LlamaIndex / LlamaCloud
by LlamaIndex, Inc.
Document parsing and knowledge agents over unstructured enterprise data
LlamaIndex is an MIT-licensed Python and TypeScript framework for building LLM applications over private data, and LlamaCloud is the commercial platform on top of it. The wedge is document processing: LlamaParse handles complex PDFs, tables, charts and scanned pages that generic text extraction mangles, feeding extraction, classification and retrieval pipelines for enterprise knowledge agents.
LlamaIndex began as an open-source data framework for connecting LLMs to private data and has become two things: a widely adopted MIT-licensed framework (51.6k GitHub stars, 25M+ monthly package downloads) and LlamaCloud, the managed platform the company sells. The commercial wedge is document processing, because that is where most enterprise RAG projects actually break. LlamaParse performs agentic OCR and parsing across 130-plus file formats, handling complex layouts, embedded images, multi-page tables, charts and handwritten notes that naive text extraction flattens or loses; LiteParse is the open-source, fully local alternative for teams that cannot send documents to a cloud. Around parsing sit four more LlamaCloud services — Extract pulls structured data against a user-defined schema, Classify categorises documents using natural-language rules, Split segments them into logical sections, and Index builds the vector retrieval pipeline. Workflows, the open-source orchestration layer, is where the agent logic lives. The company reports over a billion documents processed, 300,000+ LlamaParse users and a 99.9% uptime commitment, with Jeppesen (a Boeing company) citing roughly 2,000 engineering hours saved on a unified chat framework, alongside NTT Data, Carlyle, Rakuten and Salesforce as named users. LlamaCloud is credit-metered — 1,000 credits costs $1.25, basic parsing starts at one credit per page, and agentic parsing modes cost considerably more — with a free 10,000-credit tier, $50 and $500 monthly plans, and custom Enterprise pricing that adds SSO, VPC deployment and 5x rate limits. The full platform can also be self-hosted on Kubernetes via Helm on AWS, Azure or GCP. LlamaIndex raised a $19M Series A led by Norwest in March 2025, taking total funding to $27.5M.
The platform or applied-AI team whose retrieval quality is bottlenecked on document ingestion — scanned contracts, financial filings with multi-page tables, technical manuals with diagrams — rather than on the model or the vector store.
Turns messy real-world documents into clean, structured, queryable content, so the RAG pipeline stops failing on the parsing step that generic text extraction silently corrupts.
At a Glance
- Category
- Agent Development
- Pricing
- Freemium, Subscription, Usage-based, Contact for pricing
- Target Market
- CTOs, Enterprise Developers, ML/AI Engineers, Data Engineers, Heads of AI
- Deployment
- Cloud-first, Self-hosted, Open-source, Hybrid, Multi-cloud
- Headquarters
- San Francisco, California, United States
- Customers
- 300,000+ LlamaParse users; 10,000+ organizations signed up at Series A including 90 Fortune 500 companies
Key Features
- ✓LlamaParse agentic document parsing
Parses 130+ formats including complex layouts, multi-page tables, embedded charts and handwriting that naive text extraction destroys.
- ✓LlamaExtract structured extraction
Pulls fields into a user-defined schema across large document sets, replacing brittle template and regex pipelines.
- ✓Classify and Split
Categorises documents with natural-language rules and segments them into logical sections before indexing, improving downstream retrieval precision.
- ✓LlamaCloud Index
Managed vector retrieval pipeline with connectors, so teams do not have to operate embedding and index infrastructure themselves.
- ✓Workflows orchestration
Open-source event-driven layer for building document agents and multi-step automations on top of the parsing stack.
- ✓Self-hosting and BYOC
Runs the full LlamaCloud platform on your own Kubernetes via Helm on AWS, Azure or GCP, keeping documents inside your account.
- ✓LiteParse local parsing
Open-source fully local parser for PDFs, Office files and images with bounding-box output and no cloud dependency.
Capabilities
Use Cases
- •Financial document analysis
Parse filings and reports with multi-page tables into structured data an agent can query, which naive extraction routinely corrupts.
- •Contract review and obligation extraction
Extract parties, dates, clauses and renewal terms into a defined schema across thousands of agreements for legal and procurement teams.
- •Internal engineering knowledge assistants
Jeppesen, a Boeing company, reports saving roughly 2,000 engineering hours with a unified chat framework built on LlamaIndex.
- •Insurance and claims document processing
Classify inbound documents, split them into sections and route structured fields into downstream systems without manual keying.
- •Enterprise research and report generation
Agents search across heterogeneous internal sources, synthesise findings and generate reports rather than returning raw retrieval chunks.
