Compare Weights & Biases vs MLflow vs Neptune

The only affordable tool for ML metadata management at scale

Weights & Biases
vs
mlflow
vs
neptune-logo

When the number of runs you log at once grows in size, MLflow can break down.
When the number of folks on your team grows in size, WandB’s pricing can break your budget.

Avoid both these things, with Neptune.

Feature-by-feature comparison

Take a deep dive into the differences between WandB, MLflow and Neptune

Commercial Requirements

Commercial requirements chevron
Standalone component or a part of a broader ML platform?

Standalone component

Open-source platform which offers four separate components for experiment tracking, code packaging, model deployment, and model registry

Standalone component

Is the product available on-premises and / or in your private/public cloud?

Yes

Tracking is hosted on a local/remote server (on-prem or cloud). Is also available on a managed server as part of the Databricks platform

Is the product delivered as commercial software, open-source software, or a managed cloud service?

Managed cloud service

Open-source

Managed cloud service

Support: Does the vendor provide 24×7 support?

No

SSO, ACL: does the vendor provide user access management?

No

Security policy and compliance

Yes

No

Yes

General Capabilities

Setup chevron
What are the infrastructure requirements?

No special requirements other than having wandb python library installed and access to the internet if using managed hosting. Check here for infrastructure requirements for on-prem deployment.

No requirements other than having mlflow installed if using a local tracking server. Check here for infrastructure requirements for using a remote tracking server

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.

How much do you have to change in your training process?

Minimal. Just a few lines of code needed for tracking. Read more

Minimal. Just a few lines of code needed for traking. Read more

Minimal. Just a few lines of code needed for tracking. Read more

Does it integrate with the training process via CLI/YAML/Client library?

Yes, through PythonJava, and CLI

Yes, through the neptune-client library

Does it come with a web UI or is it console-based?
Serverless UI

No

Yes

No

Flexibility, speed, and accessibility chevron
Customizable metadata structure

Yes

No

Yes

How can you access model metadata?
– gRPC API

No

No

No

– CLI / custom API

Yes

Yes

Yes

– REST API

No

Yes

No

– Python SDK

Yes

Yes

Yes

– R SDK

No

Yes

Yes

– Java SDK

Yes

Yes

No

– Julia SDK

No

Supported operations
– Search

Yes

Yes

Yes

– Update

Yes

Yes

– Delete

Yes

Yes

Yes

– Download

Yes

Yes

Yes

Distributed training support

Yes

Yes

Pipelining support

Yes

Yes

Yes

Logging modes
– Offline

Yes

Yes

Yes

– Debug

No

No

Yes

– Asynchronous

Yes

Yes

Yes

– Synchronous

No

Yes

Yes

Live monitoring

Yes

Yes

Yes

Mobile support

No

No

No

Webhooks and notifications

Yes

No

No

Experiment Tracking

Log and display of metadata chevron
Dataset
– location (path/s3)

Yes

Yes

Yes

– hash (md5)

Yes

Yes

Yes

– Preview table

Yes

No

Yes

– Preview image

Yes

Yes

– Preview text

Yes

Yes

Yes

– Preview rich media

Yes

No

Yes

– Multifile support

Yes

Yes

Yes

Code versions
– Git

Yes

Yes

– Source

Yes

Yes

Yes

– Notebooks

Yes

Yes

Parameters

Yes

Yes

Yes

Metrics and losses
– Single values

Yes

Yes

Yes

– Series values

Yes

Yes

Yes

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

Yes

Yes

Yes

Tags

Yes

Yes

Yes

Descriptions/comments

Yes

Yes

Yes

Rich format
– Images (support for labels and descriptions)

No

Yes

– Plots

Yes

Yes

Yes

– Interactive visualizations (widgets and plugins)

Yes

No

Yes

– Video

Yes

No

Yes

– Audio

Yes

No

Yes

– Neural Network Histograms

Yes

No

No

– Prediction visualization (tabular)

