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