While LlamaIndex dominates the RAG conversation, Haystack has been quietly powering enterprise search systems for years. Both frameworks specialize in retrieval and document processing, but they approach the problem differently. Choosing between them requires understanding not just their features, but their underlying philosophies and where each excels.

Having built production RAG systems with both frameworks, I’ve learned that this choice matters more than the typical framework comparison suggests. Your retrieval architecture affects everything downstream: query quality, maintenance burden, and scaling costs.

Framework Philosophy Differences

The frameworks grew from different roots:

LlamaIndex started as GPT Index, focused on connecting LLMs to data. It views retrieval through the lens of LLM applications: how do you get the right context to a language model? This shows in its tight integration with various LLM providers and its focus on query-time optimization.

Haystack originated from deepset’s work on enterprise search and NLP. It views retrieval as a broader document processing pipeline problem. This shows in its emphasis on modular pipelines, production deployment patterns, and enterprise integrations.

These philosophical differences shape how each framework solves common problems.

When LlamaIndex Wins

LlamaIndex excels in scenarios focused on LLM-centric applications:

Rapid RAG Development: LlamaIndex’s high-level abstractions let you build working RAG systems quickly. The defaults are sensible, the documentation is excellent, and the path from zero to prototype is short.

Advanced Retrieval Strategies: LlamaIndex has invested heavily in retrieval innovation. Query decomposition, hierarchical retrieval, response synthesis modes, and knowledge graph integration are first-class features.

LLM-Native Patterns: Features like query rewriting, self-querying, and LLM-powered response refinement integrate naturally. LlamaIndex thinks about the full LLM application, not just retrieval.

Document Complexity: When dealing with complex documents (PDFs with tables, mixed formats, nested structures), LlamaIndex’s document understanding capabilities and LlamaParse integration provide superior results.

For foundational RAG patterns, my complete RAG systems implementation guide covers the techniques that work across frameworks.

When Haystack Wins

Haystack shines in different contexts:

Pipeline-First Architecture: Haystack’s explicit pipeline model makes complex document processing workflows clear and maintainable. When you need preprocessing, multiple retrieval stages, reranking, and post-processing, Haystack’s pipeline abstraction excels.

Enterprise Search Requirements: Haystack’s roots in enterprise search show in its support for Elasticsearch, OpenSearch, and other enterprise search backends. If you’re integrating with existing enterprise infrastructure, Haystack often has the connectors you need.

Production Deployment Patterns: Haystack includes production-ready patterns for deployment, including REST API serving, pipeline serialization, and monitoring integration. The framework expects to run in production environments.

Modular Component Swapping: Haystack’s component architecture makes it straightforward to swap retrievers, readers, or generators without restructuring your application. Testing different approaches becomes plug-and-play.

Feature Comparison

Feature	LlamaIndex	Haystack
High-level RAG abstractions	Excellent	Good
Pipeline explicitness	Implicit	Explicit
Retrieval innovations	Cutting-edge	Solid
Enterprise search backends	Limited	Excellent
Document processing	Advanced (LlamaParse)	Good
Production deployment	Growing	Mature
LLM provider integration	Extensive	Extensive
Community size	Larger	Established
Learning curve	Moderate	Moderate
Debugging visibility	Moderate	Good

Both frameworks support the core functionality you need. The differences are in emphasis and maturity of specific features.

Document Processing Differences

Document handling reveals meaningful differences:

LlamaIndex provides sophisticated document parsing through LlamaParse and native parsers. Complex PDFs, structured documents, and multi-modal content receive special attention. The framework handles document complexity well but can feel magical, understanding exactly how parsing works requires digging into internals.

Haystack offers straightforward document processing with clear pipeline stages. Preprocessing is explicit: you define converters, cleaners, and splitters as pipeline components. This explicitness makes debugging easier but requires more configuration for complex documents.

For production document processing patterns, see my building production RAG systems guide.

Retrieval Strategy Comparison

Both frameworks support hybrid search, but implementation differs:

LlamaIndex integrates hybrid search at the query engine level. You configure retrieval modes, and the framework handles combining dense and sparse results. Advanced features like query decomposition and sub-question generation are built into query engines.

