Neptune.ai

Metadata store for MLOps built for teams

freemiumproductionmlopsmetadataexperiment-trackingcollaboration

Memory Types

Integrations

pytorch, tensorflow, scikit-learn, kedro


Overview


Neptune.ai is a metadata store for MLOps designed specifically for team collaboration. The platform focuses on being a single source of truth for all ML metadata, making it easy for teams to track experiments, compare results, and maintain reproducibility. Neptune emphasizes ease of use and team workflows.


Unlike broader MLOps platforms, Neptune focuses specifically on metadata management, doing one thing exceptionally well. This makes it lightweight and easy to integrate into existing ML workflows without requiring major infrastructure changes.


Key Features


  • **Metadata Logging**: Track everything about experiments
  • **Experiment Comparison**: Side-by-side comparisons
  • **Model Registry**: Version control for models
  • **Team Collaboration**: Shared workspaces
  • **Query API**: Programmatic access to metadata
  • **Integrations**: 25+ ML tool integrations
  • **Async Logging**: Non-blocking tracking
  • **Reproducibility**: Full experiment recreation

  • When to Use Neptune.ai


    Neptune.ai is ideal for:

  • ML teams prioritizing collaboration
  • Organizations needing detailed metadata tracking
  • Teams using diverse ML tools
  • Reproducibility-focused projects
  • Small to medium ML teams
  • Academic research groups

  • Pros


  • Focused on metadata (does one thing well)
  • Good team collaboration
  • Easy to integrate
  • Async logging doesn't slow training
  • Free tier for individuals
  • Good documentation
  • Clean, intuitive UI
  • Lightweight compared to full MLOps platforms

  • Cons


  • Narrower scope than full platforms
  • Less popular than W&B
  • Smaller community
  • Free tier has limitations
  • Limited production monitoring
  • Some advanced features paid only
  • Primarily metadata (not full MLOps)
  • Less extensive visualization

  • Pricing


  • **Individual**: Free for personal projects
  • **Team**: $54/user/month
  • **Enterprise**: Custom pricing
  • **Academic**: Free for researchers