Compare Weights & Biases vs MLflow vs Neptune

The only affordable tool for ML metadata management at scale

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.

Show me pricing Show me case studies

Feature-by-feature comparison

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

Weights & Biases

MLflow

Neptune

Commercial Requirements

Commercial requirements

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. ML metadata store that focuses on experiment tracking and model registry

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

Tracking is hosted on a managed server, and can also be deployed on-premises and in a public/private cloud server

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

Managed cloud service

Open-source

Managed cloud service

What is the pricing model?

Free plan for individuals and small teams, paid plans for larger teams and enterprises

Free

Free individual and paid team/enterprise plans available

SLOs / SLAs: Does the vendor provide guarantees around service levels?

Only for the “enterprise” plan

Community support only

Email and chat support for individuals and teams. Well defined support SLAs for enterprise customers

Support: Does the vendor provide 24×7 support?

Only for the “enterprise” plan

Only for enterprise customers

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

Only for the “enterprise” plan

Only for enterprise customers

Security policy and compliance

Yes

Only for enterprise customers

General Capabilities

Setup

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

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

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 Python, Java, and CLI

Yes, they have SDK clients in various languages, and a CLI

Yes, through the neptune-client library

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

Web UI

Serverless UI

Yes

Flexibility, speed, and accessibility

Customizable metadata structure

Yes

How can you access model metadata?

– gRPC API

– CLI / custom API

– REST API

– Python SDK

– R SDK

– Java SDK

– Julia SDK

Supported operations

– Search

– Update

– Delete

– Download

Distributed training support

Pipelining support

Logging modes

– Offline

– Debug

– Asynchronous

– Synchronous

Live monitoring

Yes

Mobile support

Webhooks and notifications

Yes

Experiment Tracking

Log and display of metadata

Dataset

– location (path/s3)

– hash (md5)

– Preview table

– Preview image

– Preview text

– Preview rich media

– Multifile support

– Dataset slicing support

Code versions

– Git

– Source

– Notebooks

Parameters

Metrics and losses

– Single values

Yes

– Series values

Yes

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

Yes

Tags

Yes

Descriptions/comments

Yes

Rich format

– Images (support for labels and descriptions)

Limited

Yes

– Plots

Yes

– Interactive visualizations (widgets and plugins)

Yes

– Video

Yes

– Audio

Yes

– Neural Network Histograms

Yes

– Prediction visualization (tabular)

Yes

– Prediction visualization (image)

Limited

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

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

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

Hardware consumption

– CPU

– GPU

– TPU

– Memory

System information

– Console logs (Stderr, Stdout)

Yes

– Error stack trace

Yes

– Execution command

Yes

– System details (host, user, hardware specs)

Yes

Environment config

– pip requirements.txt

Yes

– conda env.yml

Yes

– Docker Dockerfile

Yes

Files

– Model binaries

Yes

– CSV

Limited

Yes

– Any file

All files can be logged, but some might not be rendered

All files can be logged, but some formats might not be rendered

– External file reference (s3 buckets)

Yes

Comparing experiments

Table format diff

Yes

Overlayed learning curves

Yes

Parameters and metrics

– Groupby on experiment values (parameters)

Yes

Available on the databricks managed version

Yes

– Parallel coordinates plots

Yes

– Parameter Importance plot

Yes

– Slice plot

– EDF plot

Rich format (side by side)

– Image

Yes

– Video

Yes

– Audio

Yes

– Plots

– Interactive visualization (HTML)

Yes

– Text

Yes

– Neural Network Histograms

– Prediction visualization (tabular)

Yes

– Prediction visualization (image, video, audio)

Yes

Code

– Git

– Source files

Yes

– Notebooks

Yes

Environment

– pip requirements.txt

– conda env.yml

– Docker Dockerfile

Hardware

– CPU

Yes

– GPU

Yes

– Memory

Yes

System information

– Console logs (Stderr, Stdout)

Yes

– Error stack trace

Yes

– Execution command

– System details (host, owner)

Yes

Data versions

– Location

Yes

– Hash

Yes

– Dataset diff

Yes

– External reference version diff (s3)

Files

– Models

– CSV

Custom compare dashboards

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

Yes

– Logging custom comparisons from notebooks/code

Yes

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

Yes

Organizing and searching experiments and metadata

Experiment table customization

– Adding/removing columns

Yes

– Renaming columns in the UI

Yes

– Adding colors to columns

Yes

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

Yes

– Automagical column suggestion

Yes

Experiment filtering and searching

– Searching on multiple criteria

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

Yes

Custom dashboards for a single experiment

– Can combine different metadata types in one view

Yes

– Saving experiment table views

Yes

– Logging project-level metadata

Yes

– Custom widgets and plugins

Yes

Tagging and searching on tags

Yes

Nested metadata structure support in the UI

Yes

Reproducibility and traceability

One-command experiment re-run

Yes

Experiment lineage

– List of datasets used downstream

Yes

– List of other artifacts (models) used downstream

Yes

– Downstream artifact dependency graph

Yes

Reproducibility protocol

Yes

Limited

Is environment versioned and reproducible

Yes

Saving/fetching/caching datasets for experiments

Limited

Collaboration and knowledge sharing

User groups and ACL

Only for the “advanced” pricing model

Only for Teams and Enterprise customers

Sharing UI links with project members

Yes

Sharing UI links with external people

Yes

Commenting

Yes

Interactive project-level reports

Yes

Model Registry

Model versioning

Code versions (used for training)

Environment versions

Parameters

Dataset versions

Results (metrics, visualizations)

Yes

Explanations (SHAP, DALEX)

Yes

Limited

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

Yes

Model lineage and evaluation history

Models/experiments created downstream

Yes

History of evaluation/testing runs

Support for continuous testing

Users who created a model or downstream experiments

Access control, model review, and promoting models

Main stage transition tags (develop, stage, production)

Yes

Custom stage tags

Yes

Locking model version and downstream runs, experiments, and artifacts

Adding annotations/comments and approvals from the UI

Limited

Model compare (current vs challenger etc)

Yes

Limited

Compatibility audit (input/output schema)

Yes

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

CI/CD/CT compatibility

Webhooks

Model accessibility

Yes

Support for continuous testing

Integrations with CI/CD tools

Model searching

Registered models

Yes

Active models

Yes

By metadata/artifacts used to create it

By date

By user/owner

By production stage

Search query language

Yes

Model packaging

Native packaging system

Yes

Compatibility with packaging protocols (ONNX, etc)

Yes

One model one file or flexible structure

Fixed directory structure

Integrations with packaging frameworks

Yes

Integrations and Support

Languages

Java

Julia

Python

REST API

Model training

Catalyst

CatBoost

fastai

FBProphet

Gluon

HuggingFace

H2O

LightGBM

Paddle

PyTorch

PyTorch Ignite

PyTorch Lightning

Scikit Learn

Skorch

Spacy

Spark MLlib

Statsmodel

TesorFlow / Keras

XGBoost

Hyperparameter Optimization

Hyperopt

Keras Tuner

Optuna

Ray Tune

Scikit-Optimize

Model visualization and debugging

DALEX

Limited

Netron

SHAP

Yes

TensorBoard

Yes

Limited

IDEs and Notebooks

JupyterLab and Jupyter Notebook

Google Colab

Deepnote

AWS SageMaker

Data versioning

DVC

Yes

Orchestration and pipelining

Airflow

Argo

Kedro

Yes

Kubeflow

Yes

ZenML

Yes

Experiment tracking tools

MLflow