LlamaIndex

What is LlamaIndex?

LlamaIndex is a developer toolkit for building LLM applications on top of your own documents. Its open-source, MIT-licensed framework handles ingesting files, indexing them and querying them with language models, and the company's hosted LlamaParse platform adds paid document services: Parse, Extract, Classify, Split and Index. Parsing runs in tiers (Fast, Cost-effective, Agentic and Agentic Plus), handles more than 130 file formats, spanning documents, images, spreadsheets and audio, and is available in North America and Europe. Every hosted feature is priced in credits at $1.25 per 1,000 credits. The Free plan includes 10,000 credits a month, Starter costs $50/mo for 40,000 credits and Pro costs $500/mo for 400,000, with custom Enterprise terms. Paid plans can keep running at the standard credit rate once the included credits are used. Documents are cached for 48 hours and are not used for model training. Self-hosted and bring-your-own-cloud deployment is offered to enterprise customers.

Use cases

• Parse PDFs and scans into clean text
• Extract structured fields from invoices and forms
• Build document question-answering apps
• Index company files for retrieval
• Classify and split mixed document batches
• Transcribe and parse audio files
• Build data-aware agents

Who it's for

• Python developers
• AI engineers
• Data engineers
• Enterprise teams

Why use LlamaIndex

• A free tier with monthly credits lets you test parsing and extraction on your own documents before paying.
• Tiered parsing lets you trade cost for accuracy, from 1 credit a page for Fast to 45 for Agentic Plus.
• All paid plans include every product, so you scale by credits rather than by feature.
• Admins on paid plans choose whether to pause jobs at the credit limit or keep going and set a spending limit.
• The open-source framework and the hosted platform can be used separately or together.

LlamaIndex features

• Open-source framework for ingestion, indexing and retrieval
• LlamaParse tiers: Fast, Cost-effective, Agentic and Agentic Plus
• Extract, Classify, Split and Index products on the same credit system
• 130+ supported file formats including documents, images, spreadsheets and audio
• Optional layout extraction and enriched forms output
• Webhooks for Parse, Extract, Classify and Split jobs
• llama-cloud SDK for Python and npm
• North America and EU regions
• Usage dashboard with a monthly credit view and overage controls
• Per-job caching opt-out with do_not_cache

LlamaIndex integrations

• Anthropic
• OpenAI
• Gemini
• Azure
• n8n
• MCP

How to get started with LlamaIndex

1. Create a LlamaParse account and get an API key, or install the open-source framework from the developer site.
2. Install the llama-cloud SDK for Python or npm and send files to Parse, Extract, Classify or Split.
3. Choose a parsing tier and any extras such as layout extraction.
4. Index the output and query it from your app with a model and retriever of your choice.
5. Watch credit usage in the dashboard, add webhooks for finished jobs and upgrade the plan when needed.

LlamaIndex limitations

• Free plan requests that need credits return a 402 error once the 10,000 credits are used
• Files are cached for 48 hours unless you set do_not_cache
• Self-hosting and bring-your-own-cloud are for enterprise customers
• Using the framework requires programming

LlamaIndex pricing

Yes, LlamaIndex has a free plan. Paid plans start at $50 per month, according to the official pricing page on 24 Oct 2026.

LlamaIndex plans

• Free

  $0/mo

  • 10,000 monthly credits
  • 5 concurrent parse jobs
  • 10,000 stored files / 10 GB
  • Community support

• Starter

  $50/mo

  • 40,000 monthly credits
  • 5 concurrent parse jobs
  • 50,000 stored files / 50 GB
  • Email support

• Pro

  $500/mo

  • 400,000 monthly credits
  • 20 concurrent parse jobs
  • 100,000 stored files / 100 GB
  • Priority support

• Enterprise

  Custom

  Contact sales

  • Custom credits
  • 100 concurrent parse jobs
  • Unlimited stored files
  • Dedicated support

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

LlamaIndex alternatives

The main LlamaIndex alternatives listed on Debuly are LangChain, Haystack, Unstructured, Docling, Reducto and Extend. The table compares pricing and platforms; order is A–Z.

1. 

   Docling

   Open-source document parsing library that converts PDF, Office files, audio and more into Markdown or JSON for generative AI.

   Developer platformsProductivityAutomation and agents
   Open sourceVisit Docling (official site)
2. 

   Extend

   Document processing APIs for parsing, extracting, splitting, classifying and verifying documents for AI agents and pipelines

   Developer platformsAutomation and agents
   FreemiumVisit Extend (official site)
3. 

   Haystack

   Open-source Python framework from deepset for building RAG pipelines, AI agents and multimodal apps from modular components.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Haystack (official site)
4. 

   LangChain

   Open-source framework for building LLM agents, paired with LangSmith for tracing, evals and agent deployment.

   Developer platformsAutomation and agentsCoding
   FreemiumVisit LangChain (official site)
5. 

   Reducto

   Document processing API and Studio that parses PDFs, scans and spreadsheets into structured data for AI and data teams.

   Developer platformsAutomation and agents
   Free trialVisit Reducto (official site)
6. 

   Unstructured

   Turns PDFs, Office files, emails and images into clean, chunked, embedded data that is ready for LLM and RAG pipelines.

   Developer platformsAutomation and agentsProductivity
   FreemiumVisit Unstructured (official site)

LlamaIndex FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from developers.llamaindex.ai · fetched 23 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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