Neptune.ai
Free tierML experiment tracking and model registry for teams running production ML pipelines
Free tier available·Technical·API available
Key strengths
Comprehensive ML experiment tracking with rich metadata loggingScalable model registry for versioning and artifact managementFlexible Python SDK integrating with any ML frameworkTeam collaboration with shared dashboards and run comparisonsSupports large-scale hyperparameter search and reporting
Free tier + paid plans
Warsaw, Poland
Founded 2017
Self-hostable
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Technical Integration Guide
Neptune.ai is designed for seamless integration into ML pipelines with minimal overhead:
- SDK Installation:
pip install neptune— supports Python 3.7+. Async logging ensures negligible training overhead. - Initialization: Authenticate via
NEPTUNE_API_TOKENenvironment variable or pass directly. Useneptune.init_run(project="workspace/project")to create a new run object. - Metadata Logging: Neptune uses a flexible key-value metadata structure — log scalars, series (time-series metrics), files, images, HTML, DataFrames, and custom objects via the
run["namespace/key"]syntax. - Framework Integrations: Drop-in callbacks and integrations available for PyTorch Lightning, TensorFlow/Keras, HuggingFace Transformers, LightGBM, XGBoost, Optuna, Ray Tune, and more.
- Model Registry API: Programmatically create model versions, upload artifacts, and transition stage (
staging→production) vianeptune.init_model()andneptune.init_model_version(). - REST API & Webhooks: Full REST API available for querying runs, fetching metadata, and automating MLOps pipelines; supports integration into CI/CD workflows.
- On-Premises Deployment: Neptune can be deployed on your own infrastructure (Kubernetes-based), with SSO, role-based access control, and private networking support.