Machine Learning · head to head
Seldon vs Semantic Kernel
Seldon
Machine Learning
Kubernetes model serving whose current version is licensed under the Business Source Licence
- From
- Free
- Rated
- -
Semantic Kernel
Machine Learning
Model-agnostic SDK for AI orchestration
- From
- Free
- Rated
- -
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.; Semantic Kernel steep learning curve for advanced features
- They diverge on capability: Seldon covers Kubernetes custom resources, Semantic Kernel covers Multi-model support.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Seldon and Semantic Kernel actually diverge.
| Attribute | Seldon | Semantic Kernel |
|---|---|---|
| Pricing model | freemium | Open source, no pricing |
| Platforms | Linux | Python, .NET, Java |
| Founded | 2014 | Unknown |
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 Semantic Kernel
- Multi-model support
- Agent framework
- Multi-agent systems
- Plugin ecosystem
- Vector database integration
- Multimodal support
- Local model support
- Enterprise observability
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 Semantic Kernel
- Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot Semantic Kernel
- Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot Semantic Kernel
- Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot Semantic Kernel
Semantic Kernel
- Building enterprise AI applications with LLM integrationnot Seldon
- Creating multi-agent systems for complex workflowsnot Seldon
- Developing AI-powered chatbots and assistantsnot Seldon
- Implementing RAG systems with vector databasesnot 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.
Semantic Kernel
- Steep learning curve for advanced features
- Documentation focuses on Azure cloud services
- Configuration complexity for multi-model scenarios
- Requires understanding of AI/LLM concepts
Pricing, plan by plan
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Semantic Kernel
Free- Open SourceFree
- MIT license
- Full framework access
- All language SDKs
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.
Choose Semantic Kernel if
- You need multi-model support.
- You want to start without paying.
- You work on Python, .NET, Java.
- You also want agent framework.
Questions people ask
- Is Seldon or Semantic Kernel better?
- Neither clearly leads. Seldon starts at Free and Semantic Kernel at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Seldon or Semantic Kernel?
- Seldon starts at Free and Semantic Kernel at Free.
- Does Seldon or Semantic Kernel run on more platforms?
- Seldon runs on Linux. Semantic Kernel runs on Python, .NET, Java.
- 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 Semantic Kernel is typically brought in for.
- What can Seldon do that Semantic Kernel cannot?
- Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol. Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem.
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.
Semantic Kernel: What LLM providers does Semantic Kernel support?
Semantic Kernel supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and other providers through extensible model implementations.
SourceSeldon: 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.
Semantic Kernel: Can I run Semantic Kernel locally?
Yes. Semantic Kernel supports local models through Ollama, LMStudio, and ONNX for complete data control and offline operation.
SourceSeldon: 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.
Semantic Kernel: Is Semantic Kernel free?
Yes. Semantic Kernel is MIT-licensed open source and completely free. You only pay for external LLM APIs you use.
SourceSeldon: 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.
Related pages
More on Semantic Kernel
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