor, and OpenAI/Anthropic-compatible APIs make migration from those providers largely a config change rather than a rewrite. The fine-tuning stack is a real differentiator, supporting SFT, DPO, LoRA, full-parameter tuning, and multi-LoRA serving that lets many fine-tuned adapters share one base model cheaply, which is a meaningfully more cost-efficient architecture than dedicating separate infrastructure per fine-tune. Transparent, published pricing for both training tokens and hourly GPU rates (H100, B200) is a welcome contrast to opaque enterprise sales models common elsewhere in the category. The only real friction is that getting full value from the platform, especially fine-tuning and dedicated deployments, assumes some ML infrastructure fluency; casual builders may find the surface area more than they need.