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Neptune vs ClearML

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Neptune

ClearML

Commercial offering

Open-source software or a managed cloud service?

Managed cloud service

Available both as an open-source platform, and a managed cloud service

Pricing model

User based and usage based (ingestion data points)

User based and usage based (multiple elements)

Guarantees around service levels (SLOs / SLAs)

Yes

Support 24×7

Yes

User access management (SSO, ACL)

Yes

Security policy and compliance

Yes

General information

Deployment

Cloud (SaaS)

Yes

Self-hosted (on-prem, private cloud)

Yes

Installation in air-gapped environment

Yes

Setup

Infrastructure requirements

Minimal setup—install the Python client and ensure internet access (for managed hosting). Self-hosting requires additional infrastructure; see requirements here.

Minimal setup—install clearml python package and ensure internet access (for managed hosting). Self-hosting requires additional infrastructure; see requirements here.

Integration with the training process

A few lines of code via the Neptune Python library.

A few lines of code via ClearML python package.

Flexibility and accessibility

Accessing model metadata

CLI/custom API and Python SDK

CLI/custom API, REST API, Python SDK

Supported operations

Search, Update, Delete, Download

Logging modes

Offline, Disabled/Off, Asynchronous

Offline, Disabled/off, Asynchronous, Synchronous

Customizable metadata structure

Yes

Distributed training support

Yes

Pipelining support

Yes

Live monitoring

Yes

Webhooks and notifications

Limited

Resuming experiments

Yes

Forking runs

Yes

Capabilities

Log and display

Parameters

Yes

Single values (metrics, losses, gradients, activations, etc.)

Yes

Series of values (metrics, losses, gradients, activations, etc.)

Yes

Series aggregates (min/max/avg/var/last)

Yes

Limited (only avg, min, and max)

Tags

Yes

Descriptions/comments

Yes

Rich format

Images, Plots, Video, Audio

Hardware consumption

Yes

CPU, GPU, Memory

Dataset

Yes

Code versions

Git, Source

Git, Source (limited)

System information

Console logs, Execution command

Console logs, Error stack trace, System details

Environment config

Only logging: pip requirements.txt, conda env.yml, Docker Dockerfile

Files (model binaries, CSV)

Yes

External file reference (S3 buckets)

Yes

Searching & filtering

Searching on multiple criteria

Yes

Limited

Query language and filtering options

Both regex and fixed selectors

Fixed selectors

Custom attribute filtering (e.g. tags)

Yes

Support for regular expressions

Yes

Auto-update charts based on regex

Yes

Saving searches and filter history

Yes

Visualizations

Custom (calculated) metrics

Forked charts

Histograms

Custom axes

Customizable global settings

Yes

Limited

Customizable legends

Yes

Comparing experiments

Table format diff

Yes

Single-metric overlayed plots

Yes

Multi-metric overlayed plots

Yes

Grouping experiments by metadata

Yes

Scatter plot

Yes

Parallel coordinates plot

Yes

Parameter importance plot

Rich format (side by side)

Images, Audio, Video, Plots

Data versions diff

Yes

Cross-project comparisons

Yes

Custom analysis

Experiment table customization

Yes

Limited

Saving experiment table views

Yes

Dashboards combining different metadata types

Yes

Custom widgets and plugins

Persistent custom plots coloring

Yes

Collaboration and knowledge sharing

Reports

Yes

Adding text in Reports

Commenting

Downloading charts

No (only saving in reports)

Sharing persistent UI links

Yes

User groups and ACL

Yes

This table was updated on 14 May 2025. Some information may be outdated.
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Thousands of ML people already chose their tool

Previously used tensorboard and azureml but Neptune is hugely better. In particular, getting started is really easy; documentation is excellent, and the layout of charts and parameters is much clearer.

Simon Mackenzie AI Engineer and Data Scientist

(…) thanks for the great tool, has been really useful for keeping track of the experiments for my Master’s thesis. Way better than the other tools I’ve tried (comet / wandb).

I guess the main reason I prefer neptune is the interface, it is the cleanest and most intuitive in my opinion, the table in the center view just makes a great deal of sense. I like that it’s possible to set up and save the different view configurations as well. Also, the comparison is not as clunky as for instance with wandb. Another plus is the integration with ignite, as that’s what I’m using as the high-level framework for model training.

Klaus-Michael Lux Data Science and AI student, Kranenburg, Germany

This thing is so much better than Tensorboard, love you guys for creating it!

Dániel Lévai Junior Researcher at Rényi Alfréd Institute of Mathematics in Budapest, Hungary

While logging experiments is great, what sets Neptune apart for us at the lab is the ease of sharing those logs. The ability to just send a Neptune link in slack and letting my coworkers see the results for themselves is awesome. Previously, we used Tensorboard + locally saved CSVs and would have to send screenshots and CSV files back and forth which would easily get lost. So I’d say Neptune’s ability to facilitate collaboration is the biggest plus.

Greg Rolwes Computer Science Undergraduate at Saint Louis University

Such a fast setup! Love it:)

Kobi Felton PhD student in Music Information Processing at Télécom Paris

For me the most important thing about Neptune is its flexibility. Even if I’m training with Keras or Tensorflow on my local laptop, and my colleagues are using fast.ai on a virtual machine, we can share our results in a common environment.

Víctor Peinado Senior NLP/ML Engineer