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Reviewed · Updated 2026-10-04

PageIndex

PageIndex is Vectify AI's vectorless RAG engine that indexes long PDFs as a tree for cited answers, available open source or as a cloud API.

Reviewed by the Conversion Gems editorial team ·
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Pricing
Freemium
Best for
Long-document RAG
Category
AI Development

PageIndex is a vectorless RAG engine from Vectify AI: it builds a tree index of long documents such as financial reports or contracts and lets an LLM reason over it to return cited answers. The open-source version is free under the MIT licence; PageIndex Cloud starts with $10 in free credits, then charges $0.01 per indexed page.

The bottom line

A strong option for cited Q&A over long PDFs, if you accept LLM-heavy retrieval.

7.5
Our score
7.5 / 10
Conversion Gems editorial verdict
Free (MIT); Cloud $0.01/page after $10 credit
Features8/10
8 - tree index, reasoning retrieval, Cloud OCR, block-level citations, MCP server and three deployment options, per pageindex.ai/developer.
Value8/10
8 - free MIT core plus $10 Cloud credits and $0.01/page indexing, but LLM costs are extra on every query.
Ease of use7/10
7 - a pip install and a few lines of Python per the quickstart, but you need API keys and coding skills.
Ecosystem7/10
7 - works with any LLM, ships an MCP server and runs on AWS, Azure or GCP for Enterprise; few named app integrations.
Support6/10
6 - docs, an active GitHub repo and demo booking; dedicated support and SLA only on Enterprise.
What it really is

An open-source and cloud document index that lets an LLM reason over a tree of sections instead of searching vector embeddings.

Our take

PageIndex is one of the more talked-about RAG approaches this year, and the repository's popularity backs that up. For long, well-structured documents where you must show exactly where an answer came from, the tree index and citations make sense. Keep two things in mind: every query still runs through an LLM you pay for, and the headline accuracy numbers are Vectify AI's own. Test it on your documents before you commit.

Why we rate it

A free MIT core, clear per-page Cloud pricing and traceable citations for long documents.

The catch

LLM costs on every query, vendor-run benchmarks, and text-only PDFs in the local version.

Best for
Q&A over financial reports and filings
Contract and manual search with citations
Teams avoiding a vector database
Not good for
Fast-changing data that needs constant re-indexing
Teams without an LLM budget
Scanned PDFs without using Cloud
Friction report
Time to value
Minutes on Cloud: install the SDK, add a PageIndex API key and an LLM key, then submit a PDF.
Scale breakpoint
Large collections that change often, since each re-indexed page is billed on Cloud and every query uses LLM tokens.
Walled garden
Low: MIT-licensed SDK, bring-your-own LLM, and an Enterprise option in your own cloud.

PageIndex pricing

OptionPriceWhat you get
PageIndex Local (open source)$0MIT-licensed SDK on your machine, text-based PDFs, your own LLM key
Cloud free start$10 in free creditsNo payment method; covers about the first 1,000 pages indexed
Cloud indexing$0.01 per pageCharged once, when a page is indexed
Cloud active pages$0.001 per page per monthFirst 1,000 active pages free each month
Cloud retrievalNo PageIndex feeUnlimited queries on active pages; your LLM provider bills you
EnterpriseQuoteRuns in your VPC on AWS, Azure or GCP, with audit logs, dedicated support and SLA

From PageIndex's pricing docs and developer page, checked 4 October 2026. Subscribers on the older Standard, Pro or Max plans keep them until they cancel.

Frequently Asked Questions

PageIndex alternatives

Other tools for document retrieval and RAG:

Pinecone: Managed vector database for similarity search, the approach PageIndex is designed to replace.
LlamaParse: Parses PDFs and dozens of other formats into clean markdown for RAG pipelines.
Unstructured: Extracts and structures data from messy documents before you index them.
LangChain: Framework for wiring LLMs, retrievers and tools into your own RAG application.
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Tags

#RAG#DocumentAI#OpenSource

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