Neptune vs Kubeflow


Neptune

Kubeflow
Commercial Requirements
Standalone component. ML metadata store that focuses on experiment tracking and model registry

Part of the Kubernetes environment

Almost all popular cloud providers maintain their own distribution of Kubeflow. It can also be installed on-premises manually. Read about the differejnt installation options available here
Managed cloud service

The base product is open source, with managed distributions made available by cloud providers

The open source version is free. Maintainers have their own priving plans for their managed distributions

Community support for the open source version. Different options available for managed distributions

Not for the open source version. Different options available for managed distributions

Different options available for managed distributions. Read more here

Not for the open source version. Different options available for managed distributions
General Capabilities
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

Advanced setup required for manual install. Installation requirements for the managed distribution varies depending on the cloud provider. Read more here
Yes, through the neptune-client library

Yes, through the kubeflow python client

No

No
No

No
No

No


No

No
No

No
Experiment Tracking

No

No



No

N/A
No

No
No

NA
No

NA
No

NA

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
No

No
No

No


No

No

No

No

No

No
No

No

No

No

Yes

No
Yes

No

Query language

N/A

No

No

Limited

N/A
No

N/A
No

N/A
Model Registry
No

N/A
No

N/A
No

N/A
No

N/A
No

N/A
Limited

N/A
No

N/A
No

N/A
No

N/A
No

N/A
No

N/A
No

N/A
Integrations and Support
No

No
No

No
No

No
No

No
No

No
No

No
No

No
No

No
No

No
No

No
No

No
Report outdated information here.
What are the key advantages of Neptune then?

- Neptune is a paid hosted metadata store with the main focus on experiment tracking and model registry
- Kubeflow is an open-source project created to enable easier deployment of ML workflows on Kubernetes
Neptune and Kubeflow are not mutually exclusive. In fact, Neptune can serve as a great solution for experiment management and model registry inside the Kubeflow Pipelines.
Check how to start using it.
See these features in action
Sign up to Neptune and install client library
pip install neptune
Track experiments
import neptune
run = neptune.init_run()
run["params"] = {
"lr": 0.1, "dropout": 0.4
}
run["test_accuracy"] = 0.84
Register models
import neptune
model = neptune.init_model()
model["model"] = {
"size_limit": 50.0,
"size_units": "MB",
}
model["model/signature"].upload(
"model_signature.json")
Thousands of ML people already chose their tool
It only takes 5 minutes to integrate Neptune with your code
Don’t overthink it