LlamaIndex vs Unstructured

Detailed side-by-side comparison to help you choose the right tool

LlamaIndex

🔴Developer

AI agent framework

LlamaIndex is an open-source Python and TypeScript framework for building RAG, document workflows, and AI agents — with LlamaCloud for managed parsing, extraction, and indexing.

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Starting Price

Free
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Unstructured

🔴Developer

Document Processing & OCR

Unstructured data platform for GenAI that connects to any source, processes 64+ file types, and outputs clean AI-ready inputs.

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Starting Price

Free
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Feature Comparison

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FeatureLlamaIndexUnstructured
CategoryAI agent frameworkDocument Processing & OCR
Pricing Plans8 tiers4 tiers
Starting PriceFreeFree
Key Features
  • • LlamaParse for 50+ unstructured file types
  • • Document parsing, extraction, indexing, and retrieval
  • • Open-source repos plus LiteParse for local document parsing
  • • Universal Document Partitioning
  • • Structure-Aware Chunking
  • • Table Extraction

LlamaIndex - Pros & Cons

Pros

  • ✓Best-in-class retrieval strategies: hybrid, parent-child, summary indexes, knowledge graphs
  • ✓LlamaParse is the strongest PDF/document parser for enterprise RAG today
  • ✓Open-source library is MIT-licensed and runs anywhere
  • ✓Workflows agent layer is a clean alternative to LangGraph for stateful task graphs
  • ✓10,000 free LlamaCloud credits make evaluation painless

Cons

  • ✗LlamaCloud paid pricing is credit-based and harder to model than seat pricing
  • ✗Workflows ecosystem is younger than LangGraph's; fewer multi-agent examples in the wild
  • ✗Library API has churned over major releases — older tutorials are often out of date
  • ✗Visual builder UX is not part of the product; teams that want no-code go elsewhere
  • ✗Pure agent orchestration with complex branching is still cleaner in LangGraph

Unstructured - Pros & Cons

Pros

  • ✓Broadest connector library in the document ingestion category — most teams will not outgrow it
  • ✓Genuine Apache 2.0 open-source escape hatch from the managed platform
  • ✓Pre-built destination connectors mean RAG ingestion is wire-and-go for major vector stores
  • ✓Scheduling and incremental refresh are in the box, not bolted-on afterwards

Cons

  • ✗Table-extraction accuracy on truly adversarial documents trails specialists like Reducto
  • ✗Platform tier gets expensive once you turn on many connectors and high-throughput parsing
  • ✗Open-source library moves fast — production users need to pin versions deliberately
  • ✗Less precise structured-extraction API than purpose-built tools (Reducto extract, LlamaParse)

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🔒 Security & Compliance Comparison

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Security FeatureLlamaIndexUnstructured
SOC2—✅ Yes
GDPR—✅ Yes
HIPAA—✅ Yes
SSO🏢 Enterprise✅ Yes
Self-Hosted🔀 Hybrid🔀 Hybrid
On-Prem—✅ Yes
RBAC—✅ Yes
Audit Log—✅ Yes
Open Source✅ Yes✅ Yes
API Key Auth✅ Yes✅ Yes
Encryption at Rest—✅ Yes
Encryption in Transit—✅ Yes
Data Residencynot publicly confirmedconfigurable
Data Retentioncached data retained for 48 hours by default for LlamaParse, with caching optionalconfigurable
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Review LlamaIndexReview Unstructured