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DeveloperMaintained by VettedlyUpdated May 2026

LlamaIndex

Data framework for connecting private knowledge to LLM applications.

LlamaIndex helps developers ingest, index, retrieve, and query private data with language models.

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Extractable verdict

LlamaIndex fits data analysis teams

LlamaIndex helps teams building RAG over private knowledge evaluate Data framework for connecting private knowledge to LLM applications.

Best for
  • Teams building RAG over private knowledge
  • Developers connecting documents and databases to LLMs
Worst for
  • Free plan limited to 5 concurrent parse jobs
  • Free plan limited to 5 indexes and 50 files per index
Price anchor
Free (10K credits/mo); $1.25 per 1000 credits for

Vettedly take

Updated May 2026

LlamaParse delivers advanced document parsing with agentic correction loops and multi-modal understanding, backed by a free tier and flexible credit-based pricing.

The bottom line

What buyers should know

Strengths

  • Free tier with 10K credits monthly (~1000 pages)
  • Agentic OCR with auto-correction and specialized task routing
  • Supports 80+ languages and 130+ file formats
  • Advanced extraction for tables, charts, images, and structured schemas
  • No credit card required for free plan
  • Startup program with free credits and support
  • Multiple parsing tiers to optimize costs (up to 80% savings with Auto Mode)

Watch-outs

  • No dedicated security/compliance page published
  • Limited support on free plan (community only)
  • Pay-as-you-go can exceed $500-$5000/mo on mid-tier plans
  • Changelog and /security pages return 404
  • Enterprise pricing requires sales contact

vs. alternatives

How LlamaIndex stacks up

Open full comparison →
ToolPricingBest forFree plan
LL

LlamaIndex this page

Data framework for connecting private knowledge to LLM applications.

Free (10K credits/mo); $1.25 per 1000 credits for pay-as-you-go

Freemium

Teams building RAG over private knowledgeYes
OP
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Capabilities

What it does

Key features

  • Agentic document understanding with semantic parsing
  • Layout-aware extraction with precise bounding boxes
  • Specialized agents for charts, tables, text, and images
  • Auto-correction loops for messy scans and multi-modal documents
  • Structured JSON extraction to custom schemas
  • Handwriting recognition and parsing
  • Smart result caching (re-parse = 0 credits)

Best for

  • Teams building RAG over private knowledge
  • Developers connecting documents and databases to LLMs

Integrations

OpenAIAnthropicVector databasesCloud storage
LlamaIndex screenshot

Pricing

What it costs

Free (10K credits/mo); $1.25 per 1000 credits for pay-as-you-go

Freemium · Free plan available

Credit-based system where each action (parse, extract, index) costs credits. Free plan includes 10K credits. Pay-as-you-go available on Starter+ plans up to monthly limits ($500/mo on Starter, $5000/mo on Pro). Enterprise custom pricing.

Plans

PlanPriceLimitsHighlights

Free

$0

  • 10K included credits
  • 5 concurrent parse jobs
  • 5 indexes
  • 50 files per index
  • 1 user
  • 1 project
  • +Agentic OCR
  • +Basic support
  • +Community access

Starter

$40K credits included

  • 40K included credits
  • Pay-as-you-go up to $500/mo
  • 5 concurrent parse jobs
  • 50 indexes
  • 500 files per index
  • 10 users
  • +Multiple parse tiers
  • +Basic email support

Pro

$400K credits included

  • 400K included credits
  • Pay-as-you-go up to $5,000/mo
  • 20 concurrent parse jobs
  • 100 indexes
  • 2000 files per index
  • 25 users
  • 5 projects
  • +Auto Mode (saves up to 80%)
  • +Slack Connect support
  • +Extraction agents

Enterprise

Custom

  • Custom credits
  • 100 concurrent jobs
  • 5x higher rate limits
  • VPC deployment
  • Enterprise SSO
  • +Volume discounts
  • +Dedicated account manager
  • +Hybrid cloud or SaaS

Trust & security

Compliance signals

SOC 2 Type 2HIPAAGDPR

Signals extracted from the vendor's published trust pages. Verify current certifications directly with the vendor before purchase.

Vendor trust page ↗

Community signals

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