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Neptune AI

ML metadata store for experiment tracking and model registry Pricing: Freemium. See pros, cons, alternatives, and comparisons.

  • Free plan available
  • No credit card
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What is Neptune AI?

Neptune AI is a metadata store designed to help machine learning teams track experiments and manage models. It acts as a central repository where you can log parameters, metrics, artefacts, and other experiment details from your training runs. This makes it easier to compare different model versions, reproduce results, and understand what settings produced your best outcomes. The tool integrates with popular ML frameworks and libraries, allowing you to instrument your training code with minimal changes. Teams use it to avoid losing track of experiments, maintain a searchable history of model iterations, and collaborate on model development. It's particularly useful if you run many experiments and need to remember which hyperparameter combinations worked best.

Key features

Experiment tracking

log parameters, metrics, and outputs from training runs for easy comparison

Model registry

store and version control trained models with metadata and performance records

Metadata storage

capture custom artefacts, code versions, and environment details alongside results

Collaborative workspace

share experiment results and model information across your team

Integration support

connect with TensorFlow, PyTorch, scikit-learn, and other common ML libraries

Search and filter

query historical experiments to find specific runs or model versions

Pros & cons

Advantages

  • Free tier lets you get started without payment, suitable for small projects and individuals
  • Straightforward integration with existing ML workflows and popular frameworks
  • Provides a persistent record of experiments, reducing the risk of losing important results or forgetting what you tried

Limitations

  • Free tier has storage and retention limits; larger teams or longer histories may require paid plans
  • Learning curve for teams new to experiment tracking tools; requires deliberate logging in your training code

Use cases

Data scientists comparing dozens of model configurations to find the best performer

Teams needing to reproduce past experiments or explain which settings produced a specific model

ML engineers maintaining a registry of production-ready models with their training details

Research groups tracking long-running experiments across multiple team members

Ready to try Neptune AI?

Pricing

Free

Free

Basic experiment tracking, limited storage, suitable for individuals and small teams

Pro

Contact for pricing

Increased storage, advanced features, team collaboration tools, priority support

Enterprise

Contact for pricing

Custom deployment, dedicated support, advanced security, on-premises options

Get started with Neptune AI

Click through to Neptune AI and start using it now.

  • Free plan available
  • No credit card