Machine Learning · head to head
Ray vs TensorBoard
TensorBoard
Machine Learning
Local visualisation for training runs, reading event files written to a directory
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; 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.
- They diverge on capability: Ray covers Distributed computing, TensorBoard covers Scalar dashboards.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Ray and TensorBoard actually diverge.
| Attribute | Ray | TensorBoard |
|---|---|---|
| Pricing model | freemium | open-source |
| Platforms | Linux, Mac, Windows | Web |
| Founded | 2019 | Unknown |
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 Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- PyTorch
- TensorFlow
- Hugging Face
Only in TensorBoard
- Scalar dashboards
- Run comparison
- Graph visualisation
- Histograms and distributions
- Embedding projector
- Image, audio and text panels
- Hyperparameter view
- Profiler
What people use each for
The jobs each tool is most often brought in to do.
Ray
- Distributed AI model training and servingnot TensorBoard
- Large-scale data processingnot TensorBoard
- Reinforcement learning workloadsnot TensorBoard
- ML inference servingnot TensorBoard
TensorBoard
- Watching a training run in progress on a workstation or a remote box, to decide whether to stop it earlynot Ray
- Diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curvenot Ray
- Profiling a slow training loop to find out whether the bottleneck is the data pipeline or the acceleratornot Ray
- Working in an environment with no outbound network access, where a hosted tracking service is not an optionnot Ray
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Ray
- Windows support is beta and multi node Ray clusters are untested on Windows
- Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
- Multi node clusters are untested on Apple Silicon Macs
- The Java API is experimental and community supported only, and requires matching Java and Python versions
- Python 3.13 support is beta
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.
Pricing, plan by plan
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
TensorBoard
FreeNo published plan breakdown. See the TensorBoard review.
Which should you pick?
Choose Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
Choose TensorBoard if
- You need scalar dashboards.
- You want to start without paying.
- You also want run comparison.
Questions people ask
- Is Ray or TensorBoard better?
- Neither clearly leads. Ray starts at Free and TensorBoard at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Ray or TensorBoard?
- Ray starts at Free and TensorBoard at Free.
- Does Ray or TensorBoard run on more platforms?
- Ray runs on Linux, Mac, Windows. TensorBoard runs on Web.
- Can I use Ray for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ray best used for?
- Ray is most often used for distributed ai model training and serving, large-scale data processing, reinforcement learning workloads, ml inference serving. Of those, distributed ai model training and serving and large-scale data processing are not what TensorBoard is typically brought in for.
- What can Ray do that TensorBoard cannot?
- Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions.
Answered from the vendors’ own pages
Ray: Is Ray free?
Yes. Ray is free and open source software with over 34,800 GitHub stars and 1,000+ contributors. Users can download and use the Ray framework at no cost.
SourceTensorBoard: 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.
Ray: Is there a paid option for Ray?
Yes. Anyscale, the managed platform built by Ray's creators, offers paid tiers with enterprise features like governance and advanced tooling. Specific Anyscale pricing details are not listed on the Ray website.
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.
Ray: Can I try Ray with credits?
Yes. New users can try Ray with $100 credit on Anyscale's managed platform to explore the service.
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.
TensorBoard: 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
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