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AI · head to head

AutoGen vs Seldon

AutoGen logo

AutoGen

AI

Programming framework for multi-agent agentic AI

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: AutoGen framework now in maintenance mode, no new features planned; 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: AutoGen covers Multi-agent orchestration, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which AutoGen and Seldon actually diverge.

Attributes where AutoGen and Seldon differ
AttributeAutoGenSeldon
Pricing modelOpen source, no pricingfreemium
PlatformsPython, .NETLinux
CategoryAIMachine Learning
FoundedUnknown2014

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 AutoGen

  • Multi-agent orchestration
  • Message passing API
  • AgentChat API
  • Extensions API
  • MCP server support
  • AutoGen Studio
  • Cross-language support
  • Observable agent networks

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.

AutoGen

  • Building multi-agent conversational systemsnot Seldon
  • Rapid prototyping of agent applicationsnot Seldon
  • Research on agentic AI patterns and architecturesnot Seldon
  • Distributed agent networks across boundariesnot Seldon

Seldon

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

Where each one falls short

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

AutoGen

  • Framework now in maintenance mode, no new features planned
  • Steeper learning curve for advanced use cases
  • Microsoft recommends new projects use Agent Framework instead
  • Limited to Python and .NET platforms

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

AutoGen

Free
  • Open SourceFree
    • MIT and CC-BY-4.0 licenses
    • Full framework access
    • Community support

Seldon

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

Which should you pick?

Choose AutoGen if

  • You need multi-agent orchestration.
  • You want to start without paying.
  • You work on Python, .NET.
  • You also want message passing api.
AutoGen 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 AutoGen or Seldon better?
Neither clearly leads. AutoGen 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, AutoGen or Seldon?
AutoGen starts at Free and Seldon at Free.
Does AutoGen or Seldon run on more platforms?
AutoGen runs on Python, .NET. Seldon runs on Linux.
Can I use AutoGen for free?
Both have a free tier, so you can try either at no cost before committing.
What is AutoGen best used for?
AutoGen is most often used for building multi-agent conversational systems, rapid prototyping of agent applications, research on agentic ai patterns and architectures, distributed agent networks across boundaries. Of those, building multi-agent conversational systems and rapid prototyping of agent applications are not what Seldon is typically brought in for.
What can AutoGen do that Seldon cannot?
AutoGen covers Multi-agent orchestration, Message passing API, AgentChat API, Extensions API. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

AutoGen: Is AutoGen still actively developed?

As of March 2026, AutoGen is in maintenance mode and will not receive new features. Microsoft recommends new projects use the Microsoft Agent Framework instead.

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.

AutoGen: Can I still use AutoGen for new projects?

While AutoGen is stable and maintained for existing projects, Microsoft recommends using the Microsoft Agent Framework for new development.

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.

AutoGen: What LLM providers does AutoGen support?

AutoGen includes extensions for OpenAI and Azure OpenAI through its Extensions API, with community support for other providers.

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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