We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”
Neptune vs Aim



Standalone component. ML metadata store that focuses on experiment tracking and model registry
Standalone tool
Can be deployed both on-premises and/or on the cloud, but has to be self-managed
Managed cloud service
Open-source
Email and chat support for individuals and teams. Well defined support SLAs for enterprise customers
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No special requirements other than having the neptune-client installed and access to the internet if using managed hosting. Check here for infrastructure requirements for on-prem deployment
Aim is available for Linux/MacOS running python 3.6+
Minimal. Just a few lines of code needed for tracking. Read more
Minimal. Only a few lines of code need to be added
Yes, through the neptune-client library
Yes, through its python API
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This page was updated on 12 March 2022. Some information may be outdated.
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What are the key advantages of Neptune, then?
- Model registry and data versioning functionality
- User management and team collaboration features
- Hosted and sharable environment
- Customizable metadata structure
- Scalability with thousands of runs

See these features in action
1. Create a free account
Sign up2. Install Neptune client library
pip install neptune-client
3. Add logging to your script
import neptune.new as neptune
run = neptune.init('Me/MyProject')
run['params'] = {'lr':0.1, 'dropout':0.4}
run['test_accuracy'] = 0.84
4. Or see how it works in a notebook (no registration)
Try live notebookThousands of ML people already chose their tool

“Previously used tensorboard and azureml but Neptune is hugely better. In particular, getting started is really easy; documentation is excellent, and the layout of charts and parameters is much clearer.”
“(…) thanks for the great tool, has been really useful for keeping track of the experiments for my Master’s thesis. Way better than the other tools I’ve tried (comet / wandb).
I guess the main reason I prefer neptune is the interface, it is the cleanest and most intuitive in my opinion, the table in the center view just makes a great deal of sense. I like that it’s possible to set up and save the different view configurations as well. Also, the comparison is not as clunky as for instance with wandb. Another plus is the integration with ignite, as that’s what I’m using as the high-level framework for model training.”
“While logging experiments is great, what sets Neptune apart for us at the lab is the ease of sharing those logs. The ability to just send a Neptune link in slack and letting my coworkers see the results for themselves is awesome. Previously, we used Tensorboard + locally saved CSVs and would have to send screenshots and CSV files back and forth which would easily get lost. So I’d say Neptune’s ability to facilitate collaboration is the biggest plus.”
“For me the most important thing about Neptune is its flexibility. Even if I’m training with Keras or Tensorflow on my local laptop, and my colleagues are using fast.ai on a virtual machine, we can share our results in a common environment.”