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Compare Neptune vs MLflow

If you want to scale your model development, you need Neptune.

neptune-logo
vs
mlflow

MLflow is great for Data Scientists and ML Engineers looking for a basic ML lifecycle platform. But it doesn’t give you the functionality or collaborative features you need as your team and projects grow in size. Neptune does.

Show me pricing Show me feature-by-feature comparison
icon MLflow at a glance
  • Functionality:
    • experiment tracking
    • model registry
    • model packaging
    • pipelines
  • Open-source
  • Community support
icon neptune.ai at a glance
  • Functionality:
    • experiment tracking
    • training monitoring
    • debugging
  • SaaS or deployed on your infra
  • Advanced UI
  • User access management
  • Collaboration features
  • Dedicated user support
  • Security and compliance (SOC 2)

Choose Neptune when bare-bones metadata management is holding you back

icon Maintenance

SaaS = Zero maintenance

It’s frustrating to spend your days dealing with storage & backups, managing user access, and setting up autoscaling for your servers. Not to mention the need to create new instances for every project.

Neptune’s SaaS solution lets you work on multiple projects & handles your backend automatically. So you can focus on managing your model development.

quote
MLflow requires what I like to call software kung fu, because you need to host it yourself. So you have to manage the entire infrastructure — sometimes it’s good, oftentimes it’s not.
Senior Data Scientist Healthcare Analytics Platform, UK
icon Team

Created for collaboration

The limitations of open-source software for access management and experiment sharing start to bite as soon as your team expands.

Packed with collaborative features — like reports, customizable workspaces and persistent shareable links — Neptune takes team management off your to-do list.

quote
We tried MLflow. But the problem is that they have no user management features, which messes up a lot of things.
AI/ML Product Manager Customer Service Automation Platform, USA
icon Interface

Debug your models faster with a flexible User Interface

Neptune allows you to compare all of your metadata in a clean, easy-to-navigate, and responsive User Interface. With searchable side-by-side run tables, parallel coordinates plots, and learning curve charts, Neptune makes it easy to analyze experiments.

quote
Neptune’s UI is highly configurable, which is way better than MLflow.
Chief Data Scientist HR Software Startup, Asia
icon Scalability

Will scale. Won’t fail.

Neptune won’t freeze up faced with large streams of logs running 1000s of experiments at once. And even when rendering complex charts to view your data Neptune will never slow you down.

quote
In MLflow, when I log a CSV file that’s about 10,000 rows, MLflow just stops working. I click on the CSV file, it may take maybe three minutes before it shows up, and even when it starts, it doesn’t work smoothly anymore. It’s totally unusable but that’s not a problem with Neptune.
Kha Nguyen Senior Data Scientist @ Zoined
See pricing Find out more about Neptune
Feature-by-feature comparison

Take a deep dive into
what makes Neptune different

Show differences only

Commercial offering

Commercial offering chevron
Open-source software or a managed cloud service?

Managed cloud service

Open-source

Pricing model

User based and usage based (ingestion data points)

Free

Guarantees around service levels (SLOs / SLAs)

Yes

Community support only

Support 24Ă—7

Yes

No

User access management (SSO, ACL)

Yes

Limited

Security policy and compliance

Yes

No

General information

Deployment chevron
Cloud (SaaS)

Yes

No. However, it’s available on a managed server as part of the Databricks platform.

Self-hosted (on-prem, private cloud)

Yes

Yes

Installation in air-gapped environment

Yes

Yes

Setup chevron
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 mlflow (for local tracking). Remote tracking server 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 Python, REST, R, Java, or CLI.

Flexibility and accessibility chevron
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, Update (limited), Delete, Download

Logging modes

Offline, Disabled/Off, Asynchronous

Offline, Disabled/off, Asynchronous, Synchronous

Customizable metadata structure

Yes

No

Distributed training support

Yes

Limited

Pipelining support

Yes

Yes

Live monitoring

Yes

Yes

Webhooks and notifications

No

No

Resuming experiments

Yes

Yes

Forking runs

Yes

No

Capabilities

Log and display chevron
Parameters

Yes

Yes

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

Yes

Yes

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

Yes

Yes

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

Yes

Yes

Tags

Yes

Yes

Descriptions/comments

Yes

Yes

Rich format

Images, Plots, Video, Audio

Plots

Hardware consumption

Yes

CPU, GPU, Memory

Dataset

No

Limited

Code versions

Git, Source

Git (limited), Source

System information

Console logs, Execution command

Execution command

Environment config

No

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

Files (model binaries, CSV)

Yes

Yes

External file reference (S3 buckets)

No

Yes

Searching & filtering chevron
Searching on multiple criteria

Yes

Yes

Query language and filtering options

Both regex and fixed selectors

Query language

Custom attribute filtering (e.g. tags)

Yes

Yes

Support for regular expressions

Yes

Limited

Auto-update charts based on regex

Yes

No

Saving searches and filter history

Yes

No

Visualizations chevron
Custom (calculated) metrics

Yes

No

Forked charts

Yes

No

Histograms

Yes

No

Custom axes

Yes

Limited

Customizable global settings

Yes

Limited

Customizable legends

Yes

No

Comparing experiments chevron
Table format diff

Yes

No

Single-metric overlayed plots

Yes

Yes

Multi-metric overlayed plots

Yes

No

Grouping experiments by metadata

Yes

Limited

Scatter plot

Yes

No

Parallel coordinates plot

No

Yes

Parameter importance plot

No

No

Rich format (side by side)

No

No

Data versions diff

No

No

Cross-project comparisons

Yes

N/A

Custom analysis chevron
Experiment table customization

Yes

Limited

Saving experiment table views

Yes

No

Dashboards combining different metadata types

Yes

No

Custom widgets and plugins

No

No

Persistent custom plots coloring

Yes

No

Collaboration and knowledge sharing chevron
Reports

Yes

No

Adding text in Reports

Yes

No

Commenting

Limited

Yes

Downloading charts

Yes

Yes

Sharing persistent UI links

Yes

Limited

User groups and ACL

Yes

Limited

This table was updated on 14 May 2025. Some information may be outdated.
Report outdated information here.
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quote
For now, I’m not using ML flow anymore ever since I switched to Neptune because I feel like Neptune is a super-set of what MLflow has to offer.
Kha Nguyen Senior Data Scientist @Zoined

Make it simple to scale your model development

Neptune is the lightweight solution for ML teams growing frustrated with MLflow’s limited functionality.

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

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