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Compare MLflow vs TensorBoard vs Neptune

For when you want to manage ML metadata at scale

mlflow
vs
TensorBoard
vs
neptune-logo

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.

Show me pricing Show me case studies
Feature-by-feature comparison

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

Commercial Requirements

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

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

Free individual and paid team/enterprise plans available

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

Community support only

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?

No

No

Only for enterprise customers

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

No

No

Only for enterprise customers

Security policy and compliance

No

No

Only for enterprise customers

General Capabilities

Setup chevron
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

Web UI

Web UI

Serverless UI

Yes

Yes

No

Flexibility, speed, and accessibility chevron
Customizable metadata structure

No

No

Yes

How can you access model metadata?
– gRPC API

No

No

No

– CLI / custom API

Yes

No

Yes

– REST API

Yes

No

No

– Python SDK

Yes

Yes

Yes

– R SDK

Yes

Limited

Yes

– Java SDK

Yes

Limited

No

– Julia SDK

No

No

No

Supported operations
– Search

Yes

Limited

Yes

– Update

Limited

No

Yes

– Delete

Yes

Yes

Yes

– Download

Yes

Yes

Yes

Distributed training support

Limited

Yes

Yes

Pipelining support

Yes

No

Yes

Logging modes
– Offline

Yes

Yes

Yes

– Debug

No

Limited

Yes

– Asynchronous

Yes

Yes

Yes

– Synchronous

Limited

No

Yes

Live monitoring

Yes

Yes

Yes

Mobile support

No

No

No

Webhooks and notifications

No

No

No

Experiment Tracking

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

Yes

No

Yes

– hash (md5)

Yes

No

Yes

– Preview table

No

No

Yes

– Preview image

Limited

Limited (requires converting the image to tf.summary.image format)

Yes

– Preview text

Yes

Yes (requires converting the text to tf.summary.text format)

Yes

– Preview rich media

No

Limited (requires converting supported media-types using tf.summary)

Yes

– Multifile support

Yes

No

Yes

– Dataset slicing support

No

No

No

Code versions

No

– Git

Only the commit hash or branch name

No

Yes

– Source

Yes

No

Yes

– Notebooks

Limited

No

Yes

Parameters

Yes

No

Yes

Metrics and losses
– Single values

Yes

Yes

Yes

– Series values

Yes

Yes

Yes

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

Yes

No

Yes

Tags

Yes

Yes

Yes

Descriptions/comments

Yes

Only with TensorBoard.dev

Yes

Rich format
– Images (support for labels and descriptions)

No

No

Yes

– Plots

Yes

Yes

Yes

– Interactive visualizations (widgets and plugins)

No

No

Yes

– Video

No

No

Yes

– Audio

No

Yes (requires conversion using tf.summary.audio)

Yes

– Neural Network Histograms

No

Yes

No

– Prediction visualization (tabular)

No

No

No

– Prediction visualization (image)

No

No

No

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

No

NA

No

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

No

NA

No

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

No

NA

No

Hardware consumption
– CPU

No

No

Yes

– GPU

No

No

Yes

– TPU

No

No

No

– Memory

No

No

Yes

System information
– Console logs (Stderr, Stdout)

No

No

Yes

– Error stack trace

No

No

Yes

– Execution command

Yes

No

No

– System details (host, user, hardware specs)

No

No

Yes

Environment config
– pip requirements.txt

Yes

No

Yes

– conda env.yml

Yes

No

Yes

– Docker Dockerfile

Yes

No

Yes

Files
– Model binaries

Yes

No

Yes

– CSV

Yes

No

Yes

– Any file

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

No

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

– External file reference (s3 buckets)

Yes

No

Yes

Comparing experiments chevron
Table format diff

No

No

Yes

Overlayed learning curves

Yes

Yes

Yes

Parameters and metrics
– Groupby on experiment values (parameters)

No

No

Yes

– Parallel coordinates plots

No

Yes (requires TensorFlow and the TensorBoard HParams plugin)

Yes

– Parameter Importance plot

No

No

No

– Slice plot

No

No

No

– EDF plot

No

No

No

Rich format (side by side)
– Image

No

No

Yes

– Video

No

No

No

– Audio

No

No

No

– Plots

No

No

No

– Interactive visualization (HTML)

No

No

No

– Text

No

No

Yes

– Neural Network Histograms

No

Yes (requires TensorFlow installed)

No

– Prediction visualization (tabular)

Yes

No

Yes

– Prediction visualization (image, video, audio)

No

No

No

Code
– Git

No

No

No

– Source files

No

No

No

– Notebooks

No

No

Yes

Environment
– pip requirements.txt

No

No

No

– conda env.yml

No

No

No

– Docker Dockerfile

No

No

No

Hardware
– CPU

No

No

Yes

– GPU

No

No

Yes

– Memory

No

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)

