Ray

Ray lets you scale Python code from your laptop to a cluster by adding a decorator to your functions. No rewriting your code, no learning a new framework. It handles distributed computing, model training (Ray Train), hyperparameter tuning (Ray Tune), model serving (Ray Serve), and reinforcement learning (RLlib).

Fully free under Apache 2.0. The core engine and all libraries are open source with no feature gating. Anyscale (the company behind Ray) offers a managed platform, but self-hosting the full stack costs $0.

The catch: Ray's "just add a decorator" marketing undersells the complexity. Distributed computing is hard, and Ray doesn't eliminate that; it manages it. Debugging distributed tasks, understanding memory management across workers, and tuning cluster resources requires real expertise. For single-machine ML, you don't need Ray. It earns its place when you need to scale beyond one box or orchestrate complex ML pipelines.