neptune.ai Pricing Overview
neptune.ai at a glance
Simple pricing for a scalable experiment tracker
| Name | Price | Features |
|---|---|---|
| Free (Cloud SaaS) | Free | For teams starting out with experiment tracking
|
| Team (Cloud SaaS) | $50.001 User Per Month | For a single team developing models
|
| Enterprise (Cloud SaaS) | Contact Us | For organizations with multiple teams that need access control
|
| Custom (self-hosted) | Contact Us | For organizations with unique security requirements
|
neptune.ai pricing & plans
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neptune.ai Pricing Reviews
We've been using Neptune for two years now (we have self-hosted version). I use it almost on daily basis. During this time, it has introduced many new features and found by my team bugs have been fixed. Neptune integrates easily into our workflow and offers broad compatibility with many popular frameworks like PyTorch or AirFlow. This makes it easy to integrate it inside your code and quicker to start using.
For our company, a big plus of Neptune is the availability of a self-hosted version. We were looking for such a solution and found that not many services offer this option at an affordable price. Using the self-hosted version of Neptune is no different from the cloud version for end users. However, it has a several of advantages - unlimited storage and isolation from the global network, which increases data security.
In the end, Neptune has made our workflow more reproduceable, straightforward, visual test. Review collected by and hosted on G2.com.
At the begning of our usage, this tool had bugs, UI wasn't be reach, but the Neptune support/development team fixed these issues, enchanced UI. Currently, we have no significant complaints about Neptune.
Maybe the only downside is that new features are delivered somewhat slower for self-hosted version compared to the cloud version, but they are trying to speed up the delivery of new features to us. Review collected by and hosted on G2.com.
Neptune AI covers all the needs of a Model Registry in an efficient way, thus, it can be a core part of the lifecycle of ML models within a platform.
It offers a user-friendly UI with which, in addition to being able to easily view the registered metadata associated with each execution, we can build more or less complex dashboards. On the user side, the API works in an easy, intuitive and flexible way, providing Data Scientists a simple way to interact with the projects from both script and Jupyters Noteboks.
About the installation, it is as simple as installing a Python package if you use the SaaS option. If you opt for something self-hosted it is a bit more complex but you will always have expert support.
Finally, the Neptune team always responds quickly, offering both mail support and call availability, although most of the time the doubts can be solved by consulting the documentation which is more than correct. Review collected by and hosted on G2.com.
As I have mentioned in the benefits, the use of the Neptune API is very flexible, perhaps excessively flexible if used in a corporate environment since we could find metadata structured in different ways in different projects.
If this is a problem, as a company you may have to develop custom components on top of the Neptune AI API to adapt it to your specific needs and standardize the metadata registry. Review collected by and hosted on G2.com.
