PageIndex is live · free plan, paid from $0.001 per active page · last verified 24 Oct 2026 · viability monitor
PageIndex
PageIndex delivers tree-structured document retrieval with verifiable, page-cited answers from long PDFs
PageIndex is the pick when auditability outranks latency. The vendor reports 98.7% retrieval accuracy on FinanceBench versus 50.0% for approximate vector search, and on a 420-page document a tree-walk query costs 1x against 16x for pasting the whole file into an LLM. The local SDK and the open-source Flash indexer make it viable for teams that cannot ship documents to a third party — a narrow but real advantage over most hosted document AI.
Verdict generatedVerified 6d ago · liveness 69/100 · cite: rightaichoice.com/tools/pageindex
- Developers building vectorless RAG without embeddings or a vector DB
- Compliance, legal and finance teams needing answers auditable to source pages
- Teams with data-residency rules that require document AI on their own machines
- Researchers and analysts working through 100+ page PDFs and technical manuals
- High-QPS applications where sub-100ms retrieval latency is non-negotiable
- Simple keyword lookup over short documents that never needed retrieval infrastructure
- Teams with a tuned vector DB stack and no citation-accuracy complaints
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Skip PageIndex if your workload is high-QPS search where sub-100ms latency is the requirement, or if your documents are short enough that keyword lookup already answers them.
Indexing is charged once per page at $0.01, so a 100,000-page corpus costs about $1,000 to bring in before you have answered a single query.
PageIndex Cloud is usage-billed: $0.01 per page indexed once, then $0.001 per active page per month with the first 1,000 pages free, $10 in starting credits and no minimum monthly cost. That suits pilot budgets and mid-size corpora. Local is free and open source, so teams that can absorb their own compute pay nothing to Vectify AI. Very large always-on corpora shift the arithmetic toward whoever can host retrieval cheapest at scale.
In short
PageIndex — PageIndex delivers tree-structured document retrieval with verifiable, page-cited answers from long PDFs. Best for Developers building vectorless RAG without embeddings or a vector DB, Compliance, legal and finance teams needing answers auditable to source pages, Teams with data-residency rules that require document AI on their own machines. Free to start; paid plans from $0.001 per active page.
What is PageIndex?
PageIndex — PageIndex delivers tree-structured document retrieval with verifiable, page-cited answers from long PDFs. Best for Developers building vectorless RAG without embeddings or a vector DB, Compliance, legal and finance teams needing answers auditable to source pages, Teams with data-residency rules that require document AI on their own machines. Free to start; paid plans from $0.001 per active page.
PageIndex is a document AI platform and SDK built for files that break naive chunk-and-embed retrieval: multi-hundred-page annual reports, technical manuals, financial filings, legal contracts. Instead of slicing documents into chunks and storing embeddings, it constructs a hierarchical tree index from the document's own structure — headings, sections, page numbers — and lets an LLM agent walk that tree, reading only the pages on the path it needs. Answers carry citations: in Cloud a citation names the page and layout block and resolves to a bounding box your viewer can highlight; the local SDK cites to the page. The developer flow is short. Install with `pip install -U pageindex`, bring your own model key, index a document, and query it — the SDK shows gpt-5.6-sol, claude-opus-5 and glm-5.3-flash as interchangeable choices, and retrieval accuracy rises with the model you pick without re-indexing or re-embedding. PageIndex Flash, the open-source tree indexer (August 2026), runs indexing entirely on local machines and handles 1,000 pages in minutes for roughly a dollar. The File System layer, shipped May 2026, extends vectorless retrieval from single documents to file-system-scale corpora. Three entry points cover most teams. The Chat app gives explainable answers with page-level citations for analysts who just need to interrogate a PDF. The developer API plus MCP integration slots into RAG pipelines without embeddings or a vector database. Enterprise deployments run inside your own VPC on AWS, Azure or GCP with audit logs and an SLA. Cloud billing is usage-based at $0.01 per page indexed (once) plus $0.001 per active page per month after the first 1,000 free pages, with no retrieval fees; new accounts start with $10 in credits and no payment method. Local stays free and open source. Against Pinecone-plus-tuned-RAG or a hosted document AI suite, PageIndex trades raw lookup latency for traceable, page-anchored answers.
