Together AI, Fireworks AI & Replicate: The Open-Model Inference Clouds
Open-weight models are free to use but painful to serve. Three companies — Together AI, Fireworks AI, and Replicate — rent you a production kitchen for those recipes: a broad catalog with 1M-token contexts and GPU clusters, an enterprise low-latency runtime, and a Stripe-style anything-model API. Same weights, dramatically lower serving effort and often 2-10x lower prices.
01.The Problem: Free Recipe, No Kitchen
Open weights changed the map. A lab like Meta publishes a frontier-class model's full recipe, and legally you may use it.
Solved: "can I use this model?"
Still unsolved: "can I serve it at 3 a.m. without an ML infrastructure team?"
Because the recipe being free does not make the cooking free. Serving means: renting GPUs, running a batching engine, handling quantization, autoscaling, cold capacity, latency SLAs, incident pages. Teams that tried it themselves ended up hiring platform engineers just to keep a weight file warm.
So a new category appeared: companies that run the kitchens for you, serving anyone's open recipes as a clean API.
Together AI, Fireworks AI, and Replicate are the three that matter. They sit one layer above the model repositories (where the weights live — the Hugging Face topic covers that supply side) and one layer below your app (which just calls an API).
Keep one picture through this topic: three commercial-kitchen rentals, all licensed to cook the same public recipes. Different equipment, different pricing models, same lasagna (the weights are identical everywhere).
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