We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”
Neptune vs MLflow
Scalable MLflow alternative with a hosted UI built for teamwork

Looking for an experiment tracking and/or model registry solution? Not sure how Neptune.ai and MLflow are actually different? Or maybe you’re using MLflow but wanted to check some alternatives?
You’re in the right place. On this page we break down:
- what are the differences between MLflow and Neptune
- why do people switch from MLflow to Neptune
- what are the cases when you should go with Neptune (and when you shouldn’t)
When should you go with Neptune?
Why others choose Neptune over MLflow?
MLflow is open-source and free to use. You can customize it if you want to, but you have to set it up and maintain the infrastructure.
Neptune.ai is a hosted SaaS solution (an on-prem option is also available). It’s easy to plug it into your workflow and it comes with user support.
So, in this aspect, it’s the good, old “buy vs use open-source” dilemma. Many ML people we talked to have a general impression that open source tools are good enough for very small teams or individual contributors with a need for basic tracking. After some time you just get to the point when it’s not enough.
Now when you look into what parts of the ML lifecycle both tools cover, the scope is quite similar.
MLflow does experiment tracking and model registry, but also comes with two additional components: Projects (packaging format) and Models (general format for sending models to deployment tools).
Neptune.ai focuses on experiment tracking and model registry, but it doesn’t limit its functionality. Within those features, it covers also dataset versioning, model versioning, or monitoring model training live. On top of that, thanks to the flexible API you can package models however you like and attach any metadata in any structure to a model version.
This flexibility is often brought up by Neptune users, as one of the first advantages they notice. The tool easily hooks into multiple frameworks, so no matter what solution you use for training, deploying, or monitoring models, Neptune will fit in.
There’s one more thing that will probably be important when comparing Neptune and MLflow, especially for the teams.
Neptune comes with out-of-the-box collaboration features.
- You can share persistent links to the UI with other team members, or with external people if needed.
- And you can create multiple projects and manage who can access them.
There are teams that use the open-source MLflow, but the collaboration is much more challenging there. Team management features are available in the Managed MLflow which is a paid solution developed by Databricks. So probably this kind of functionality won’t be added to the free, open-source version.
MLflow is a solid solution, and it checks many boxes when the team needs a simple experiment tracking and model registry. But it doesn’t scale well. Many ML teams quickly reach its limits and look for something more advanced.
Here’s what Neptune users say

“For now, I’m not using MLflow anymore ever since I switched to Neptune because I feel like Neptune is a superset of what MLflow has to offer.” [Read full case study]
“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.”
Give Neptune a try
pip install neptune-client
import neptune.new as neptune
run = neptune.init('Me/MyProject')
run['params'] = {'lr':0.1,
'dropout':0.4}
run['test_accuracy'] = 0.84
Dig deeper into the differences between Neptune and MLflow


Standalone component. ML metadata store that focuses on experiment tracking and model registry
Open-source platform which offers four separate components for experiment tracking, code packaging, model deployment, and model registry
Tracking is hosted on a local/remote server (on-prem or cloud). Is also available on a managed server as part of the Databricks platform
Managed cloud service
The standalone product is open-source, while the Databricks managed version is commercial
Email and chat support for individuals and teams. Well defined support SLAs for enterprise customers
Community support for the open-source version, various support plans for the databricks managed version
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
No requirements other than having mlflow installed if using a local tracking server. Check here for infrastructure requirements for using a remote tracking server
Minimal. Just a few lines of code needed for traking. Read more
Minimal. Just a few lines of code needed for traking. Read more
Yes, through the neptune-client library
Yes, they have SDK clients in various languages, and a CLI
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This table was updated on 14 June 2022. Some information may be outdated.
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