Yes

No

No

– Prediction visualization (image)

No

No

– Prediction visualization (image – interactive confusion matrix for image classification)

Yes

No

No

– Prediction visualization (image – overlayed prediction masks for image segmentation)

NA

No

No

– Prediction visualization (image – overlayed prediction bounding boxes for object detection)

NA

No

No

Hardware consumption
– CPU

Yes

No

Yes

– GPU

Yes

No

Yes

– TPU

Yes

No

No

– Memory

Yes

No

Yes

System information
– Console logs (Stderr, Stdout)

Yes

No

Yes

– Error stack trace

No

No

Yes

– Execution command

Yes

Yes

No

– System details (host, user, hardware specs)

Yes

No

Yes

Environment config
– pip requirements.txt

Yes

Yes

Yes

– conda env.yml

No

Yes

Yes

– Docker Dockerfile

Yes

Yes

Yes

Files
– Model binaries

Yes

Yes

Yes

– CSV

Yes

Yes

Yes

– External file reference (s3 buckets)

Yes

Yes

Yes

Comparing experiments chevron
Table format diff

Yes

No

Yes

Overlayed learning curves

Yes

Yes

Yes

Parameters and metrics
– Groupby on experiment values (parameters)

Yes

No

Yes

– Parallel coordinates plots

Yes

Yes

Yes

– Parameter Importance plot

Yes

No

No

Rich format (side by side)
– Image

Yes

No

Yes

– Video

Yes

No

No

– Audio

Yes

No

No

– Plots

No

No

No

– Interactive visualization (HTML)

Yes

No

No

– Text

Yes

No

Yes

– Neural Network Histograms

No

No

No

– Prediction visualization (tabular)

Yes

Yes

Yes

– Prediction visualization (image, video, audio)

Yes

No

No

Code
– Git

No

No

No

– Source files

Yes

No

No

– Notebooks

Yes

No

Yes

Environment
– pip requirements.txt

No

No

No

– conda env.yml

No

No

No

– Docker Dockerfile

No

No

No

Hardware
– CPU

Yes

No

Yes

– GPU

Yes

No

Yes

– Memory

Yes

No

Yes

System information
– Console logs (Stderr, Stdout)

No

No

Yes

– Error stack trace

No

No

Yes

– Execution command

No

No

No

– System details (host, owner)

No

Yes

Yes

Data versions
– Location

No

No

Yes

– Hash

Yes

No

Yes

– Dataset diff

No

No

Yes

– External reference version diff (s3)

No

No

Yes

Files
– Models

No

No

No

– CSV

No

No

No

Custom compare dashboards
– Combining multiple metadata types (image, learning curve, hardware)

Yes

No

Yes

– Logging custom comparisons from notebooks/code

Yes

No

No

– Compare/diff of multiple (3+) experiments/runs

Yes

Yes

Yes

Organizing and searching experiments and metadata chevron
Experiment table customization
– Adding/removing columns

Yes

Yes

Yes

– Renaming columns in the UI

No

No

Yes

– Adding colors to columns

No

No

Yes

– Displaying aggregate (min/max/avg/var/last) for series like training metrics in a table

Yes

No

Yes

– Automagical column suggestion

Yes

No

Yes

Experiment filtering and searching
– Searching on multiple criteria

No

Yes

Yes

– Query language vs fixed selectors

Regex on names at the project level, fixed selectors at the run level

Query language

– Saving filters and search history

No

No

Yes

Custom dashboards for a single experiment
– Can combine different metadata types in one view

Yes

No

Yes

– Saving experiment table views

Yes

No

Yes

– Logging project-level metadata

Yes

No

Yes

– Custom widgets and plugins

Yes

No

No

Tagging and searching on tags

Yes

Yes

Yes

Nested metadata structure support in the UI

Yes

No

Yes

Reproducibility and traceability chevron
One-command experiment re-run

Yes

Yes

No

Experiment lineage
– List of datasets used downstream

Yes

No

– List of other artifacts (models) used downstream

Yes

No

– Downstream artifact dependency graph

Yes

Yes

No

Reproducibility protocol

Yes

Yes

Is environment versioned and reproducible

Yes

Yes

Yes

Saving/fetching/caching datasets for experiments

No

No

Collaboration and knowledge sharing chevron
Sharing UI links with project members

