Fireworks AI

Fireworks AI serves Llama, Mixtral, Qwen, and DeepSeek at low latency through an OpenAI-compatible API. The right pick when you've decided to run open-source models in production and want one less thing to operate.

Fireworks' technical edge is the FireAttention inference engine, which delivers measurably faster throughput on Llama and Mixtral models than vanilla vLLM. For production apps, that translates into lower per-token cost or higher concurrency at the same cost — meaningful at scale. The OpenAI-compatible API means migrating from a frontier model is a base_url change, not a code rewrite.

The head-to-head versus Together.ai is essentially a coin flip for most workloads. Both serve similar models at similar prices with similar latency. Fireworks tends to win on raw inference speed for popular models; Together tends to have a slightly broader catalog of fine-tunes and a stronger LoRA hosting story. The right call is to benchmark on your specific model and workload — both companies will give you trial credits.

Buy Fireworks for production inference on open-source models, especially Llama 3.1 70B-class workloads where their performance edge matters. Pair with frontier APIs for the few highest-stakes calls in the same product. Skip if you only consume frontier APIs (no value here) or if Groq's LPU latency advantage is critical for your use case (specific scenarios).