Neptune / neptune.ai
ML experiment metadata store and tracking platform that logs training runs, compares model versions, and provides a collaborative workspace for machine learning teams.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
3
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
Neptune.ai is a machine learning metadata store and experiment tracking platform — competing with Weights & Biases for ML teams wanting to log training metrics, compare experiments, and collaborate on ML development without building custom logging infrastructure.
Experiment tracking logs all metadata from training runs — metrics (loss, accuracy, custom KPIs), hyperparameters, model files, datasets, environment variables, and Git commit hashes. Every training run is searchable, filterable, and comparable across team members.
Model comparison enables side-by-side analysis of multiple training runs — comparing metric curves, hyperparameter choices, and evaluation results to identify which configurations produce better models. Visual comparison reduces the time to extract insights from experimental results.
Collaboration features share experiments across teams — tagging runs with notes, creating reports from selected experiments, and maintaining a shared model registry. For distributed ML teams, Neptune provides a shared experiment workspace replacing personal tracking notebooks.
Integration breadth covers 50+ frameworks — PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, and LightGBM all log to Neptune with a few lines of code. Framework integrations reduce the setup overhead for adopting experiment tracking.
Neptune's free plan is more generous for individual users than W&B's — providing unlimited runs for individuals, making it accessible for researchers and solo practitioners.
Neptune.ai runs as ml platform software built around data workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on python, cli, and web, with API access for teams that want to embed it into their own products.
The capabilities that matter most for teams evaluating Neptune.ai.
Logs all training run metadata automatically — metrics, hyperparameters, model files, datasets, and environment details — making every run searchable, comparable, and reproducible.
Side-by-side visual analysis of multiple training run metrics, hyperparameters, and results — reducing time to identify which experimental configurations produce better model performance.
Shared experiment tracking across team members with tagging, reports, and model registry — replacing individual tracking notebooks with a team-shared ML experiment history.
Free plan (200 hours compute, individual). Team $49/user/month. Scale custom. Enterprise custom.
Model
Freemium
Starting price
Free
Free trial
No
Weights & Biases (rank 653) is the most widely adopted ML tracking platform. MLflow (covered) provides open source experiment tracking. Comet ML provides competing tracking. Azure ML provides experiment tracking within Azure ML platform.
A side-by-side look at the closest alternative in this category.
Key facts about model providers, platforms, and team support.
Model Provider
Neptune
Platforms
Python, CLI, Web
Deployment
SaaS, On-premise
Integrations
PyTorch, TensorFlow, Keras, scikit-learn, 50+ frameworks, API
Team Collaboration
Yes
Launch Year
2022
Compliance signals and data-handling notes as reported by the vendor.
SOC 2 Type II. GDPR compliant. On-premise deployment option. Enterprise data handling agreements.
ML training metadata processed on Neptune's cloud infrastructure. On-premise deployment available for data-sensitive organisations. Training data remains in customer infrastructure.
Editorial Verdict
Neptune.ai is a strong AI ML experiment tracking platform for individual researchers and teams wanting generous free tier, strong model comparison visualisation, and on-premise deployment options.
Last verified July 24, 2026.
Free plan (200 hours compute, individual). Team $49/user/month. Scale custom. Enterprise custom.
Free plan (unlimited projects for individuals). Teams $50/user/month. Enterprise custom.
SOC 2 Type II. GDPR compliant. On-premise deployment option. Enterprise data handling agreements.
SOC 2 Type II. ISO 27001. GDPR compliant. HIPAA eligible. Enterprise data handling agreements.
ML training metadata processed on Neptune's cloud infrastructure. On-premise deployment available for data-sensitive organisations. Training data remains in customer infrastructure.
ML training metrics and model artifacts processed on W&B's cloud or self-hosted infrastructure. Enterprise deployment options available for sensitive model and data requirements.
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