Haystack vs LlamaIndex

Side-by-side comparison of features, pricing, and ratings

Analysis generated Live tool data as of 2026-11-05
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Haystack or LlamaIndex: which should you choose?

DimensionHaystackLlamaIndex
PricingFree plan · paid plans on requestFree · from $500/mo
Best forEngineering teams that need transparent, auditable RAG pipelines in production, Developers building agents with standardized tool calling and loop controlEngineering teams parsing invoices, filings, and due-diligence packets with dense tables and scans, RAG builders whose corpus mixes charts, handwriting, and multi-page tables
Standout featuresComposable RAG pipelines from retrievers, generators, memory, and tools · Agent hooks to intercept and control the agent loop (3.0) · First-class skills for reusable agent capabilities (3.0)Agentic OCR with layout-aware parsing of complex documents · OCRia-1 4B-parameter document parsing model for tables and charts · Agentic Understanding converts messy layouts into LLM-ready output
Viability score86/10083/100
APIYesYes

Haystack is the stronger pick for engineering teams that need transparent, auditable rag pipelines in production; LlamaIndex fits better for engineering teams parsing invoices, filings, and due-diligence packets with dense tables and scans.

Built from live tool data, last verified 2026-11-05.

Haystack
Haystack

Haystack is deepset's open-source Python framework for building inspectable RAG pipelines and production-grade AI agents

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LlamaIndex
LlamaIndex

LlamaParse turns messy PDFs, dense tables, and handwriting into LLM-ready structured data

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Pricing
Freemium
Freemium
Plans
Haystack (Open Source)Free
Enterprise SupportCustom
Haystack Enterprise PlatformCustom
Pay-as-you-goFree
Pro$500/mo
EnterpriseCustom
Popularity
5.1k views
4.9k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
WebAPICLI
APIWeb
Categories
🕸️ Agent Frameworks & Orchestration📦 LLM App Frameworks & SDKs
📑 Document AI & Data Extraction
FeaturesHaystackLlamaIndex
Composable RAG pipelines from retrievers, generators, memory, and tools
Agent hooks to intercept and control the agent loop (3.0)
First-class skills for reusable agent capabilities (3.0)
Built-in introspection for debugging agent behaviour (3.0)
Pre-built agents for faster startup (3.0)
AgentTool for building multi-agent systems (3.1)
Context compaction and token counters for agent memory (3.1)
Smart compaction and token budget guardrails (3.2)
ToolResultOffloadCompactor keeps agent context lean without losing tool results (3.4)
Python 3.15 support (3.4)
Hybrid retrieval combining dense and sparse strategies
Branching and looping pipelines for multi-step decision flows
YAML-serializable, cloud-agnostic, Kubernetes-ready deployment with logging and monitoring
Multimodal pipelines: image processing and audio transcription
Standardized generator interfaces for conversational AI
Agentic OCR with layout-aware parsing of complex documents
OCRia-1 4B-parameter document parsing model for tables and charts
Agentic Understanding converts messy layouts into LLM-ready output
Auto-correction loops that recursively detect and fix parsing errors
Handwritten text parsing with structure extraction
Advanced table extraction from dense or irregular layouts
Chart and graph extraction into structured data
In-line image understanding rendered into markdown
Layout detection with precise bounding boxes
Schema-based extraction agents with citations and confidence scores
Document classification and segmentation via natural-language rules
Parse tiers: Cost Effective, Agentic, Agentic Plus with Auto Mode per-page routing
Output formats: Markdown, Plain Text, per-page JSON, XLSX, HTML Tables, Annotated PDF
130+ file formats and 80+ languages
Smart result caching where re-parse costs zero credits
IntegrationsHaystackLlamaIndex
OpenAI
Anthropic
Mistral
Hugging Face
Weaviate
Pinecone
Elasticsearch
LangChain
Qdrant
Milvus
MongoDB Atlas Vector Search
AstraDB
Azure AI Search
Postgres
S3
Google Drive
Microsoft OneDrive
Microsoft SharePoint
Confluence
Jira
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Frequently Asked Questions

Which is better, Haystack or LlamaIndex?

The best choice between Haystack and LlamaIndex depends on your specific use case — we compare them independently on features, current pricing, integrations, and real-world signals (with an on-demand sentiment scan available for each). See the side-by-side breakdown above to match them to your needs.

What are the main differences between Haystack and LlamaIndex?

The key differences include pricing model, feature set, platform support, and skill level requirements. Review the full comparison on RightAIChoice for a detailed breakdown.

Is there a free version of Haystack or LlamaIndex?

Check the pricing section in the comparison for the latest pricing details on both tools, including free tiers, trial options, and paid plans.

What else are Haystack and LlamaIndex compared with?

Where can you read more about Haystack and LlamaIndex?

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Analysis generated by our model pipeline from live data: