Compare MLflow vs TensorBoard vs Neptune

For when you want to manage ML metadata at scale

Your experiments have grown to 100s of runs. Your team has grown too.
Sluggish performance at runtime and clumsy workarounds for collaboration
are slowing you down. It’s time you tried Neptune.

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Feature-by-feature comparison

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

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MLflow

TensorBoard

Neptune

Commercial Requirements

Commercial requirements

Standalone component or a part of a broader ML platform?

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

Open source tool which is a part of the TensorFlow ecosystem

Standalone component

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

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

TensorBoard is hosted locally. No
TensorBoard.dev is available on a managed server as a free service

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?

Open-source

TensorBoard is open-source, while TensorBoard.dev is available as a free managed cloud service

Managed cloud service

What is the pricing model?

Free

Free individual and paid team/enterprise plans available

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

Limited

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 enterprise customers

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

Only for enterprise customers

Security policy and compliance

Yes

General Capabilities

Setup

What are the infrastructure requirements?

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

Basic logging can be done by having just TensorBoard installed. However, most advanced logging also requires TensorFlow to be installed

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 traking. Read more

Minimal if already using the TensorFlow framework, else significant

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

TensorBoard is available both as a client library and CLI. TensorBoard .dev is available only as 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

Limited

Yes

Pipelining support

Yes

Logging modes

– Offline

Yes

– Disabled/off

Yes

– Asynchronous

Yes

– Synchronous

Yes

Live monitoring

Yes

Mobile support

Webhooks and notifications

Experiment Tracking

Log and display of metadata

Dataset

– location (path/s3)

– hash (md5)

– Preview table

– Preview image

– Preview text

– Preview rich media

– Multifile 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

Only with TensorBoard.dev

Yes

Rich format

– Images (support for labels and descriptions)

Yes

– Plots

Yes

– Interactive visualizations (widgets and plugins)

Yes

– Video

Yes

– Audio

Yes

– Neural Network Histograms

Yes

– Prediction visualization (tabular)

– Prediction visualization (image)

– 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

Yes

– GPU

Yes

– TPU

– Memory

Yes

System information

– Console logs (Stderr, Stdout)

Yes

– Error stack trace

Yes

– Execution command

Yes

– System details (host, user, hardware specs)

Limited

Yes

Environment config

– pip requirements.txt

Yes

– conda env.yml

Yes

– Docker Dockerfile

Yes

Files

– Model binaries

Yes

– CSV

Yes

– Any file

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

– Parallel coordinates plots

Yes

– Parameter Importance plot

Rich format (side by side)

– Image

Yes

– Video

– Audio

– Plots

– Interactive visualization (HTML)

– Text

Yes

– Neural Network Histograms

– Prediction visualization (tabular)

Yes

– Prediction visualization (image, video, audio)

Code

– Git

– Source files

– 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)

Yes

Files

– Models

– CSV

Custom compare dashboards

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

Yes

– Logging custom comparisons from notebooks/code

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

Yes

Limited

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

Limited

Yes

– Query language vs fixed selectors

Query language

Regex with limited query language on the TensorBoard.dev experiments homepage

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

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

Limited

– List of other artifacts (models) used downstream

Limited

– Downstream artifact dependency graph

Yes

Reproducibility protocol

Yes

Limited

Is environment versioned and reproducible

Yes

Saving/fetching/caching datasets for experiments

Collaboration and knowledge sharing

User groups and ACL

Limited

Only in paid plans

Sharing UI links with project members

Limited

Only in TensorBoard.dev

Yes

Sharing UI links with external people

Limited

Only in TensorBoard.dev

Yes

Commenting

Yes

Limited

Interactive project-level reports

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

Limited

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

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

Limited

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

How Brainly avoids workflow bottlenecks with automated tracking

How Neptune gave Waabi organization-wide visibility on experiment data

How Elevatus uses Neptune to check experiment results in under 1 minute

Building a Machine Learning Platform

Learnings From Building the ML Platform at Mailchimp

How Brainly avoids workflow bottlenecks with automated tracking

How Neptune gave Waabi organization-wide visibility on experiment data

How Elevatus uses Neptune to check experiment results in under 1 minute

Building a Machine Learning Platform

Learnings From Building the ML Platform at Mailchimp

For when you want to manage ML metadata at scale

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

MLflow

TensorBoard

Neptune

Commercial Requirements

General Capabilities

Experiment Tracking

Model Registry

Integrations and Support