Yes

Yes

Sharing UI links with external people

Yes

Yes

Commenting

Yes

Yes

Interactive project-level reports

Yes

No

No

Model Registry

Model versioning chevron
Code versions (used for training)

Yes

Yes

Environment versions

No

Yes

Parameters

Yes

Yes

Yes

Dataset versions

Yes

No

Yes

Results (metrics, visualizations)

Yes

Yes

Yes

Explanations (SHAP, DALEX)

No

Yes

Model files (packaged models, model weights, pointers to artifact storage)

Yes

Yes

Yes

Model lineage and evaluation history chevron
Models/experiments created downstream

Yes

No

History of evaluation/testing runs

No

No

No

Support for continuous testing

No

No

No

Users who created a model or downstream experiments

No

No

No

Access control, model review, and promoting models chevron
Main stage transition tags (develop, stage, production)

Yes

Yes

Yes

Custom stage tags

Yes

No

No

Locking model version and downstream runs, experiments, and artifacts

No

No

No

Adding annotations/comments and approvals from the UI

No

Model compare (current vs challenger etc)

No

Yes

Compatibility audit (input/output schema)

No

Yes

No

Compliance audit (datasets used, creation process approvals, results/explanations approvals)

No

No

CI/CD/CT compatibility chevron
Webhooks

No

No

No

Model accessibility

No

Yes

No

Support for continuous testing

No

No

No

Integrations with CI/CD tools

No

No

Model searching chevron
Registered models

Yes

Yes

Yes

Active models

No

Yes

Yes

By metadata/artifacts used to create it

No

No

Yes

By date

No

No

Yes

By user/owner

No

No

Yes

By production stage

No

No

Yes

Search query language

No

No

Yes

Model packaging chevron
Native packaging system

No

Yes

No

Compatibility with packaging protocols (ONNX, etc)

No

Yes

No

One model one file or flexible structure

No

No

Integrations with packaging frameworks

No

Yes

No

Integrations and Support

Languages chevron
Java

No

Yes

No

Julia

Yes

No

Python

Yes

Yes

Yes

R

No

Yes

REST API

No

Yes

No

Model training chevron
Catalyst

Yes

Yes

Yes

CatBoost

No

Yes

No

fastai

Yes

Yes

Yes

FBProphet

No

Yes

Yes

Gluon

No

Yes

No

HuggingFace

Yes

Yes

Yes

H2O

No

Yes

No

LightGBM

Yes

Yes

Yes

Paddle

Yes

Yes

No

PyTorch

Yes

Yes

Yes

PyTorch Ignite

Yes

Yes

Yes

PyTorch Lightning

Yes

Yes

Yes

Scikit Learn

Yes

Yes

Yes

Skorch

Yes

Yes

Yes

Spacy

Yes

Yes

No

Spark MLlib

No

Yes

No

Statsmodel

No

Yes

No

TesorFlow / Keras

Yes

Yes

Yes

XGBoost

Yes

Yes

Yes

Hyperparameter Optimization chevron
Hyperopt

No

No

No

Keras Tuner

Yes

No

Optuna

Yes

Yes

Yes

Ray Tune

Yes

Yes

No

Scikit-Optimize

No

No

Model visualization and debugging chevron
DALEX

No

No

Yes

Netron

No

No

No

SHAP

No

Yes

No

TensorBoard

Yes

Yes

No

IDEs and Notebooks chevron
JupyterLab and Jupyter Notebook

Yes

Yes

Google Colab

Yes

Yes

Deepnote

Yes

Yes

AWS SageMaker

Yes

No

Yes

Data versioning chevron
DVC

No

No