Fireworks AI

Fireworks and Together are often mentioned in the same breath, but they optimize for different things. Together wins on catalog breadth; Fireworks wins on the serving layer. FireAttention, speculative decoding and quantization-aware serving are the kind of infrastructure work you only get from a team that came out of PyTorch's core, and in latency-sensitive workloads the difference is measurable, not theoretical. The dual OpenAI-and-Anthropic compatibility is a quietly brilliant move — teams can reroute either vendor's traffic to open models by changing a URL. The hesitations are practical: the $1 trial credit makes serious evaluation a paid exercise, the curated catalog means your favorite niche model may be absent, and everything about the product assumes an engineer at the keyboard. The honest guidance: benchmark it. If your product lives or dies on time-to-first-token — a voice agent, a real-time copilot, a high-traffic chatbot — Fireworks frequently comes out on top, and per-second GPU billing sweetens scaling. If you just need the widest choice of models at prototype prices, Together remains the broader default.