LlamaIndex vs Semantic Kernel
Detailed side-by-side comparison to help you choose the right tool
LlamaIndex
Orchestration & Chains
Data framework for RAG pipelines, indexing, and agent retrieval.
Starting Price
Custom
Semantic Kernel
Agent Frameworks
SDK for building AI agents with planners, memory, and connectors.
Starting Price
Custom
Feature Comparison
| Feature | LlamaIndex | Semantic Kernel |
|---|---|---|
| Category | Orchestration & Chains | Agent Frameworks |
| Pricing Plans | 19 tiers | 11 tiers |
| Starting Price | ||
| Key Features |
|
|
LlamaIndex - Pros & Cons
Pros
- ✓Best-in-class framework for RAG and data-augmented LLM applications
- ✓Extensive data connector library (LlamaHub) for 100+ sources
- ✓Sophisticated indexing strategies for different retrieval needs
- ✓Open-source with optional managed cloud service
- ✓Strong focus on production-grade retrieval quality
Cons
- ✗Primarily retrieval-focused — less suited for general agent orchestration
- ✗Index creation can be slow and resource-intensive for large datasets
- ✗Learning curve for choosing the right index type and retrieval strategy
- ✗Cloud service pricing can add up for high-volume applications
Semantic Kernel - Pros & Cons
Pros
- ✓First-class support for C# and .NET alongside Python
- ✓Backed by Microsoft with enterprise-grade stability
- ✓Plugin architecture makes it easy to extend with custom skills
- ✓Strong integration with Azure AI services and OpenAI
- ✓Well-suited for enterprise environments already using Microsoft stack
Cons
- ✗Smaller community compared to Python-first frameworks
- ✗Documentation can be fragmented across C# and Python versions
- ✗Less mature agent orchestration compared to dedicated agent frameworks
- ✗Azure-centric patterns may not suit multi-cloud strategies
Ready to Choose?
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