Machine Learning · head to head
TensorBoard vs Weights & Biases
TensorBoard
Machine Learning
Local visualisation for training runs, reading event files written to a directory
- From
- Free
- Rated
- -
Weights & Biases
Machine Learning
Developer tools for machine learning
- From
- Free
- Rated
- -
The short version
- Each has a real cost: TensorBoard there is no authentication of any kind, so putting it on a shared host exposes every run, every metric and every logged sample image to anyone who can reach the port, and adding access control means building and maintaining a reverse proxy.; Weights & Biases pricing can be prohibitive for large teams without enterprise discounts
- They diverge on capability: TensorBoard covers Scalar dashboards, Weights & Biases covers Experiment tracking.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which TensorBoard and Weights & Biases actually diverge.
| Attribute | TensorBoard | Weights & Biases |
|---|---|---|
| Pricing model | open-source | Unknown |
| Platforms | Web | Web, Python SDK, REST API |
| Founded | Unknown | 2017 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in TensorBoard
- Scalar dashboards
- Run comparison
- Graph visualisation
- Histograms and distributions
- Embedding projector
- Image, audio and text panels
- Hyperparameter view
- Profiler
Only in Weights & Biases
- Experiment tracking
- Dataset versioning
- Model registry
- Hyperparameter sweeps
- Collaborative dashboards
- PyTorch
- TensorFlow
- Keras
What people use each for
The jobs each tool is most often brought in to do.
TensorBoard
- Watching a training run in progress on a workstation or a remote box, to decide whether to stop it earlynot Weights & Biases
- Diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curvenot Weights & Biases
- Profiling a slow training loop to find out whether the bottleneck is the data pipeline or the acceleratornot Weights & Biases
- Working in an environment with no outbound network access, where a hosted tracking service is not an optionnot Weights & Biases
Weights & Biases
- Machine learningnot TensorBoard
- Data analysisnot TensorBoard
- Model trainingnot TensorBoard
- Predictive analyticsnot TensorBoard
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
TensorBoard
- There is no authentication of any kind, so putting it on a shared host exposes every run, every metric and every logged sample image to anyone who can reach the port, and adding access control means building and maintaining a reverse proxy.
- Event files grow without limit and the interface loads runs into memory, so a directory holding hundreds of runs or a script logging scalars every step becomes slow to start and unpleasant to navigate well before the disk fills.
- It shows only what the training loop chose to write, so nothing connects a curve back to the code commit, the data set version or the environment unless the engineer logged those explicitly, which means the reproducibility problem is left entirely to you.
- TensorBoard.dev, the hosted service for sharing a run by link, was shut down at the end of 2023, so results now travel between colleagues as screenshots or through a deployment somebody on the team has to operate.
- Comparing runs is done by ticking boxes in a run list, which is fine for ten runs and useless for a thousand, and that threshold is precisely where teams start paying for MLflow, Weights and Biases or Neptune instead.
Weights & Biases
- Pricing can be prohibitive for large teams without enterprise discounts
- Limited integrations compared to some competitors
- Dashboard customization options limited on lower plans
- Requires some setup and configuration knowledge
Pricing, plan by plan
TensorBoard
FreeNo published plan breakdown. See the TensorBoard review.
Weights & Biases
Free- FreeFree
- 5 model seats
- 5 GB storage
- 1 GB/month Weave ingestion
- Pro$60/month
- 10 seats
- 100 GB storage
- Private projects
- Teams$179/month
- Team collaboration
- Advanced analytics
- Dedicated support
Which should you pick?
Choose TensorBoard if
- You need scalar dashboards.
- You want to start without paying.
- You also want run comparison.
Choose Weights & Biases if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python SDK, REST API.
- You also want dataset versioning.
Questions people ask
- Is TensorBoard or Weights & Biases better?
- Neither clearly leads. TensorBoard starts at Free and Weights & Biases at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, TensorBoard or Weights & Biases?
- TensorBoard starts at Free and Weights & Biases at Free.
- Does TensorBoard or Weights & Biases run on more platforms?
- TensorBoard runs on Web. Weights & Biases runs on Web, Python SDK, REST API.
- Can I use TensorBoard for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is TensorBoard best used for?
- TensorBoard is most often used for watching a training run in progress on a workstation or a remote box, to decide whether to stop it early, diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curve, profiling a slow training loop to find out whether the bottleneck is the data pipeline or the accelerator, working in an environment with no outbound network access, where a hosted tracking service is not an option. Of those, watching a training run in progress on a workstation or a remote box, to decide whether to stop it early and diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curve are not what Weights & Biases is typically brought in for.
- What can TensorBoard do that Weights & Biases cannot?
- TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions. Weights & Biases covers Experiment tracking, Dataset versioning, Model registry, Hyperparameter sweeps.
Answered from the vendors’ own pages
TensorBoard: Does it work with PyTorch?
Yes. PyTorch includes a SummaryWriter that emits the same event file format, and Lightning wires it up by default. Nothing about the tool requires TensorFlow at run time.
Weights & Biases: Does Weights & Biases have a free plan?
Yes. The Free tier includes 5 model seats, 5 GB storage, and 1 GB/month Weave ingestion. Academic users get unlimited tracked hours, 200 GB storage, and 100 seats at no cost.
SourceTensorBoard: Do I have to install TensorFlow to use it?
No. The tensorboard package installs on its own. Some plugins expect TensorFlow to be present, but the scalar, histogram and image dashboards do not.
Weights & Biases: What are the paid plans for Weights & Biases?
Pro starts at $60/month with 10 seats and 100 GB storage. Team plans start at $179/month. Enterprise pricing is custom.
SourceTensorBoard: Is it an experiment tracker?
No, and treating it as one is the common mistake. It visualises whatever a run wrote to disk. It does not store hyperparameters, code versions, artefacts or results in a way that survives someone deleting the log directory.
Weights & Biases: What machine learning features does W&B provide?
Weights & Biases captures hyperparameters, metrics, and model outputs automatically. Features include experiment tracking, interactive Reports for sharing findings, Artifacts for managing datasets and models, advanced hyperparameter sweeps, and model deployment tools.
SourceTensorBoard: How do I share a dashboard with a colleague?
Host it yourself behind your own authentication, or send screenshots. The hosted sharing service was retired at the end of 2023.
TensorBoard: What does it cost?
Nothing. It is Apache 2.0 licensed and runs on your own machine.
Related pages
More on TensorBoard
More on Weights & Biases
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