Baseten

Getting a trained machine learning model from a notebook into a reliable, scalable production API is a genuinely different skill set than building the model itself — infrastructure, autoscaling, latency optimization, none of which most ML researchers want to own directly. Baseten is built specifically for that deployment gap, letting teams serve custom and open-source models in production without managing the underlying infrastructure themselves.

Its positioning sits between fully managed foundation-model APIs (where you can't run your own custom model at all) and raw cloud infrastructure (where you own every operational detail yourself). For teams that have their own fine-tuned or open-source model and need it served reliably at scale — with autoscaling, cold-start optimization, and monitoring already handled — that middle position is exactly the gap worth filling.

It's developer infrastructure aimed at ML engineering teams, not an end-user product. Teams evaluating it are typically weighing it against self-managed Kubernetes-based serving or against other managed ML inference platforms — see our Coding tools directory for adjacent developer-focused AI infrastructure tools.

Custom model deployment

Deploys custom or fine-tuned machine learning models into a production-ready API.

Open-source model serving

Serves open-source models at scale without managing infrastructure directly.

ML inference autoscaling

Automatically scales model serving infrastructure based on real-time demand.

Can I deploy my own custom model on Baseten?

Yes, deploying custom and fine-tuned models is a core use case, unlike fully managed foundation-model-only APIs.

Does Baseten manage infrastructure for me?

Yes, autoscaling, cold-start optimization, and monitoring are handled by the platform.

Is Baseten for individual developers or teams?

It’s built primarily for ML engineering teams deploying models to production at real scale.

Does Baseten offer an API?

Yes, deployed models are served as production-ready APIs.