Neptune vs Kubeflow
Which tool is better?

Neptune is a more lightweight tool that gives you more experiment tracking capabilities, comes with an experiment-focused UI, better Jupyter Notebook experience, and more machine learning framework integrations than Kubeflow does. Log anything you want, compare experiments interactively and organize and share the work of your entire team with Neptune!

Why Choose Neptune over Kubeflow?

Neptune can serve all the experiment tracking needs of your team (any language, any framework, any infrastructure)
it is easier to set up and puts the focus on experimentation rather than orchestration and management of machine learning workflows.

Show differences only

Kubeflow
Neptune
Pricing
Kubeflow
Neptune
Pricing

Free

  • Free for individuals, non-profit and educational research
  • Team: from $49
  • Enterprise: from $499
Free plan limitations

  • Free: 1 user
  • Unlimited private and public projects
Open-Source
Easy integration
Lightweight
Experiment Tracking Features
Kubeflow
Neptune
Data Versioning

Limited

Model Versioning

Limited

Environment Versioning

Limited

Notebook Autosnapshots
Logging Data to Finished Experiment
UI Features
Kubeflow
Neptune
Customizable Experiment Dashboard
Experiment Organization

Limited

Saving Experiment Views
Notebook Diffs
Comments
Integrations
Kubeflow
Neptune
MLflow
Sacred
Google Colab
Scikit-Learn
fastai
skorch
PyTorch Lightning
PyTorch Ignite
Catalyst
Optuna
Scikit-Optimize
HiPlot

This page was updated on 28 April 2020. Some information may be outdated.
See some incorrect info? -> Email us at marketing@neptune.ai and we will update it.

The most lightweight experiment management
tool that fits any workflow

Keeping track of machine learning experiments made simple.

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Why Neptune is the Best Alternative to Kubeflow

ZERO MAINTENANCE

Is it easy to set up and maintain Kubeflow server and visualization dashboard for your entire team?

With Neptune you get all your experiment data saved and backed-up on a hosted server or on-prem installation. You can manage user permissions and share experiments in the beautiful UI with no additional overhead. Powerful, simple, and available for you and your team in minutes.

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EXPERIMENT-FOCUSED UI

Is your Kubeflow UI built for experimentation?

Neptune was built with a strong focus on machine learning experiments. It gives you the ability to scroll through model predictions, compare metrics and loss charts, group and compare hyperparameters and much more.

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DASHBOARD VIEWS

Can you save different experiment dashboard views in Kubeflow?

When you have multiple users working on many ideas the things you want to look at change… and so should your experiment dashboard.
Neptune lets you customize, change, and save experiment dashboard views depending on your needs.

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IMAGE CHANNEL DISPLAY

Can you scroll through your images and charts?

Neptune lets you log images and charts to multiple image channels and scroll through them to quickly see the progress of your model training. Get a full picture of what is happening in your training and validation loops by leveraging more information!

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NOTEBOOK AUTOSNAPSHOTS

Does Kubeflow snapshot your Jupyter notebooks automatically?

Neptune notebook integration automatically snapshots your .iipynb whenever you run a cell with neptune.create_experiment() in it. Whether you remember to submit your experiment or not everything will be safely versioned and ready to be explored.

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ANALYZE EXPERIMENT DASHBOARD IN JUPYTER NOTEBOOK

Does Kubeflow let you fetch your experiment dashboard directly to a pandas DataFrame?

With Neptune you can fetch whatever information you or your teammates tracked and explore it however you like. We have some nice exploratory features, like HiPlot integration to help you with that.

neptune.init('USERNAME/example-project')

make_parallel_coordinates_plot(

     metrics= ['eval_accuracy', 'eval_loss',...],

     params = ['activation', 'batch_size',...])
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NOTEBOOK VERSIONING AND DIFFING

Does Kubeflow let you track your exploratory analysis?

Neptune goes beyond the tracking of machine learning experiments and allows you to version your exploratory data analysis or results exploration as well!
Once it is saved in Neptune you can name, share, download or see diffs of your notebook checkpoints.

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jupyter notebook compare
KUBEFLOW + NEPTUNE

Can you use Kubeflow for orchestration and Neptune for experiment tracking?

Kubeflow is great at orchestration of your machine learning workflows but is missing a lot of the experiment tracking functionality (it’s not the focus of the project). The best thing is you can actually use both tools together! Use Neptune experiment tracking to have a great view of your experiments with run orchestration from Kubeflow!

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