Softwr

Machine Learning · head to head

Semantic Kernel vs Zilliz

Semantic Kernel logo

Semantic Kernel

Machine Learning

Model-agnostic SDK for AI orchestration

From
Free
Rated
-
Full reviewWebsite
Zilliz logo

Zilliz

Databases

Managed vector database and vector lakebase for AI applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Semantic Kernel steep learning curve for advanced features; Zilliz pricing structure not publicly disclosed, requires sales contact
  • They diverge on capability: Semantic Kernel covers Multi-model support, Zilliz covers Vector indexing.
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which Semantic Kernel and Zilliz actually diverge.

Attributes where Semantic Kernel and Zilliz differ
AttributeSemantic KernelZilliz
Pricing modelOpen source, no pricingcontact-sales
PlatformsPython, .NET, JavaCloud, Self-hosted
CategoryMachine LearningDatabases
FoundedUnknown2017

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 Semantic Kernel

  • Multi-model support
  • Agent framework
  • Multi-agent systems
  • Plugin ecosystem
  • Vector database integration
  • Multimodal support
  • Local model support
  • Enterprise observability

Only in Zilliz

  • Vector indexing
  • Distributed architecture
  • SQL interface
  • Tensor support
  • Real-time search
  • Cloud-native
  • Open-source compatible

What people use each for

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

Semantic Kernel

  • Building enterprise AI applications with LLM integrationnot Zilliz
  • Creating multi-agent systems for complex workflowsnot Zilliz
  • Developing AI-powered chatbots and assistantsnot Zilliz
  • Implementing RAG systems with vector databasesnot Zilliz

Zilliz

  • Build retrieval-augmented generation (RAG) systemsnot Semantic Kernel
  • Implement semantic search over documentsnot Semantic Kernel
  • Create multimodal search with text and imagesnot Semantic Kernel
  • Power recommendation engines with vector similaritynot Semantic Kernel
  • Enable similarity search on user embeddingsnot Semantic Kernel

Where each one falls short

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

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

Zilliz

  • Pricing structure not publicly disclosed, requires sales contact
  • Operational complexity for self-hosted Milvus deployments
  • Learning curve for those unfamiliar with vector databases
  • Limited built-in analytics compared to some alternatives

Pricing, plan by plan

Semantic Kernel

Free
  • Open SourceFree
    • MIT license
    • Full framework access
    • All language SDKs

Zilliz

Free

No published plan breakdown. See the Zilliz review.

Which should you pick?

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.
Semantic Kernel review

Choose Zilliz if

  • You need vector indexing.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want distributed architecture.
Zilliz review

Questions people ask

Is Semantic Kernel or Zilliz better?
Neither clearly leads. Semantic Kernel starts at Free and Zilliz at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Semantic Kernel or Zilliz?
Semantic Kernel starts at Free and Zilliz at Free.
Does Semantic Kernel or Zilliz run on more platforms?
Semantic Kernel runs on Python, .NET, Java. Zilliz runs on Cloud, Self-hosted.
Can I use Semantic Kernel for free?
Both have a free tier, so you can try either at no cost before committing.
What is Semantic Kernel best used for?
Semantic Kernel is most often used for building enterprise ai applications with llm integration, creating multi-agent systems for complex workflows, developing ai-powered chatbots and assistants, implementing rag systems with vector databases. Of those, building enterprise ai applications with llm integration and creating multi-agent systems for complex workflows are not what Zilliz is typically brought in for.
What can Semantic Kernel do that Zilliz cannot?
Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem. Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor support.

Answered from the vendors’ own pages

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.

Source
Zilliz: What is the difference between Milvus and Zilliz Cloud?

Milvus is the open-source vector database that you can self-host. Zilliz Cloud is the fully managed service built on Milvus that removes operational overhead and handles scaling automatically.

Source
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.

Source
Zilliz: How many vectors can Zilliz handle?

Milvus and Zilliz Cloud can store and search billions of vectors through their distributed architecture that separates storage and compute layers.

Source
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.

Source
Zilliz: Is Milvus open-source?

Yes, Milvus is open-source under the Apache License 2.0 and is part of the LF AI & Data Foundation.

Source
Zilliz: What pricing does Zilliz Cloud offer?

Zilliz Cloud pricing is not publicly listed and requires contacting their team to discuss your specific scale and use case requirements.

Source
Share

Related pages

More on Semantic Kernel

More on Zilliz

Keep looking

Other head to heads