Neptune.ai pricing guide details
Pricing Overview
Neptune.ai operates under an enterprise pricing model following OpenAI's definitive acquisition agreement announced in December 2025. Before the acquisition, Neptune.ai offered a freemium structure with a free tier for individual researchers and small teams, alongside paid plans for larger organizations requiring advanced collaboration, expanded storage, and enterprise-grade security features. The free tier provided core experiment tracking, metric visualization, and basic collaboration features at no cost, making it accessible to solo ML practitioners and academic researchers. Paid tiers required direct contact with Neptune.ai's sales team for custom quotes tailored to usage volume, team size, storage requirements, and infrastructure preferences including cloud-hosted or on-premise deployment. With the OpenAI acquisition now underway, Neptune.ai's standalone pricing page is no longer publicly listed. Teams evaluating Neptune.ai today should contact OpenAI directly to understand current availability, integration timelines, and any future pricing changes resulting from the transition into OpenAI's research infrastructure.
Plan Comparison
Neptune.ai historically structured its offering around three tiers: a free Individual plan for solo researchers, a Team plan for collaborative ML workflows, and an Enterprise plan for large-scale foundation model training deployments. Each tier built progressively on the previous one, adding collaboration depth, storage capacity, and administrative controls.
| Feature | Individual (Free) | Team | Enterprise |
|---|---|---|---|
| Experiment Tracking | Yes | Yes | Yes |
| Metric Visualization | Yes | Yes | Yes |
| Run Comparison | Basic | Advanced | Advanced |
| Collaboration | Limited | Full | Full |
| Storage | Limited | Expanded | Custom |
| Project Management | Basic | Full | Full |
| SSO / SAML | No | No | Yes |
| Role-Based Access Control | No | Basic | Advanced |
| Priority Support | No | No | Yes |
| Custom Integrations | No | Limited | Yes |
| On-Premise Deployment | No | No | Yes |
| SLA Guarantee | No | No | Yes |
| Audit Logging | No | No | Yes |
The Individual plan served solo researchers needing core experiment tracking capabilities for personal projects and prototyping. Users could log experiments, visualize training metrics, and compare runs within a single workspace. The Team plan unlocked full collaboration features, enabling multiple researchers to share experiments, annotate runs, and coordinate across shared ML projects. This tier also expanded storage limits and introduced basic project-level access controls. The Enterprise plan added comprehensive security features including SSO/SAML authentication, advanced role-based access control, audit logging, and the option for on-premise deployment. Enterprise customers also received dedicated infrastructure, priority support with contractual SLAs, and custom integration capabilities for connecting Neptune.ai into existing ML pipelines. Following the OpenAI acquisition, these tiers are no longer actively marketed, and prospective users should inquire about current access options through OpenAI.
Hidden Costs and Considerations
Neptune.ai's enterprise model carried several costs beyond the base subscription that teams should account for during budgeting. Storage overages for teams logging thousands of experiments with large model artifacts, checkpoints, and datasets added up quickly, particularly for foundation model training where individual runs generate terabytes of metadata. On-premise deployments required dedicated DevOps resources for initial installation, ongoing maintenance, security patching, and version upgrades. Data migration costs applied when moving historical experiment data from competing platforms like Weights & Biases or MLflow, particularly for teams with years of experiment histories embedded in their workflows. The OpenAI acquisition introduces additional strategic uncertainty: teams that build critical training workflows around Neptune.ai face potential vendor lock-in risks if the product becomes exclusive to OpenAI's internal research stack rather than remaining a commercially available tool.
How Neptune.ai Pricing Compares
Neptune.ai's pricing positioned it at the premium end of the MLOps experiment tracking market, targeting teams training large-scale foundation models rather than small ML projects. Competitors in this space offer more transparent pricing with publicly listed tiers, making direct cost comparison significantly easier for budget-conscious teams evaluating their options.
| Tool | Free Tier | Paid Starting Price | Enterprise | Key Differentiator |
|---|---|---|---|---|
| Neptune.ai | Yes | Contact Sales | Contact Sales | Foundation model training focus |
| Weights & Biases | Yes | $60/mo (Pro) | Custom | Broad MLOps ecosystem |
| ClearML | Yes (Open Source) | $15/mo | Custom | Self-hosted open-source option |
| Comet ML | Yes | $19/mo (Pro) | Custom | Model production monitoring |
Weights & Biases is the closest competitor in positioning and market presence, charging $60/mo for its Pro tier. This makes it the most expensive option with publicly listed pricing, though its feature set spans beyond experiment tracking into model registry, dataset versioning, and production monitoring. ClearML takes a fundamentally different approach with a fully open-source core product that teams can self-host at no cost, while paid tiers starting at $15/mo provide managed cloud hosting and additional enterprise features. Comet ML occupies the middle ground at $19/mo for Pro features, with particular strength in model production monitoring and governance workflows.
Neptune.ai's lack of published pricing historically made it harder to evaluate upfront, but its specialized depth in tracking months-long foundation model training runs with multiple steps and branches justified custom quotes for large research teams. The platform's ability to handle massive metric volumes and enable comparison of thousands of runs in seconds set it apart from competitors focused on smaller-scale ML experimentation. With the OpenAI acquisition, Neptune.ai's competitive positioning shifts fundamentally. The platform may evolve into an internal research tool deeply integrated into OpenAI's training stack rather than continuing as a standalone commercial product competing for MLOps market share.