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AI · head to head

Modal vs TensorBoard

Modal logo

Modal

AI

Cloud functions for AI and ML

From
Free
Free plan
Yes
Full reviewWebsite
T

TensorBoard

Machine Learning

Local visualisation for training runs, reading event files written to a directory

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Modal the Team plan carries a $250 monthly base fee and returns only $100 of that as free credits, so $150 is a flat charge before any compute; 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: Modal covers Serverless GPUs, TensorBoard covers Scalar dashboards.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Modal and TensorBoard actually diverge.

Attributes where Modal and TensorBoard differ
AttributeModalTensorBoard
Pricing modelusage-basedopen-source
PlatformsCloud, ApiWeb
CategoryAIMachine Learning
Founded2021Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 Modal

  • Serverless GPUs
  • Python functions
  • Auto-scaling
  • Fast cold starts
  • Python SDK
  • GitHub Actions
  • Cloud storage
  • Cloud support

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.

Modal

  • Running serverless GPU workloads for model inference and trainingnot TensorBoard
  • Executing Python functions on cloud compute without managing serversnot TensorBoard

TensorBoard

  • Watching a training run in progress on a workstation or a remote box, to decide whether to stop it earlynot Modal
  • Diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curvenot Modal
  • Profiling a slow training loop to find out whether the bottleneck is the data pipeline or the acceleratornot Modal
  • Working in an environment with no outbound network access, where a hosted tracking service is not an optionnot Modal

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Modal

  • The Team plan carries a $250 monthly base fee and returns only $100 of that as free credits, so $150 is a flat charge before any compute
  • Compute is billed per second across separate GPU and CPU meters, so total cost depends on execution time rather than any fixed rate
  • The Starter plan's $30 monthly free credit is the only allowance below the paid base fee
  • Enterprise volume discounts are custom and unpublished

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

Modal

Free
  • StarterFree
    • 3 seats
    • 100 containers
    • 10 GPU concurrency
  • Team$250/month
    • Unlimited seats
    • 5,000 containers
    • 50 GPU concurrency
  • Enterprise$null/custom
    • Custom seats, containers, and GPU concurrency

TensorBoard

Free

No published plan breakdown. See the TensorBoard review.

Which should you pick?

Choose Modal if

  • You need serverless gpus.
  • You want to start without paying.
  • You work on Cloud, Api.
  • You also want python functions.
Modal review

Choose TensorBoard if

  • You need scalar dashboards.
  • You want to start without paying.
  • You also want run comparison.
TensorBoard review

Questions people ask

Is Modal or TensorBoard better?
Neither clearly leads. Modal 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, Modal or TensorBoard?
Modal starts at Free and TensorBoard at Free.
Does Modal or TensorBoard run on more platforms?
Modal runs on Cloud, Api. TensorBoard runs on Web.
Can I use Modal for free?
Both have a free tier, so you can try either at no cost before committing.
What is Modal best used for?
Modal is most often used for running serverless gpu workloads for model inference and training, executing python functions on cloud compute without managing servers. Of those, running serverless gpu workloads for model inference and training and executing python functions on cloud compute without managing servers are not what TensorBoard is typically brought in for.
What can Modal do that TensorBoard cannot?
Modal covers Serverless GPUs, Python functions, Auto-scaling, Fast cold starts. TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions.

Answered from the vendors’ own pages

Modal: How much does Modal cost?

Modal uses pay-as-you-go pricing with Team plan at 250 USD/month base. Starter includes 30 USD/month free credits; Team includes 100 USD/month free credits. Compute charges per second for CPU cores, memory, and GPU instances.

Source
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.

Modal: Is there a free tier?

Yes, Starter plan is free plus 30 USD/month in compute credits included monthly for new users.

Source
TensorBoard: 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.

Modal: What are the seat limits?

Starter plan includes 3 seats; Team plan provides unlimited seats; Enterprise tier has custom seat allocations.

Source
TensorBoard: 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.

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