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Machine Learning · head to head

Seldon vs TensorBoard

Seldon logo

Seldon

Machine Learning

Kubernetes model serving whose current version is licensed under the Business Source Licence

From
Free
Rated
-
Full reviewWebsite
T

TensorBoard

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Seldon seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.; 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: Seldon covers Kubernetes custom resources, TensorBoard covers Scalar dashboards.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Seldon and TensorBoard actually diverge.

Attributes where Seldon and TensorBoard differ
AttributeSeldonTensorBoard
Pricing modelfreemiumopen-source
PlatformsLinuxWeb
Founded2014Unknown

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 Seldon

  • Kubernetes custom resources
  • Inference graphs
  • Traffic strategies
  • Open Inference Protocol
  • Alibi Explain
  • Alibi Detect
  • Kafka-backed pipelines in v2
  • Commercial control plane

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.

Seldon

  • Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot TensorBoard
  • Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot TensorBoard
  • Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot TensorBoard
  • Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot TensorBoard

TensorBoard

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

Where each one falls short

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

Seldon

  • Seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
  • Core v1 remains Apache 2.0 but is in maintenance, so taking the free route means running software that receives no new development while the architecture it belongs to moves on without it.
  • Version 2 is a different system rather than a newer release, with different custom resources, a scheduler component and a Kafka-based pipeline model, so migrating from v1 is a re-implementation of every deployment manifest rather than an upgrade.
  • Kafka is a dependency for v2 pipelines, so an organisation that does not already operate it takes on a distributed log with its own storage, retention, rebalancing and failure modes purely in order to serve models.
  • Everything assumes Kubernetes fluency and the failure modes are Kubernetes failure modes, custom resource version mismatches, an operator that will not reconcile, admission webhooks and resource limits terminating an inference pod mid-request, so it needs a platform engineer rather than a data scientist.

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

Seldon

Free
  • Seldon CoreFree
    • Open source
    • Kubernetes deployment
    • Model serving
  • Seldon DeployFree
    • Enterprise features
    • GUI
    • Monitoring

TensorBoard

Free

No published plan breakdown. See the TensorBoard review.

Which should you pick?

Choose Seldon if

  • You need kubernetes custom resources.
  • You want to start without paying.
  • You work on Linux.
  • You also want inference graphs.
Seldon 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 Seldon or TensorBoard better?
Neither clearly leads. Seldon 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, Seldon or TensorBoard?
Seldon starts at Free and TensorBoard at Free.
Does Seldon or TensorBoard run on more platforms?
Seldon runs on Linux. TensorBoard runs on Web.
Can I use Seldon for free?
Both have a free tier, so you can try either at no cost before committing.
What is Seldon best used for?
Seldon is most often used for serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate services, running genuine production experiments where a share of live traffic goes to a candidate model and the results are compared, regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on later, organisations with an established kubernetes platform team who want serving expressed as manifests under existing deployment controls. Of those, serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate services and running genuine production experiments where a share of live traffic goes to a candidate model and the results are compared are not what TensorBoard is typically brought in for.
What can Seldon do that TensorBoard cannot?
Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol. TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions.

Answered from the vendors’ own pages

Seldon: Is Seldon open source?

Partly, and this is the thing to check before you build on it. Core v1 is Apache 2.0 but in maintenance. Core v2 was moved to the Business Source Licence in 2024, which allows evaluation but not unlicensed production use. Verify the current licence of each component you intend to run, including MLServer and the Alibi libraries.

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.

Seldon: What is the difference between v1 and v2?

Architecture, not just version number. v2 introduces a scheduler, a different set of custom resources and Kafka-backed pipelines. Manifests, mental model and operations all change, so treat a move as a project.

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.

Seldon: Do I need Kubernetes?

Yes. It is a Kubernetes-native system and there is no meaningful deployment without a cluster and someone competent to run it.

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.

Seldon: What is MLServer?

Seldon's Python inference server implementing the Open Inference Protocol, usable inside Seldon deployments or on its own. Check its current licence alongside Core's, since the company has moved projects onto the Business Source Licence.

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.

Seldon: Do I have to run Kafka?

For v2 pipelines, yes. If you only need single models served, that dependency is a large amount of infrastructure for the benefit, and a simpler serving layer may be the better answer.

TensorBoard: What does it cost?

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

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