Compare Neptune vs Comet

Similar software, different focus

vs

Both Neptune and Comet allow you to track, visualize, and compare your ML model metadata. And both make easy collaboration a reality. But only Neptune will support the large scale model training confidently.

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Interface

The user interface that works as fast as you do

You shouldn’t have to live with a lagging UI or long experiment load times. Neptune is — and always will be — everything you need to manage your ML metadata. And nothing else. We built it as lightweight as possible. So you can work as fast as possible.

Scale from 10 to 10000+ runs. With zero effect on speed.

Deployment

Smooth and scalable on-prem deployment

If your organization handles sensitive data that must remain on local servers, or if your industry has particular compliance requirements, you can deploy Neptune on your infrastructure. You get high availability, horizontal scalability, and everything inside your network by design.

We’re fully invested in self-hosted. You can use it on your own terms, without scale limitations or a push toward SaaS.

For us, self-hosted deployment was too difficult and time-consuming in the previous solution. We could achieve that with Neptune, and it allowed us to close important deals that had stringent security requirements.

Daniel Danciu CTO at Cradle Bio

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Feature-by-feature comparison

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what makes Neptune different

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Neptune

Comet

Commercial offering

Commercial offering

Open-source software or a managed cloud service?

Managed cloud service

Managed cloud service

Pricing model

User based and usage based (ingestion data points)

User based and usage based (training hours)

Guarantees around service levels (SLOs / SLAs)

Support 24×7

User access management (SSO, ACL)

Security policy and compliance

Yes

General information

Deployment

Cloud (SaaS)

Yes

Yes

Self-hosted (on-prem, private cloud)

Installation in air-gapped environment

Yes

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 comet_ml 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 comet_ml library.

Flexibility and accessibility

Accessing model metadata

CLI/custom API and Python SDK

CLI/custom API, REST API, Python SDK, R SDK, Java SDK

Supported operations

Search, Update, Delete, Download

Search (limited), Update, Delete, Download

Logging modes

Offline, Disabled/Off, Asynchronous

Offline, Asynchronous

Customizable metadata structure

Distributed training support

Pipelining support

Yes

Live monitoring

Yes

Webhooks and notifications

No

Webhooks only for model management. Notifications only for a change in experiment status.

Resuming experiments

Forking runs

No

Capabilities

Log and display

Parameters

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

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

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

Yes

Tags

Descriptions/comments

Rich format

Images, Plots, Video, Audio

Plots, Interactive visualizations, Video, Audio

Hardware consumption

CPU, GPU, Memory

Dataset

No

Yes

Code versions

Git, Source, Notebooks

System information

Console logs, Execution command

Console logs, Error stack trace, System details

Environment config

No

pip requirements.txt, conda env.yml, Docker Dockerfile

Files (model binaries, CSV)

External file reference (S3 buckets)

No

Searching & filtering

Searching on multiple criteria

Query language and filtering options

Both regex and fixed selectors

Query language

Custom attribute filtering (e.g. tags)

Yes

Yes

Support for regular expressions

Exact and substring only

Auto-update charts based on regex

No

Saving searches and filter history

Yes

Visualizations

Custom (calculated) metrics

No

Forked charts

No

Histograms

No

Custom axes

Customizable global settings

Customizable legends

Yes

Comparing experiments

Table format diff

Single-metric overlayed plots

Multi-metric overlayed plots

Yes

Grouping experiments by metadata

Scatter plot

Parallel coordinates plot

No

Parameter importance plot

No

No

Rich format (side by side)

No

Images, Plots, Texts

Data versions diff

No

Cross-project comparisons

Custom analysis

Experiment table customization

Saving experiment table views

Dashboards combining different metadata types

Custom widgets and plugins

No

Persistent custom plots coloring

Yes

Yes

Collaboration and knowledge sharing

Reports

Adding text in Reports

Commenting

Downloading charts

Yes

Sharing persistent UI links

Yes

User groups and ACL

This table was updated on 14 May 2025. Some information may be outdated.
Report outdated information here.

Neptune is way better than the other tools I’ve tried, like Comet and WandB. In my opinion, Neptune has the cleanest and most intuitive interface — that’s the main reason I prefer using it.

Klaus-Michael Lux Data Scientist

You need a tracker purpose-built for foundation models

With Neptune, that’s exactly what you get.

Check out the best-fit plan for your business today.

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