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Machine Learning · head to head

TensorBoard vs Vellum

T

TensorBoard

Machine Learning

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

From
Free
Free plan
Yes
Full reviewWebsite
Vellum logo

Vellum

AI

Personalized AI assistant with persistent memory and autonomous action-taking

From
Free
Free plan
Yes
Full reviewWebsite

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.; Vellum pricing varies significantly by computational tier, making budget planning difficult
  • They diverge on capability: TensorBoard covers Scalar dashboards, Vellum covers Persistent AI identity.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which TensorBoard and Vellum actually diverge.

Attributes where TensorBoard and Vellum differ
AttributeTensorBoardVellum
Pricing modelopen-sourceFreemium with paid plans and pay-as-you-go credits
PlatformsWebWeb, macOS, iOS, Slack, Telegram, Terminal
CategoryMachine LearningAI

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 TensorBoard

  • Scalar dashboards
  • Run comparison
  • Graph visualisation
  • Histograms and distributions
  • Embedding projector
  • Image, audio and text panels
  • Hyperparameter view
  • Profiler

Only in Vellum

  • Persistent AI identity
  • Long-term memory
  • Email automation
  • Calendar management
  • Autonomous task execution
  • Multi-device synchronization
  • Custom plugins
  • Open-source deployment

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 Vellum
  • Diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curvenot Vellum
  • Profiling a slow training loop to find out whether the bottleneck is the data pipeline or the acceleratornot Vellum
  • Working in an environment with no outbound network access, where a hosted tracking service is not an optionnot Vellum

Vellum

  • Personal productivity automation across email and calendarnot TensorBoard
  • CRM updates and pipeline managementnot TensorBoard
  • Expense report generation and managementnot TensorBoard
  • Document drafting and content creationnot TensorBoard
  • Meeting scheduling and calendar coordinationnot 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.

Vellum

  • Pricing varies significantly by computational tier, making budget planning difficult
  • Requires explicit permission for sensitive actions, limiting autonomy
  • Self-hosting option may intimidate non-technical users
  • Learning curve to develop customizations with plugins

Pricing, plan by plan

TensorBoard

Free

No published plan breakdown. See the TensorBoard review.

Vellum

Free
  • FreeFree
    • Starter credits included
    • Basic features
  • Mighty$30/month
    • 1 vCPU, 2 GiB RAM
    • 10 GB storage
    • Included usage allowance
  • Super$100/month
    • 2.5 vCPU, 5 GiB RAM
    • 30 GB storage
    • Assistant email and subdomain
  • Ultra$200/month
    • 4 vCPU, 8 GiB RAM
    • 60 GB storage
    • Assistant email and subdomain

Which should you pick?

Choose TensorBoard if

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

Choose Vellum if

  • You need persistent ai identity.
  • You want to start without paying.
  • You work on Web, macOS, iOS, Slack, Telegram, Terminal.
  • You also want long-term memory.
Vellum review

Questions people ask

Is TensorBoard or Vellum better?
Neither clearly leads. TensorBoard starts at Free and Vellum at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, TensorBoard or Vellum?
TensorBoard starts at Free and Vellum at Free.
Does TensorBoard or Vellum run on more platforms?
TensorBoard runs on Web. Vellum runs on Web, macOS, iOS, Slack, Telegram, Terminal.
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 Vellum is typically brought in for.
What can TensorBoard do that Vellum cannot?
TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions. Vellum covers Persistent AI identity, Long-term memory, Email automation, Calendar management.

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.

Vellum: How does Vellum protect my data privacy?

Vellum uses permission-based controls requiring explicit approval for sensitive actions. Data is encrypted and the option for self-hosting provides complete data control. Open-source deployment option allows hosting on your own infrastructure.

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.

Vellum: Can I use Vellum for business team collaboration?

Vellum is primarily designed as a personal assistant with persistent identity. For team collaboration, platforms like Lindy offer better multi-user features with shared credit pools and Slack workspace integration.

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.

Vellum: What does the free tier include?

Vellum offers free starter credits to begin using the platform. Beyond that, it operates on a pay-as-you-go credit model with paid tiers starting at $30/month for increased computational resources and bundled credits.

Source
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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