Pick a plan that suits your needs

Use as a service or deploy on any cloud or your own hardware.

Individual

Basics of Neptune for every data scientist. Perfect if you’re student, researcher, or working on side-projects.

$0

Per month

unlimited experiments
unlimited notebook checkpoints
unlimited private and public projects
100 GB storage
User management
Collaboration features

Team

Designed for teams that want to get a taste of collaboration in data science projects with Neptune.

$0

Per user / month

unlimited experiments
unlimited notebook checkpoints
1 private or public project
10 GB storage
User management
Collaboration features

Team Pro

Well suited for professional data science teams.

$79

Per user / month

unlimited experiments
unlimited notebook checkpoints
unlimited private and public projects
1 TB storage
User management
Collaboration features

TESTIMONIALS

Perfect for teams, loved by individuals

reply.ai + neptune.ml
Reply.ai team leverages remote work while keeping results in a common environment.

At Reply.ai our goal is to make customer services faster and smarter by automating repetitive processes and delivering instant and personalized attention on messaging channels.
We're developing products that leverage Natural Language Processing to improve customer care. And as a fully remote team, we were looking for a tool that would allow us to track experiments, compare results, and share both datasets and models, without changing our usual workflow.
As a team leader I'm also very concerned about the reproducibility of our experiments, especially when different data scientists and Machine Learning engineers have their own ways of doing things. It's not always easy to follow software engineering best practices.
For me the most important thing about Neptune is its flexibility. Even if I'm training with Keras or Tensorflow in my local laptop, while my team folks are using fast.ai on a virtual machine, we can share our results in a common environment. Also, thanks to Neptune's Query API, our backend team, which is in charge of models deployment, is able to programmatically access all the experiments we run and fetch the best model.

Victor Peinado
Senior NLP/ML Engineer at Reply.ai
New Yorker using Neptune
NewYorker is benefiting from keeping track of machine learning experiments.

New Yorker is a leading German clothing retailer managing over 1000 branches spread across 40 countries. Our data science team focuses on price forecasting and object detection. Neptune allows us to keep everything we want to know about experiments in one centralized place where our team can easily access it. What we really like about Neptune is that it easily hooks into multiple frameworks. Keeping track of machine learning experiments systematically over time and visualising clearly the output adds a lot of value for us.

Ronert Obst
Head of Data Science
Deepsense.ai using Neptune
Collaboration is enabling deepsense.ai to build top quality machine learning models for their clients.

Here at Deepsense.ai we are providing machine and deep learning solutions and consultancy for market leaders such as BCG, IBM, Juniper, EY, nielsen, nVidia, and Loreal. Using Neptune, we are able to cooperate more closely with our customers and eliminate most of the problems related to project communication. We limited the number of meetings to the ones that are really necessary to make strategic decisions, since clients have real time access to what we do and we can now collaborate on a regular basis. When everything is tracked and organized in the one knowledge centre we don’t need to create much additional documentation for our clients. With Neptune we are able to deliver our projects faster and we have optimized time spent onboarding new data scientists to a project.

Robert Bogucki
Chief Science Officer, deepsense.ai

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