Softwr

Machine Learning · head to head

Orange vs Seldon

Orange logo

Orange

Machine Learning

Data mining and visualization toolkit

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: Orange orange is licensed under the GNU General Public License version 3, so distributing modified or derived software requires releasing the source under the GPL; 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: Orange covers Visual programming, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Orange and Seldon actually diverge.

Attributes where Orange and Seldon differ
AttributeOrangeSeldon
Pricing modelopen-sourcefreemium
PlatformsLinux, Mac, WindowsLinux
Founded19962014

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 Orange

  • Visual programming
  • Data visualization
  • Machine learning
  • Text mining
  • Bioinformatics
  • Python
  • scikit-learn
  • PyQt

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.

Orange

  • Visual programming for data mining and machine learning workflowsnot Seldon
  • Teaching data science without writing codenot Seldon
  • Exploratory data visualisation and clustering on tabular datanot Seldon

Seldon

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

Where each one falls short

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

Orange

  • Orange is licensed under the GNU General Public License version 3, so distributing modified or derived software requires releasing the source under the GPL
  • The widgets and canvas are built on Qt, which is itself distributed under GPL 3.0
  • Orange add-ons may carry additional licensing requirements set in their own licence files
  • Documentation and website content are under Creative Commons Attribution-ShareAlike, which imposes an attribution and share-alike obligation on reuse
  • The software is distributed without any warranty of merchantability or fitness for a particular purpose

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

Orange

Free
  • Open SourceFree
    • Visual programming
    • Machine learning
    • Data visualization

Seldon

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

Which should you pick?

Choose Orange if

  • You need visual programming.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want data visualization.
Orange 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 Orange or Seldon better?
Neither clearly leads. Orange 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, Orange or Seldon?
Orange starts at Free and Seldon at Free.
Does Orange or Seldon run on more platforms?
Orange runs on Linux, Mac, Windows. Seldon runs on Linux.
Can I use Orange for free?
Both have a free tier, so you can try either at no cost before committing.
What is Orange best used for?
Orange is most often used for visual programming for data mining and machine learning workflows, teaching data science without writing code, exploratory data visualisation and clustering on tabular data. Of those, visual programming for data mining and machine learning workflows and teaching data science without writing code are not what Seldon is typically brought in for.
What can Orange do that Seldon cannot?
Orange covers Visual programming, Data visualization, Machine learning, Text mining. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

Orange: What is the cost of Orange Data Mining?

Orange Data Mining is free open-source software available for Windows, Mac, and other platforms. There are no subscription fees, licensing costs, or paid tiers.

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.

Orange: How is Orange Data Mining funded?

Orange Data Mining is supported through optional voluntary donations. The project encourages donations from users who value the software to support bug fixes, new features, educational content, and infrastructure maintenance.

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.

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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