AI/ML Tools Tools

This category covers the layer beneath the models themselves: places to find and host weights, run inference, track experiments, and ship an ML feature without standing up your own GPU fleet.

The practical question is usually where the boundary sits between what you build and what you rent. Inference-as-a-service is the fastest path to a working feature and the easiest to reason about operationally. Managed training and fine-tuning platforms make sense once you have proprietary data worth adapting a model to. Self-hosting is a real option now that open weights are strong, but it's an infrastructure commitment, not a checkbox.

Model hubs matter more than they first appear: the availability of weights, licences, and a community porting models to usable formats often decides what's actually buildable, independent of which model benchmarks best.