Ideal For
Best For
- ✓RAG pipelines over complex PDFs — financial filings, contracts, technical manuals — where table and layout fidelity determines answer quality
- ✓Structured extraction against a defined schema from large document sets, replacing brittle regex and template pipelines
- ✓Enterprise knowledge agents that must search, synthesise and report across heterogeneous unstructured data
- ✓Teams that want an MIT-licensed framework they can adopt for free and a commercial cloud they can graduate to without a rewrite
- ✓Regulated buyers who need the same platform self-hosted in their own AWS, Azure or GCP account via Helm
Not Ideal For
- ✗Teams with simple, clean, machine-generated text where a basic extractor plus a vector store does the job — LlamaCloud's credit cost only earns its keep on documents that actually defeat naive parsing
- ✗Anyone needing predictable flat-rate costs at scale: credit consumption varies sharply between basic and agentic parsing modes, which makes forecasting a large ingestion backlog genuinely hard
- ✗Developers who want a thin, transparent library — the framework's abstractions are convenient until you need to customise the retrieval pipeline, at which point you have to learn the internals
- ✗Non-Python-first shops; the ecosystem, documentation and community examples centre on Python, with TypeScript a smaller second
Integrations
Deployment
Market & Ratings
300,000+ LlamaParse users; 10,000+ organizations signed up at Series A including 90 Fortune 500 companies
Market Analysis
Pros
- ✓Best-in-class document parsing for the hard cases — multi-page tables, charts, scans, handwriting — which is where most enterprise RAG projects actually fail
- ✓Very large permissively licensed open-source base (51.6k stars, MIT, 25M+ monthly downloads) with a clear paid upgrade path and no rewrite between them
- ✓Full platform self-hosting via Helm on AWS, Azure or GCP, which clears the data-residency objection that blocks cloud-only parsers
- ✓Real named enterprise references with quantified outcomes, including Jeppesen (Boeing) at ~2,000 engineering hours saved, plus NTT Data, Carlyle, Rakuten and Salesforce
- ✓SOC 2 Type 2 and GDPR posture with RBAC and SSO, published via a dedicated trust centre
Cons
- ✗Over-abstraction is the recurring practitioner complaint: the framework 'feels like magic' until you need to customise the retrieval pipeline, at which point you have to understand internals the abstractions were hiding
- ✗Credit-metered pricing is hard to forecast — agentic parsing modes cost many times the one-credit-per-page floor, so a large backlog's cost is not knowable from page count alone
- ✗The open-source framework has churned across versions (the v0.10 release and the later monorepo overhaul both restructured packages), which dates tutorials, blog posts and StackOverflow answers quickly
- ✗Heavy overlap with LangChain/LangGraph means many teams end up running both — LangChain for orchestration, LlamaIndex for retrieval — adding dependency surface rather than replacing it
- ✗Self-hosting is not lightweight: the platform requires Postgres, MongoDB, Temporal, RabbitMQ and Redis, which is a real operational commitment for a BYOC deployment
- ✗No G2, Capterra or TrustRadius rating of substance, so the independent evidence is developer commentary rather than structured buyer reviews
Pricing
Open-source framework
$0
- ✓MIT licence
- ✓Python and TypeScript
- ✓Workflows orchestration
- ✓LiteParse local parsing
Free
$0
- ✓10,000 credits per month (~1,000 pages)
- ✓5 concurrent jobs
- ✓5 file indexes
- ✓100 users
- ✓Basic support
Starter
From $50/mo
- ✓40,000 credits included
- ✓Up to 400,000 additional credits pay-as-you-go
- ✓5 concurrent jobs
- ✓100 users
- ✓Basic support
Pro
From $500/mo
- ✓400,000 credits included
- ✓Up to 4,000,000 additional credits pay-as-you-go
- ✓20 concurrent jobs
- ✓100 file indexes
- ✓Slack support
Enterprise
Contact for pricing
- ✓Volume credit discounts
- ✓5x higher rate limits
- ✓100 concurrent jobs
- ✓Enterprise SSO
- ✓VPC and hybrid cloud deployment
- ✓Dedicated account manager
The framework is free under MIT; LlamaCloud is credit-metered at 1,000 credits per $1.25. Basic parsing starts at one credit per page, but agentic parsing modes that use an LLM for accuracy consume substantially more, so a page count alone does not predict the bill — benchmark your own document mix before committing. The free tier is a genuine 10,000 credits (~1,000 pages) per month rather than a time-limited trial. Paid tiers are $50 and $500 per month with pay-as-you-go overage capped at 400K and 4,000K credits respectively; SSO, VPC deployment, 5x rate limits and volume discounts are Enterprise-only.
Security & Compliance
Connect
Sources
This page was written from 7 sources, 5 on domains other than llamaindex.ai.
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