mlop
MLOps platform with W&B-compatible API — experiment tracking, model registry, and collaboration for ML teams
Track and optimize machine learning experiments with precision
MLflow is itself an open-source project (Apache 2.0), but most teams run it through Databricks' managed hosting, where experiment tracking, the model registry, and storage are tied to Databricks' platform and billing. Teams looking to self-host something similar without adopting the full Databricks stack often look for lighter-weight, independently hosted alternatives.
mlop is worth a look if you want an MLOps platform with an API compatible with Weights & Biases, rather than MLflow's own tracking API.
FAQ
Is there a free, open-source alternative to managed MLflow?
Yes. mlop is Apache-2.0 licensed and self-hostable, giving you experiment tracking and a model registry without Databricks-managed hosting.
What's the best open-source alternative to MLflow?
mlop is the pick if your team already uses Weights & Biases-style logging calls and wants a compatible self-hosted backend instead of MLflow's own API.
What are the top open-source alternatives to MLflow?
Browse all open-source alternatives to MLflow above, or explore the full Developer Tools category for more options.