Document Processing & OCR🔴Developer

Chunkr

Name: Chunkr
Brand: Chunkr
Availability: InStock

Document intelligence API that turns PDFs, images, and spreadsheets into clean, LLM-ready HTML, Markdown, or JSON.

Starting at$0

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In Plain English

Document intelligence API that turns PDFs, images, and spreadsheets into clean, LLM-ready HTML, Markdown, or JSON.

Overview

Chunkr is an Apache-licensed document intelligence API by Lumina AI that parses PDFs, images, and spreadsheets into LLM-ready Markdown/HTML/JSON, with OCR, layout detection, table extraction, and citation metadata for RAG.

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Difficulty:intermediate

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Key Features

Feature information is available on the official website.

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Pricing Plans

Self-host (OSS)

Cloud Pay-as-you-go

Usage-based

Enterprise

Custom

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Best Use Cases

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RAG pipelines that need clean chunks and citations

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Legal, financial, and insurance document workflows

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Scientific literature ingestion for research agents

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Grounded enterprise search over PDF-heavy corpora

Pros & Cons

✓ Pros

✓Citation metadata on every chunk is rare and a real win for compliance-sensitive RAG
✓Apache 2.0 license + self-host option avoids vendor lock-in
✓Table extraction with bounding boxes is materially better than generic OCR
✓Managed and self-hosted SDKs are identical — easy to migrate either direction
✓Designed for LLM consumption, not human reading — chunks drop straight into vector stores

✗ Cons

✗Per-page pricing on cloud can add up for very large corpora — model it before committing
✗Smaller community than Unstructured.io; fewer third-party connectors
✗Self-host requires GPU for the layout/OCR models — not laptop-friendly
✗Schema extraction quality is still domain-dependent and may need light fine-tuning

Frequently Asked Questions

How much does Chunkr cost?+

Chunkr pricing starts at $0. They offer 3 pricing tiers.

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Quick Info

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More about Chunkr

Pricing Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Pros & Cons

✓ Pros

✓Citation metadata on every chunk is rare and a real win for compliance-sensitive RAG
✓Apache 2.0 license + self-host option avoids vendor lock-in
✓Table extraction with bounding boxes is materially better than generic OCR
✓Managed and self-hosted SDKs are identical — easy to migrate either direction
✓Designed for LLM consumption, not human reading — chunks drop straight into vector stores

✗ Cons

✗Per-page pricing on cloud can add up for very large corpora — model it before committing
✗Smaller community than Unstructured.io; fewer third-party connectors
✗Self-host requires GPU for the layout/OCR models — not laptop-friendly
✗Schema extraction quality is still domain-dependent and may need light fine-tuning