Key features
- Tree-structured document indexing without chunking or embeddings
- Page-level citations with block references and bounding boxes in Cloud
- PageIndex Flash: open-source local tree indexing, 1,000 pages in minutes for about a dollar
- Image understanding for charts and tables in documents
- Python SDK (pip install -U pageindex) for indexing and document chat
- MCP (Model Context Protocol) integration for LLM agents
- Bring your own LLM — switch models for higher retrieval accuracy with no re-index
- PageIndex Local: open-source SDK running on your own machine
- PageIndex Cloud: managed indexing, storage and retrieval, free to start
- PageIndex Enterprise: deploy inside your own VPC on AWS, Azure or GCP
- File System layer scaling vectorless retrieval to file-system-scale corpora
- Text-based PDFs read directly, no OCR pipeline required
- Streaming responses from document chat
- Usage-based Cloud billing with per-page indexing and active-page pricing
- LLM agent navigates the document tree using full conversational context
Behind the verdict
Most retrieval stacks are built for short, uniform text. Feed them a 400-page 10-K with nested tables and the chunk boundaries land in the wrong places, embeddings blur figures together, and the citation you hand an auditor points at something vaguely related. PageIndex attacks that specific failure. The tree index mirrors the document's own hierarchy, so the agent navigates to the relevant section the way a person flips to a chapter, then reads only the pages on that path. The cost model follows from that. You pay $0.01 per page to index once, then $0.001 per active page per month, and nothing per retrieval — so a document you query fifty times costs the same as one you query twice. Pasted-context workflows scale the opposite way, which is why the 1x-versus-16x comparison on that 420-page file matters more than it first appears. Pick this when answers need to be defensible. Compliance review, financial diligence, contract analysis, engineering documentation — anywhere someone will ask "which page says that?" and needs a real answer with a bounding box. The open-source Flash indexer and the local SDK also make it a genuine option for teams whose data cannot leave their machines, which rules out most managed document AI. Pass when latency is the product. Tree walks are not vector lookups; if you need sub-100ms retrieval at high query-per-second volume, a tuned Pinecone pipeline is still the better tool, and the citation advantage rarely justifies the regression. Skip it too for short documents and simple keyword lookup — building retrieval infrastructure for a two-page memo is wasted effort. There is migration friction worth naming. If you already run a vector database with acceptable accuracy and no complaints from users, swapping in a tree index means rethinking
Models under the hood
as of 2026-10-24
Limitations
- Retrieval runs through an LLM, so per-query latency and token cost sit above vector search — the trade is accuracy and explainability for speed.
- The project launched in 2025; docs and community are still filling in.
- PageIndex needs an external LLM for retrieval and answer generation, so bring an API key or a local model.
- Block-level citations are a PageIndex Cloud feature — run locally and citations stop at the page.
- Vector DBs remain the better fit for sub-100ms lookups and battle-tested infrastructure at scale.
as of 2026-10-04
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Is PageIndex still active?
PageIndex is live · free plan, paid from $0.001 per active page · last verified 24 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is PageIndex? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What's new in PageIndex
Checked 7 days agoAcross the latest 2 updates: 2 launches.
PageIndex Flash: Fast Local Tree Indexing for PDFs
PageIndex Flash builds hierarchical tree indexes from PDF layout entirely on local machines, indexing 1,000 pages in minutes for about a dollar, and ships in the PageIndex SDK.
PageIndex File System: Massive-Scale Document Search
The File System layer extends PageIndex's vectorless retrieval from single documents to file-system-scale corpora.
What do users say about PageIndex?
What people actually say about PageIndex — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about PageIndex— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified PageIndex 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed SSL certificate
- — changed who should skip it, features, description, summary, who it suits, pricing tiers, our verdict
- — changed pricing model
- — changed setup time, who it suits, description, our verdict, features, hidden costs, limitations, switching in, switching out, models, who should skip it, pricing tiers, pricing fit, summary, use cases
- — changed pricing page
- — changed features, our verdict, summary, who it suits, integrations, who should skip it, description
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does PageIndex cost in 2026?
PageIndex: Free to start; paid plans from $0.001 per active page. Pricing last verified 24 Oct 2026.
Plans compared
Every published PageIndex tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free Starter | Free | — | A developer or analyst testing tree-based retrieval on a handful of PDFs before committing budget.Adds: Free entry point: $10 in credits, no payment method required, covering the first 1,000 pages indexed plus 1,000 active pages free each month with unlimited retrieval on those pages. |
| Usage Billing | $0.001 per active page (intro $0.01)Vendor: “$0.01 per page indexed, then $0.001 per active page per” | usage | Teams running steady document volume who want to pay per page rather than per seat.Adds: Meters usage beyond the free allowance: $0.01 per page indexed once, $0.001 per active page per month after the first 1,000, no retrieval fee, and you pay your LLM provider directly. |
| Enterprise | Custom | quote | — |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where PageIndex's pricing actually pencils out — and where peers do it cheaper.
PageIndex Cloud is usage-billed: $0.01 per page indexed once, then $0.001 per active page per month with the first 1,000 pages free, $10 in starting credits and no minimum monthly cost. That suits pilot budgets and mid-size corpora. Local is free and open source, so teams that can absorb their own compute pay nothing to Vectify AI. Very large always-on corpora shift the arithmetic toward whoever can host retrieval cheapest at scale.
Has PageIndex's pricing changed?
No plan price for PageIndex has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick PageIndex?
Pick PageIndex if you are Developers building vectorless RAG without embeddings or a vector DB, Compliance, legal and finance teams needing answers auditable to source pages, Teams with data-residency rules that require document AI on their own machines, Researchers and analysts working through 100+ page PDFs and technical manuals.
Look elsewhere if you are High-QPS applications where sub-100ms retrieval latency is non-negotiable, Simple keyword lookup over short documents that never needed retrieval infrastructure, Teams with a tuned vector DB stack and no citation-accuracy complaints, Workflows built around image-heavy or handwritten scans that need a full OCR pipeline.