Haystack implements hybrid search through explicit pipeline components. You define separate retrievers and combine them with a JoinNode or ranker. This explicitness means more configuration but clearer understanding of what’s happening.

For chunking strategies that work with either framework, my chunking strategies for RAG systems guide covers the patterns that matter.

Performance and Scaling

Real-world performance depends on your specific workload:

LlamaIndex optimizes for LLM application patterns: reducing token usage, improving response quality, minimizing latency for interactive applications. Its caching and optimization features focus on the LLM call path.

Haystack optimizes for throughput and scale: handling many documents, processing pipelines efficiently, supporting enterprise search workloads. Its optimization features focus on the retrieval path.

Neither is universally faster. LlamaIndex may handle a complex RAG query more efficiently. Haystack may process a batch of documents more quickly. Test with your actual workload.

Integration Ecosystem

Both frameworks integrate widely, with different strengths:

LlamaIndex integrates deeply with:

• LLM providers (OpenAI, Anthropic, local models)
• Vector databases (extensive coverage)
• Document services (LlamaParse, various loaders)
• Observability tools (LlamaTrace, OpenTelemetry)

Haystack integrates deeply with:

• Enterprise search (Elasticsearch, OpenSearch)
• Cloud services (AWS, Azure, GCP)
• Deployment tools (Docker, Kubernetes patterns)
• Monitoring infrastructure (enterprise logging)

Your existing infrastructure often determines which integration set matters more.

Learning Curve and Documentation

Both frameworks have substantial documentation:

LlamaIndex documentation focuses on use cases and examples. The “how do I build X?” question is usually well-answered. Understanding the underlying architecture requires more exploration.

Haystack documentation emphasizes concepts and architecture. Understanding how pipelines work is straightforward. Finding the specific pattern for your use case sometimes requires more searching.

Community activity slightly favors LlamaIndex currently, but Haystack’s community is established and helpful.

Migration Considerations

If you’re already invested in one framework:

LlamaIndex to Haystack: Focus on extracting your retrieval logic. Haystack’s explicit pipelines require restructuring how you think about the flow, but document formats are generally portable.

Haystack to LlamaIndex: The higher-level abstractions may hide complexity you’re used to controlling. Start by mapping your pipeline stages to LlamaIndex concepts.

Either to the other: Both frameworks use standard embedding formats and vector database protocols. Your indexed data can often transfer without re-embedding.

Decision Framework

Use this to guide your choice:

Choose LlamaIndex when:

• LLM application quality is the primary goal
• You need advanced retrieval strategies quickly
• Document parsing complexity is high
• Rapid prototyping matters most
• You want cutting-edge retrieval features

Choose Haystack when:

• Enterprise search integration is required
• Pipeline explicitness aids your team
• Production deployment patterns matter
• Elasticsearch/OpenSearch is your backend
• Debugging visibility is important

Consider either when:

• Standard RAG patterns suffice
• Team expertise doesn’t favor one
• Integration requirements are flexible

Hybrid Approaches

As with other framework decisions, you’re not limited to one:

Haystack for ingestion, LlamaIndex for querying: Use Haystack’s explicit pipelines for document processing, LlamaIndex’s query engines for retrieval.

Different frameworks for different services: A microservices architecture can use each framework where it fits best.

Framework for structure, custom code for specifics: Use the framework that provides the structure you need, implement specific components in plain Python.

Making Your Decision

The LlamaIndex vs Haystack choice often comes down to where your complexity lies:

If your complexity is in retrieval strategy and LLM integration, LlamaIndex’s innovations matter. Its focus on query-time optimization and response quality makes complex RAG patterns manageable.

If your complexity is in document processing pipelines and enterprise integration, Haystack’s explicit architecture helps. Its production deployment patterns and enterprise search connectors reduce integration burden.

For most RAG applications, either framework works well. The best choice is the one that matches your team’s expertise and your system’s integration requirements. Don’t over-optimize this decision. Both are capable tools that can build production-quality systems.

For deeper guidance on RAG architecture, watch my implementation tutorials on YouTube.

Ready to discuss RAG framework choices with engineers who’ve shipped production systems? Join the AI Engineering community where we share real experiences building retrieval systems with various frameworks.