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Machine Learning · head to head

Semantic Kernel vs LangChain

Semantic Kernel logo

Semantic Kernel

Machine Learning

Model-agnostic SDK for AI orchestration

From
Free
Rated
-
Full reviewWebsite
LangChain logo

LangChain

Machine Learning

Build applications with LLMs through composability

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Semantic Kernel steep learning curve for advanced features; LangChain the free Developer plan of LangSmith is limited to 1 seat
  • They diverge on capability: Semantic Kernel covers Multi-model support, LangChain covers Chains and agents.

Where they differ

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

Attributes where Semantic Kernel and LangChain differ
AttributeSemantic KernelLangChain
Pricing modelOpen source, no pricingfreemium
PlatformsPython, .NET, JavaLinux, Mac, Windows
FoundedUnknown2022

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 LangChain

  • Chains and agents
  • Retrieval-augmented generation
  • Memory management
  • Tool integration
  • Prompt templates
  • OpenAI
  • Anthropic
  • Hugging Face

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

LangChain

  • Building LLM applications and agents in Python or JavaScriptnot Semantic Kernel
  • Tracing and debugging LLM chains and agent runsnot Semantic Kernel
  • Evaluating prompt and model changes against datasetsnot 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

LangChain

  • The free Developer plan of LangSmith is limited to 1 seat
  • Base traces are retained for 14 days only; 400 day retention costs extra
  • Included traces are capped at 5,000 per month on Developer and 10,000 per month on Plus, with everything beyond billed pay as you go
  • Self hosted and hybrid deployment of LangSmith is Enterprise only
  • Custom SSO, RBAC and ABAC are Enterprise only
  • A support SLA is Enterprise only
  • Enterprise pricing is by quote with no published rate

Pricing, plan by plan

Semantic Kernel

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

LangChain

Free
  • Open SourceFree
    • Full framework
    • Community support
  • LangSmith$39/month
    • Debugging
    • Monitoring
    • Testing

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 LangChain if

  • You need chains and agents.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want retrieval-augmented generation.
LangChain review

Questions people ask

Is Semantic Kernel or LangChain better?
Neither clearly leads. Semantic Kernel starts at Free and LangChain at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Semantic Kernel or LangChain?
Semantic Kernel starts at Free and LangChain at Free.
Does Semantic Kernel or LangChain run on more platforms?
Semantic Kernel runs on Python, .NET, Java. LangChain runs on Linux, Mac, Windows.
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 LangChain is typically brought in for.
What can Semantic Kernel do that LangChain cannot?
Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem. LangChain covers Chains and agents, Retrieval-augmented generation, Memory management, Tool integration.

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
LangChain: Does LangChain charge for its services?

LangChain's main website does not display pricing. However, LangSmith (a related platform) offers both free and paid plans. Visit the dedicated pricing page or contact LangChain for details.

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
LangChain: How can I learn about LangChain pricing?

Click on the Pricing link in navigation or use the Try LangSmith or Get a demo options to explore pricing for LangChain's commercial offerings.

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
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