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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- Hyperparameter optimization logging — Integrate with Optuna, Ray Tune, or custom search loops to automatically log every trial's parameters, scores, and intermediate metrics.
- Distributed training monitoring — Log metrics from multi-GPU or multi-node training jobs by initializing separate Neptune runs per worker or aggregating via a master run.
- CI/CD model validation — Use Neptune's REST API in GitHub Actions or Jenkins pipelines to fetch the best model version's metrics and gate deployment automatically.
- LLM fine-tuning tracking — Log training loss curves, evaluation benchmarks, prompt/response samples, and tokenizer configs when fine-tuning large language models with HuggingFace Transformers.
- Model artifact versioning — Store model weights, ONNX exports, feature importance plots, and confusion matrices as versioned artifacts attached to each run.
- MLOps pipeline automation — Programmatically query runs via the Python SDK or REST API to build automated retraining triggers, leaderboard reports, and audit logs.