The only affordable tool for ML metadata management at scale


When the number of runs you log at once grows in size, MLflow can break down.
When the number of folks on your team grows in size, WandB’s pricing can break your budget.
Avoid both these things, with Neptune.

Take a deep dive into the differences between WandB, MLflow and Neptune

Weights & Biases

MLflow
Neptune
Commercial Requirements

Standalone component

Open-source platform which offers four separate components for experiment tracking, code packaging, model deployment, and model registry
Standalone component

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

Open-source
Managed cloud service

Community support only

No

No
General Capabilities

No special requirements other than having wandb python library 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
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.

Yes, they have SDK clients in various languages, and a CLI
Yes, through the neptune-client library

No

Yes
No







No

No
No
Experiment Tracking









No
No

NA

No
No

NA

No
No





Yes

Yes
No









No

No
No

Yes

No
No

No

No
No



No

No
No



No

No
No

No

No
No

No

No
No





No

No
No





No

No
No

No

No
No







Regex on names at the project level, fixed selectors at the run level

Query language




Model Registry

No

No
No

No
No
Integrations and Support

No

No