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PyTorch vs Seldon

PyTorch logo

PyTorch

Machine Learning

Deep learning framework with dynamic computation graphs

From
Free
Rated
-
Full reviewWebsite
Seldon logo

Seldon

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: PyTorch dynamic computation graph can be less efficient for production inference than static graphs; 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.
  • They diverge on capability: PyTorch covers Dynamic computation graphs, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which PyTorch and Seldon actually diverge.

Attributes where PyTorch and Seldon differ
AttributePyTorchSeldon
Pricing modelUnknownfreemium
PlatformsLinux, Windows, macOSLinux
Founded20162014

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 PyTorch

  • Dynamic computation graphs
  • Automatic differentiation
  • GPU acceleration
  • Distributed training
  • TorchScript
  • TorchVision
  • TorchText
  • TorchAudio

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

What people use each for

The jobs each tool is most often brought in to do.

PyTorch

  • Machine learningnot Seldon
  • Data analysisnot Seldon
  • Model trainingnot Seldon
  • Predictive analyticsnot Seldon

Seldon

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

Where each one falls short

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

PyTorch

  • Dynamic computation graph can be less efficient for production inference than static graphs
  • Requires more manual code for distributed training compared to some alternatives
  • Documentation focused heavily on research use cases rather than production deployment

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.

Pricing, plan by plan

PyTorch

Free

No published plan breakdown. See the PyTorch review.

Seldon

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

Which should you pick?

Choose PyTorch if

  • You need dynamic computation graphs.
  • You want to start without paying.
  • You work on Linux, Windows, macOS.
  • You also want automatic differentiation.
PyTorch review

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

Questions people ask

Is PyTorch or Seldon better?
Neither clearly leads. PyTorch starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, PyTorch or Seldon?
PyTorch starts at Free and Seldon at Free.
Does PyTorch or Seldon run on more platforms?
PyTorch runs on Linux, Windows, macOS. Seldon runs on Linux.
Can I use PyTorch for free?
Both have a free tier, so you can try either at no cost before committing.
What is PyTorch best used for?
PyTorch is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Seldon is typically brought in for.
What can PyTorch do that Seldon cannot?
PyTorch covers Dynamic computation graphs, Automatic differentiation, GPU acceleration, Distributed training. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

PyTorch: Is PyTorch free and open source?

Yes. PyTorch is an open source machine learning framework that is completely free to use. It was originally created and open-sourced by Facebook (now Meta) in 2016.

Source
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.

PyTorch: What platforms does PyTorch support?

PyTorch supports Linux, Windows, and macOS. It provides strong GPU acceleration through CUDA and other backends for high-performance computing.

Source
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.

PyTorch: Can I use PyTorch for production deployments?

Yes. PyTorch provides graph-based execution, distributed training, mobile deployment, and quantization features to support production deployments.

Source
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.

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.

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.

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