Compare Weight & Biases vs TensorBoard vs Neptune

The only experiment tracker for large scale model development

TensorBoard is great for basic experiment tracking. But for advanced metadata management (that doesn’t cost an arm and a leg)? Choose Neptune.

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Feature-by-feature comparison

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

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Weights & Biases

TensorBoard

Neptune

Commercial offering

Open-source software or a managed cloud service?

Managed cloud service

Open-source

Managed cloud service

Pricing model

User based and usage based (tracked hours)

Free

User based and usage based (ingestion data points)

Guarantees around service levels (SLOs / SLAs)

Yes

Community support only

Yes

Support 24×7

Yes

User access management (SSO, ACL)

Yes

Security policy and compliance

Yes

General information

Deployment

Cloud (SaaS)

Yes

Self-hosted (on-prem, private cloud)

Yes

Installation in air-gapped environment

Yes

Setup

Infrastructure requirements

Minimal setup—install wandb python library and ensure internet access (for managed hosting). Self-hosting requires additional infrastructure; see requirements here.

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

Minimal setup—install the Python client and ensure internet access (for managed hosting). Self-hosting requires additional infrastructure; see requirements here.

Integration with the training process

A few lines of code via Python, JavaScript, or CLI.

A few lines of code via client library and CLI.

A few lines of code via the Neptune Python library.

Flexibility and accessibility

Accessing model metadata

CLI/custom API, Python SDK, Java SDK, Julia SDK

Python SDK, R SDK (limited), Julia SDK (limited)

CLI/custom API and Python SDK

Supported operations

Search, Update, Delete, Download

Search, Delete, Download

Search, Update, Delete, Download

Logging modes

Offline, Disabled/off, Asynchronous, Synchronous

Offline, Asynchronous

Offline, Disabled/Off, Asynchronous

Customizable metadata structure

Yes

Distributed training support

Yes

Pipelining support

Yes

Live monitoring

Yes

Webhooks and notifications

Resuming experiments

Limited

Forking runs

Capabilities

Log and display

Parameters

Yes

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

Yes

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

Yes

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

Yes

Tags

Yes

Descriptions/comments

Yes

Limited

Yes

Rich format

Images, Plots, Interactive visualizations, Video, Audio

Plots, Audio

Images, Plots, Video, Audio

Hardware consumption

CPU, GPU, TPU, Memory

Yes

Dataset

Yes

Limited

Code versions

Git, Source, Notebooks

Git, Source

System information

Console logs, Error stack trace, Execution command, System details

Console logs, Execution command

Environment config

pip requirements.txt, Docker Dockerfile

Files (model binaries, CSV)

Yes

External file reference (S3 buckets)

Yes

Searching & filtering

Searching on multiple criteria

Yes

Basic filtering in the UI

Yes

Query language and filtering options

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

Regex with limited query language

Both regex and fixed selectors

Custom attribute filtering (e.g. tags)

Yes

Support for regular expressions

Yes

Auto-update charts based on regex

Yes

Saving searches and filter history

Yes

Visualizations

Custom (calculated) metrics

Yes

Forked charts

Yes

Histograms

Yes

Custom axes

Yes

Limited

Yes

Customizable global settings

Yes

Limited

Yes

Customizable legends

Yes

Comparing experiments

Table format diff

Yes

Single-metric overlayed plots

Yes

Multi-metric overlayed plots

Yes

Grouping experiments by metadata

Yes

Scatter plot

Yes

Yes, through embeding projector

Yes

Parallel coordinates plot

Yes

Parameter importance plot

Yes

Rich format (side by side)

Images, Audio, Video, Interactive visualizations, Text

Data versions diff

Cross-project comparisons

Yes

N/A

Yes

Custom analysis

Experiment table customization

Limited

Yes

Saving experiment table views

Yes

Dashboards combining different metadata types

Yes

Custom widgets and plugins

Yes

Persistent custom plots coloring

Yes

Collaboration and knowledge sharing

Reports

Yes

Adding text in Reports

Yes

Commenting

Yes

Limited

Downloading charts

Yes

Sharing persistent UI links

Yes

User groups and ACL

Yes

This table was updated on 14 May 2025. Some information may be outdated.
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When I’m doing hyperparameter optimization and I am running a lot of experiments TensorBoard gets very cluttered. It’s hard to organize and compare things. Neptune UI is very clear, it’s intuitive, and scales with many runs.

Ihab Bendidi Biomedical AI Researcher

The way we work is that we do not experiment constantly. After checking out both Neptune and Weights and Biases, Neptune made sense to us due to its pay-per-use or usage-based pricing. Now when we are doing active experiments then we can scale up and when we’re busy integrating all our models for a few months we scale down again.

Viet Yen Nguyen CTO at Hypefactors

I’d say the advantage (of TensorBoard) is that it’s free and it works pretty well, but anytime an engineer wanted to show the team some training curve, they’d need to start the VM (Virtual Machine) containing the logs, or make their localhost port available, expose it to the internet, it was not very secure… When you end up having to start a VM just to visualize some logs, you realize there should be a better tool.

Nicolas Lopez Carranza DeepChain and BioAI Lead at InstaDeep

I chose Neptune over WandB because it is more lightweight and I’m more comfortable working with it.

Jonathan Donzallaz Data Science Researche