Baseten

Baseten gives you autoscaling GPU inference for custom or fine-tuned models without managing the underlying infrastructure. The right pick for ML teams shipping their own models to production.

Baseten's thesis is correct: most ML teams shouldn't be operating Kubernetes clusters with GPU autoscaling. Truss (their open-source packaging format) lets you wrap any model — fine-tuned Llama, custom transformer, ComfyUI workflow — in a standard interface, then deploy it to autoscaling GPU infrastructure. Cold-start optimization for large models (which can otherwise take 60+ seconds) is a meaningful product investment that's hard to replicate.

The distinction from Fireworks/Together matters: Fireworks and Together are great for serving popular open-source models at fixed prices. Baseten is for serving custom or fine-tuned models — your specific model that no one else is hosting. The use case is narrower but underserved; many ML teams need exactly this and end up operating GPU infrastructure themselves at meaningful cost.

Buy Baseten if your team trains, fine-tunes, or otherwise produces custom models that need production serving. The pricing (per-GPU-second) is fair for autoscaling workloads with variable traffic. Stay with Fireworks/Together if you only serve popular OSS models. Skip if you're a frontier-API-only shop.