Last updated 08-13-2026
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LlamaIndex
Developers building LLM apps use LlamaIndex to parse messy documents before retrieval or agent steps. LlamaParse turns PDFs, scans, tables, charts, and handwritten notes into structured markdown and JSON, then adds schema-based extraction, classification, splitting, and indexing on top. Open-source LlamaIndex and Workflows libraries cover the same RAG building blocks for teams that want to self-host pieces of the stack.
Where generic OCR tools stop at plain text, LlamaParse routes pages through task-specific agents with auto-correction loops, so messy layouts survive as clean markdown or JSON without custom templates. Auto Mode picks a parse tier per page and can cut credit spend by up to 80%, which matters when you are processing invoices, claims, or technical manuals at volume rather than one-off uploads.
Teams in finance, insurance, manufacturing, and healthcare use LlamaIndex to feed LLMs and document agents with citation-backed fields instead of brittle copy-paste. Developers get Python and TypeScript SDKs, a REST API, and optional VPC deployment when SaaS data residency is not enough.
Free tier includes 10,000 credits per month, roughly 1,000 pages at basic parse rates
Parses 130+ file types including PDF, Office docs, spreadsheets, and images
Agentic parse tiers with Auto Mode routing that can save up to 80% on credits
LlamaExtract returns field-level confidence scores and citations tied to source pages
Enterprise plans support VPC deployment with SOC 2, HIPAA, and GDPR compliance
Open-source LiteParse runs locally with no cloud tokens for PDF and Office parsing
Concurrent parse jobs scale from 5 on Free to 100 on Enterprise plans
Handles tables, charts, handwriting, and irregular layouts that break traditional OCR.
Credit-based pricing with a generous 10,000 free monthly credits for testing pipelines.
Combines hosted LlamaParse with widely used open-source LlamaIndex libraries.
Field-level citations and confidence scores on LlamaExtract outputs aid audit trails.
SOC 2, HIPAA, and GDPR compliance plus VPC options suit regulated enterprise teams.
Credit costs rise quickly on agentic parse tiers for complex multi-page documents.
Starter and Pro list prices require contacting sales or calculating from credit packs.
Full platform value sits in LlamaParse cloud; local LiteParse lacks agentic extraction features.
Is LlamaIndex open source?
LlamaIndex ships both open-source libraries and a commercial LlamaParse platform. The LlamaIndex and Workflows repos are open source for building RAG apps, while LlamaParse is a hosted product with 10,000 free monthly credits for new users.
How much does LlamaParse cost per page?
LlamaIndex bills through credits where 1,000 credits equal $1.25. Basic parsing can cost as little as 1 credit per page, while layout-aware agentic modes use more credits for higher accuracy on complex documents.
What file formats does LlamaIndex support?
LlamaParse on LlamaIndex supports 130+ formats including PDF, DOCX, PPTX, XLSX, CSV, PNG, and JPG. Output can be markdown, plain text, JSON, XLSX, HTML tables, or annotated PDF depending on your pipeline.
Does LlamaIndex offer on-premises deployment?
LlamaIndex offers LlamaParse SaaS on secure cloud tenants with optional 48-hour cached data retention. Enterprise customers can deploy in a private VPC across major cloud providers so data never leaves their tenant.
What compliance certifications does LlamaIndex have?
LlamaParse through LlamaIndex holds SOC 2 Type II, GDPR, and HIPAA certifications. Enterprise plans can add SSO, MFA, custom BAAs, and hybrid cloud options for regulated industries.
What is included in the LlamaIndex free plan?
The LlamaIndex Free plan includes 10,000 credits per month, agentic OCR parsing, structured schema extraction, up to 5 concurrent parse jobs, 5 indexes with 50 files each, and support for up to 100 users in one project.