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Machine Learning · head to head

Semantic Kernel vs Weaviate

Semantic Kernel logo

Semantic Kernel

Machine Learning

Model-agnostic SDK for AI orchestration

From
Free
Rated
-
Full reviewWebsite
Weaviate logo

Weaviate

Machine Learning

Open-source vector database

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Semantic Kernel steep learning curve for advanced features; Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection
  • They diverge on capability: Semantic Kernel covers Multi-model support, Weaviate covers Vector and keyword search.

Where they differ

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

Attributes where Semantic Kernel and Weaviate differ
AttributeSemantic KernelWeaviate
Pricing modelOpen source, no pricingfreemium
PlatformsPython, .NET, JavaLinux, Mac, Windows, Web
FoundedUnknown2019

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 Semantic Kernel

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

Only in Weaviate

  • Vector and keyword search
  • Built-in vectorizers
  • GraphQL API
  • Multi-tenancy
  • Hybrid search
  • OpenAI
  • Hugging Face
  • Cohere

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 Weaviate
  • Creating multi-agent systems for complex workflowsnot Weaviate
  • Developing AI-powered chatbots and assistantsnot Weaviate
  • Implementing RAG systems with vector databasesnot Weaviate

Weaviate

  • Running a vector database for semantic and hybrid searchnot Semantic Kernel
  • Generating and storing embeddings alongside the objects they describenot 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

Weaviate

  • The free tier caps at 100,000 objects, 1 GB of memory and a single collection
  • Billing is per million vector dimensions rather than per record, so wider embeddings cost proportionally more for the same object count
  • Premium is a prepaid contract starting at $400 a month rather than pay as you go
  • Storage rates do not fall consistently with tier, and Premium Dedicated is $0.1505 per GiB against $0.12 on the cheaper Flex plan
  • The Query Agent is metered separately, free to 1,000 requests a month and $30 a month plus overage beyond

Pricing, plan by plan

Semantic Kernel

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

Weaviate

Free
  • Open SourceFree
    • Full features
    • Self-hosted
  • ServerlessFree
    • Managed service
    • Auto-scaling

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 Weaviate if

  • You need vector and keyword search.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want built-in vectorizers.
Weaviate review

Questions people ask

Is Semantic Kernel or Weaviate better?
Neither clearly leads. Semantic Kernel starts at Free and Weaviate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Semantic Kernel or Weaviate?
Semantic Kernel starts at Free and Weaviate at Free.
Does Semantic Kernel or Weaviate run on more platforms?
Semantic Kernel runs on Python, .NET, Java. Weaviate runs on Linux, Mac, Windows, Web.
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 Weaviate is typically brought in for.
What can Semantic Kernel do that Weaviate cannot?
Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem. Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy.

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
Weaviate: What pricing options does Weaviate offer?

Weaviate provides a free tier with usage-based pricing, plus enterprise options. Visit the pricing page for detailed information on plans.

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
Weaviate: Does Weaviate offer customer support?

Yes, support is included with Weaviate's cloud offerings. Enterprise customers receive first-class support from their global team of experts.

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
Weaviate: Can I deploy Weaviate on my own infrastructure?

Yes. Weaviate is open source and deployment-agnostic. You can run it in your own cloud environment or use their managed cloud service.

Source
Weaviate: What data security features does Weaviate provide?

Weaviate includes security & governance, RBAC, SOC 2 and HIPAA compliance, along with multi-tenancy and high availability for enterprise requirements.

Source
Weaviate: How do I get started with Weaviate?

Sign up for their cloud tier, create your first dataset, connect an LLM, and build your AI app. Documentation and quickstart guides are available for Python, Go, TypeScript, and JavaScript.

Source
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