Machine Learning · head to head
Comet ML vs TensorBoard
Comet ML
Machine Learning
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
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: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; 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: Comet ML covers Experiment tracking, TensorBoard covers Scalar dashboards.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Comet ML and TensorBoard actually diverge.
| Attribute | Comet ML | TensorBoard |
|---|---|---|
| Pricing model | freemium | open-source |
| Platforms | Web, Linux, Mac, Windows | Web |
| Founded | 2017 | 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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
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.
Comet ML
- LLM observability and monitoringnot TensorBoard
- AI agent testing and debuggingnot TensorBoard
- Experiment tracking for machine learningnot TensorBoard
- Model registry and version managementnot TensorBoard
- ML model training monitoringnot TensorBoard
TensorBoard
- Watching a training run in progress on a workstation or a remote box, to decide whether to stop it earlynot Comet ML
- Diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curvenot Comet ML
- Profiling a slow training loop to find out whether the bottleneck is the data pipeline or the acceleratornot Comet ML
- Working in an environment with no outbound network access, where a hosted tracking service is not an optionnot Comet ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Comet ML
- The free cloud tier caps data at 25,000 spans a month with 60 day retention
- Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
- Overage on Pro is $5 per additional 100,000 spans
- The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
- Pro MLOps is $19 per user per month and caps the team at 10 users
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
Comet ML
Free- Free CloudFree
- Up to 10 team members
- 25,000 spans per month
- 60-day data retention
- Pro Cloud$19/month
- Up to 50 team members
- 100,000 spans per month
- 60-day data retention
- MLOps FreeFree
- 1 user with fair usage policy
- Experiment tracking
- Dataset management
- MLOps Pro$19/user/month
- Up to 10 users
- 1,500 training hours included
- 500GB storage included
TensorBoard
FreeNo published plan breakdown. See the TensorBoard review.
Which should you pick?
Choose Comet ML if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Linux, Mac, Windows.
- You also want code versioning.
Choose TensorBoard if
- You need scalar dashboards.
- You want to start without paying.
- You also want run comparison.
Questions people ask
- Is Comet ML or TensorBoard better?
- Neither clearly leads. Comet ML 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, Comet ML or TensorBoard?
- Comet ML starts at Free and TensorBoard at Free.
- Does Comet ML or TensorBoard run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. TensorBoard runs on Web.
- Can I use Comet ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Comet ML best used for?
- Comet ML is most often used for llm observability and monitoring, ai agent testing and debugging, experiment tracking for machine learning, model registry and version management. Of those, llm observability and monitoring and ai agent testing and debugging are not what TensorBoard is typically brought in for.
- What can Comet ML do that TensorBoard cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions.
Answered from the vendors’ own pages
Comet ML: Does Comet.ml offer a free plan?
Yes, Comet.ml offers free tiers for both Opik (cloud observability) and MLOps platforms. Free Cloud Opik includes up to 10 team members and 25,000 spans/month. Free MLOps tier is limited to 1 user.
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.
Comet ML: How many team members can use the free Comet.ml tier?
Free Cloud supports up to 10 team members. The Pro Cloud plan supports up to 50 team members at $19/month.
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.
Comet ML: What is a span in Comet.ml pricing?
A span represents a single tracked operation such as model requests or function calls. Free Cloud tier includes 25,000 spans per month.
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.
Comet ML: Does Comet.ml offer academic pricing?
Yes, a free Pro plan is available for academic users; verification is required via signup.
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
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