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Machine Learning · head to head

TensorBoard vs Vitess

T

TensorBoard

Machine Learning

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

From
Free
Free plan
Yes
Visit TensorBoardFull review
Vitess logo

Vitess

Databases

Scalable database clustering system for horizontal scaling of MySQL

From
Free
Free plan
Yes
Visit VitessFull review

The short version

  • Both are free.
  • Only TensorBoard lists scalar dashboards; only Vitess lists horizontal Sharding.
  • Where Vitess falls short: VTGate scatter queries without sharding key incur significant performance penalties.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which TensorBoard and Vitess actually diverge.

Attributes where TensorBoard and Vitess differ
AttributeTensorBoardVitess
PlatformsWebLinux, macOS, Self-hosted
CategoryMachine LearningDatabases

Identical on both: starting price (Free), free tier (Yes).

Not yet recorded: pricing model for Vitess, founded for TensorBoard.

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 Vitess

  • Horizontal Sharding
  • Connection Pooling
  • Query Routing
  • Online Schema Changes
  • Shard Management
  • Replication Management
  • Automated Failover
  • MySQL

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

Vitess

  • Transaction processingnot TensorBoard
  • Data storagenot TensorBoard
  • Application backendnot TensorBoard
  • Reportingnot TensorBoard
  • Data 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

Vitess

  • VTGate scatter queries without sharding key incur significant performance penalties
  • Foreign key constraints not enforced across shards, requiring application-level integrity handling
  • Single primary per keyspace limits multi-region write capabilities
  • Distributed transactions without proper sharding key routing suffer performance degradation

Pricing, plan by plan

TensorBoard

Free

No published plan breakdown. See the TensorBoard review.

Vitess

Free

No published plan breakdown. See the Vitess review.

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 Vitess if

  • You need horizontal sharding.
  • You want to start without paying.
  • You work on Linux, macOS, Self-hosted.
  • You also want connection pooling.
Vitess review

Questions people ask

Is TensorBoard or Vitess better?
Neither clearly leads. TensorBoard starts at Free and Vitess at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, TensorBoard or Vitess?
TensorBoard starts at Free and Vitess at Free.
Does TensorBoard or Vitess run on more platforms?
TensorBoard runs on Web. Vitess runs on Linux, macOS, Self-hosted.
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 Vitess is typically brought in for.
What can TensorBoard do that Vitess cannot?
TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions. Vitess covers Horizontal Sharding, Connection Pooling, Query Routing, Online Schema Changes.

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.

Vitess: Is Vitess free to use?

Yes. Vitess is completely free and open source under the Apache 2.0 license. It is a graduated CNCF project with no licensing costs or pricing tiers.

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.

Vitess: What databases does Vitess support?

Vitess supports MySQL and MariaDB as backend databases. It acts as a middleware layer that adds sharding and orchestration capabilities on top of these databases.

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.

Vitess: Does Vitess require Kubernetes to run?

No. Vitess can run on Kubernetes using the Vitess Operator, but it can also be deployed on traditional infrastructure. Kubernetes integration is optional and provides additional automation benefits.

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.

Vitess: How does Vitess handle cross-shard transactions?

Vitess supports distributed transactions across shards, but they require queries to be routed through the sharding key. Transactions without a proper sharding key can result in slower performance.

Source
TensorBoard: What does it cost?

Nothing. It is Apache 2.0 licensed and runs on your own machine.

Vitess: Does Vitess enforce foreign key constraints?

Vitess does not enforce foreign key constraints across shards by default. Referential integrity must be managed at the application layer, though per-database support can be enabled with limitations.

Source
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