Yes

No

Yes

Data versions
– Location

No

No

Yes

– Hash

No

No

Yes

– Dataset diff

No

No

Yes

– External reference version diff (s3)

No

No

No

Files
– Models

No

No

No

– CSV

No

No

No

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

No

No

Yes

– Logging custom comparisons from notebooks/code

No

No

No

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

Yes

No

Yes

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

Yes

No

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

No

No

Yes

– Automagical column suggestion

No

No

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

No

No

Yes

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

No

No

Yes

– Saving experiment table views

No

No

Yes

– Logging project-level metadata

No

No

Yes

– Custom widgets and plugins

No

No

No

Tagging and searching on tags

Yes

Yes

Yes

Nested metadata structure support in the UI

No

No

Yes

Reproducibility and traceability chevron
One-command experiment re-run

Yes

No

No

Experiment lineage
– List of datasets used downstream

Limited

No

No

– List of other artifacts (models) used downstream

Limited

No

No

– Downstream artifact dependency graph

Yes

No

No

Reproducibility protocol

Yes

No

Limited

Is environment versioned and reproducible

Yes

No

Yes

Saving/fetching/caching datasets for experiments

No

No

No

Collaboration and knowledge sharing chevron
User groups and ACL

No

No

Only for Teams and Enterprise customers

Sharing UI links with project members

No

Only in TensorBoard.dev

Yes

Sharing UI links with external people

No

Only in TensorBoard.dev

Yes

Commenting

Yes

No

Limited

Interactive project-level reports

No

No

No

Model Registry

Model versioning chevron
Code versions (used for training)

Limited to git hash

No

Yes

Environment versions

Yes

No

No

Parameters

Yes

No

Yes

Dataset versions

No

No

Yes

Results (metrics, visualizations)

Yes

No

Yes

Explanations (SHAP, DALEX)

Yes

No

Limited

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

Yes

No

Yes

Model lineage and evaluation history chevron
Models/experiments created downstream

No

No

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

No

Yes

Custom stage tags

No

No

No

Locking model version and downstream runs, experiments, and artifacts

No

No

No

Adding annotations/comments and approvals from the UI

Limited to annotations

No

Limited

Model compare (current vs challenger etc)

Yes

No

Limited

Compatibility audit (input/output schema)

Yes

No

No

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

No

No

No

CI/CD/CT compatibility chevron
Webhooks

No

No

No

Model accessibility

Yes

No

No

Support for continuous testing

No

No

No

Integrations with CI/CD tools

No

No

No

Model searching chevron
Registered models

Yes

No

Yes

Active models

No

No

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

Yes

No

No

Compatibility with packaging protocols (ONNX, etc)

Yes

No

No

One model one file or flexible structure

Fixed directory structure

No

No

Integrations with packaging frameworks

Yes

Yes

No

Integrations and Support

Languages chevron
Java

Yes

No

No

Julia

Yes

No

No

Python

Yes

Yes

Yes

R

Yes

Limited

Limited

REST API

Yes

No

No

Model training chevron
Catalyst

Yes

Yes

Yes

CatBoost

Yes

Yes

No

fastai

Yes

Yes

Yes

FBProphet

Yes

No

Yes

Gluon

Yes

Yes

No

HuggingFace

Yes

Yes

Yes

H2O

Yes

Yes

No

LightGBM

Yes

No

Yes

Paddle

Yes

No

No

PyTorch

Yes

Yes

Yes

PyTorch Ignite

Yes

Yes

Limited

PyTorch Lightning

Yes

Yes

Yes

Scikit Learn

Yes

No

Yes

Skorch

Yes

Yes

Yes

Spacy

Yes

No

No

Spark MLlib

Yes

No

No

Statsmodel

Yes

No

No

TesorFlow / Keras

Yes

Yes

Yes

XGBoost

Yes

No

Yes

Hyperparameter Optimization chevron
Hyperopt

No

No

No

Keras Tuner

No

No

Limited

Optuna

Yes

Yes

Yes

Ray Tune

Yes

Yes

No

Scikit-Optimize

No

No

Limited

Model visualization and debugging chevron
DALEX

No

No

Limited

Netron

No

Limited

No

SHAP

Yes

No

No

TensorBoard

Yes

NA

Limited

IDEs and Notebooks chevron
JupyterLab and Jupyter Notebook

Limited

Yes

Yes

Google Colab

Limited

Yes

Yes

Deepnote

Limited

Yes

Yes

AWS SageMaker

No

Yes

Yes

Data versioning chevron
DVC

No

No

Yes

Orchestration and pipelining chevron
Airflow

No

No

No

Argo

No

Yes

No

Kedro

Yes

No

Yes

Kubeflow

No

Yes

No

ZenML

Yes

Yes

Yes

Experiment tracking tools chevron
MLflow

NA

No

Limited

Sacred

No

No

Yes

TensorBoard

No

NA