Neptune for Enterprise

The metadata store for your entire organization

Give your teams a best-in-class MLOps tool built for model metadata management at scale.

Neptune empowers your data scientists and ML engineers to:

Understand all your experimentation with unparalleled visibility
Control your models with an easily-traceable single source of truth
Build more reliable models faster with automated experiment tracking

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Trusted by Enterprise worldwide

Neptune runs smoothly whether you run 50 or 5000 experiments at once. The consistent operating speed makes it possible for you to automate and standardize your training processes — at any scale.

All of the data you store in Neptune gets encrypted at rest.

Neptune also supports self-hosting, giving you flexible deployment options to accommodate your unique security requirements. Deploy either on-prem or in your private could.

Easily add or onboard new team members — at no extra cost. Neptune gives you unlimited users as standard. So you can share your ML projects with anyone in your org — even if they aren’t actively involved.

Product security

Complete access control

Neptune allows you to authorize users for entire workspaces or individual projects only. And filter access based on role — including admin, owner, contributor, or view-only.

With SSO, you get the controls you need to govern identity and usage across your entire organization.

Single source of truth

All your metadata. All of your models.
In one place.

Neptune’s easily searchable logging system organizes all your model-building metadata in one place.

With your entire project history at your fingertips, your team can rapidly reproduce past versions of experiments. And with 20:20 real-time visibility of projects throughout your organization, you can monitor the model development moving the needle for your business.

Improved productivity

Cross-corporation collaboration

Neptune’s customizable workspaces and persistent shareable links make collaborating on experiments effortless for your team.

And thanks to Neptune’s flexibility, your engineers can always share the results and progress of experiments in a common environment. When the only other option is building a home-grown solution, using Neptune saves your team days of dev time. Literally.

Integrates with any MLOps tool stack

Neptune seamlessly hooks into dozens of the most popular frameworks, libraries, and ML tools. So it’s simple for you to integrate into your enterprise stack. And scale your operations at speed.

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Everything you’d expect from a solution built to scale

Our dedicated onboarding team will be on hand to make sure you get set up smoothly (in only a few hours).

With Neptune’s Enterprise plan, you get priority support from a dedicated customer success rep. So there’s always someone available to answer your questions and fix any issues you encounter.

Neptune constantly monitors deployment with a geographically distributed network of uptime checks.

Should any production incidents occur (extremely rare!), we have a team of engineers available 24/7 to fix the issue as fast as possible.

Compliance is top of your priority list. Ours too. We’re currently in the process of receiving SOC2 certification.

We will be fully SOC2 certified by the end of Q1 2023.

Neptune’s UI and the front-end work great, and you don’t feel that you ‘fight’ with it. So instead of ‘fighting’ the tool, the tool itself is helping.

Hubert Bryłkowski Senior Machine Learning Engineer at Brainly

We trained more than 120 000 models in total, for more than 7000 subproblems identified by various combinations of features. Due to Neptune, we were able to filter experiments for given subproblems and compare them to find the best one. Also, we stored a lot of metadata, visualizations of hyperparameters’ tuning, predictions, pickled models, etc. In short, we were saving everything we needed in Neptune.

Patryk Miziuła Senior Data Scientist at deepsense.ai

Neptune is an excellent choice for users with large-scale training activities, as the Neptune workflow is already set up to handle the most common model training scenarios. It’s a great tool to organize deep learning experiments, whereas most people have home-grown dashboards or, worse, no dashboards.

Dr. Robert Toth Founder of Theta Tech AI

We wanted a friendly method for even a non-technical person to look at a couple of plots, scores, or similar, and decide if we wish to send the processed data (to the client), or maybe someone else with more knowledge should investigate it.

Łukasz Grad Chief Data Scientist at ReSpo.Vision