Neptune vs Pachyderm


Neptune

Pachyderm
Commercial Requirements
Standalone component

Standalone tool

Can be hosted both on-prem and on the cloud
Managed cloud service

Base package is open-source, with an enterprise grade commercial offering available

Ranges from a free to use open-source plan to enterprise grade commercial offerings. Read more about their enterprise offering here

No
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.
Minimal. Just a few lines of code needed for tracking. Read more

Extensive code and infrastructure changes required. Check their quick start to get an overview
Yes, through the neptune-client library

Yes, through their CLI

No

No
No

No
No

No
No

No
No

No


No

No
Experiment Tracking




No

No
No

No
No

No

No
No

No
No

No

No

No

No

N/A



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

N/A

No

N/A
No

No
Model Registry
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
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Report outdated information here.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 differences then?

- Neptune is a metadata store with the main focus on experiment tracking and model registry
- Pachyderm serves, above all, for data processing and orchestration.
Neptune and Pachyderm are not mutually exclusive. In fact, they can be complimentary element of the MLOps stack.
Check how to start using Neptune.
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
Donât overthink it