Skip it if Skip PageIndex if your workload is high-QPS search where sub-100ms latency is the requirement, or if your documents are short enough that keyword lookup already answers them.
GeneratedReal-world workflow fit
Concrete scenarios for the personas PageIndex actually fits — and what changes day-one when you adopt it.
Install the SDK, submit a 120-page annual report to Cloud, and stream answers through the MCP integration while gpt-5.6-sol runs the tree search.
Outcome: A working document-chat endpoint in an afternoon, with citations that resolve to page and block rather than a bare page number.
Ask where Asia segment revenue is stated and how it changed year over year, then hand the answer to a reviewer.
Outcome: Two cited blocks — page 12 and page 13 — with bounding boxes the viewer highlights, so the reviewer checks the source instead of re-reading the document.
Run PageIndex Local over internal PDFs with an in-house model key, indexing with the open-source Flash indexer.
Outcome: Documents never leave the disk, no vector database is deployed, and citations stop at page level.
Use cases
- Extract precise answers from a 120-page annual report and trace each one to the page and block it came from
- Build a chatbot over academic research that keeps full conversation context across multi-hundred-page PDFs
- Run domain-specific Q&A over legal documents with verifiable citations a reviewer can open
- Index and search financial filings on a laptop with the local SDK and no vector database
- Scale vectorless retrieval across millions of files with the File System layer
- Give an existing LLM agent a persistent document structure through the MCP integration
Setup time & first value
How long it actually takes to get something useful out of PageIndex — broken out by persona, not the marketing-page minute.
Developers: minutes to a first indexed document — pip install, a PageIndex API key from the Developer Dashboard, a model key, then submit and query. Analysts using the Chat app: near-immediate, upload and ask. Teams deploying Enterprise inside their own VPC on AWS, Azure or GCP: plan days, since that path involves cloud infrastructure and audit-log configuration.
Switching to or from PageIndex
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a vector-DB RAG stack: replace chunk-and-embed retrieval with tree indexing and keep your LLM, since no re-embedding or re-index is needed to change models.
- →From full-document LLM prompting: index once and query the tree — the vendor measures 1x per-question cost versus 16x for reading the whole file on a 420-page document.
- →From OCR pipelines: text-based PDFs are read directly, and Cloud handles scanned files and images, so the separate OCR step can be dropped.
- ↗To a vector database: rebuild embeddings from your source documents; the tree index is not a vector store and will not port over.
- ↗To full-document prompting: no export needed, but you give up the roughly 16x per-query cost advantage the tree walk provides on long documents.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “PageIndex”, and we withheld 6: 6 could not be judged, because “PageIndex” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about PageIndex.
Official links
What are the best PageIndex alternatives?
The closest alternatives to PageIndex we list are PDF Pals, Myreader AI and Google NotebookLM. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| PDF Pals | Paid | Pricing not published | — |
| Myreader AI | Freemium | Pricing not published | — |
| Google NotebookLM | Free | Free | — |
Alternatives to PageIndex
View all
PDF Pals
Chat with PDFs on your Mac — an AI PDF assistant with OCR, page-cited answers, and your own API keys.
Myreader AI
Chat with your PDFs, ebooks and YouTube links — get cited answers that jump to the exact page.
Google NotebookLM
Google Gemini Notebook (formerly NotebookLM) delivers source-grounded AI research with cited answers and Audio Overview podcasts.
Tools that pair well with PageIndex
Common stack mates teams adopt alongside PageIndex, with the specific reason each pairing earns its keep.
PDF Pals
Chat with PDFs on your Mac — an AI PDF assistant with OCR, page-cited answers, and your own API keys.
Myreader AI
Chat with your PDFs, ebooks and YouTube links — get cited answers that jump to the exact page.
Google NotebookLM
Google Gemini Notebook (formerly NotebookLM) delivers source-grounded AI research with cited answers and Audio Overview podcasts.
PageIndex vs its closest rival
PageIndex's closest head-to-head is with Screenplayiq — ScreenplayIQ and PageIndex serve completely different needs. ScreenplayIQ is for screenwriters/producers who want data-driven box office predictions from their scripts. PageIndex is for developers/enterprises needing auditable, vectorless document retrieval. Choose based on your domain: entertainment versus document AI. Verdict generated
Head-to-head comparisons
Pageindex vs Screenplayiq
ScreenplayIQ and PageIndex serve completely different needs. ScreenplayIQ is for screenwriters/producers who want data-driven box office predictions from their scripts. PageIndex is for developers/enterprises needing auditable, vectorless document retrieval. Choose based on your domain: entertainment versus document AI.
GeneratedPageindex vs Praktika
Praktika and PageIndex serve completely different needs. Praktika is the choice for language learners wanting conversational speaking practice with AI tutors, while PageIndex is for developers and enterprises needing precise, auditable document retrieval without vector databases. Your decision hinges on whether you need to improve your spoken language fluency or extract accurate answers from complex documents.
GeneratedFrequently asked questions
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