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docs/comparisons/kreuzberg-vs-markitdown.md

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+# Kreuzberg vs MarkItDown
+
+MarkItDown is a Microsoft-backed Python library that converts documents to Markdown -- purpose-built for feeding content into LLMs. Kreuzberg is a Rust-based extraction library with broader format support, native language bindings, and built-in RAG pipeline features. Both are permissively licensed and work well for AI-adjacent document processing.
+
+## At a Glance
+
+|                  | Kreuzberg                                                       | MarkItDown                                |
+| ---------------- | --------------------------------------------------------------- | ----------------------------------------- |
+| **Written in**   | Rust                                                            | Python                                    |
+| **File formats** | 90+                                                             | ~25                                       |
+| **Use from**     | Python, TypeScript, Go, Ruby, Java, C#, PHP, Elixir, Rust, Wasm | Python                                    |
+| **Output**       | Unified text, element-based, per-page JSON, Markdown            | Markdown                                  |
+| **License**      | Apache-2.0                                                      | Elastic-2.0                               |
+| **Sweet spot**   | Full extraction pipelines with chunking and embeddings          | Quick Markdown conversion for LLM context |
+
+---
+
+## How They Differ
+
+### Philosophy
+
+Different tools for different stages of a pipeline.
+
+- **Kreuzberg** -- A full extraction library. Extracts text, tables, metadata, and images. Offers multiple output formats, built-in chunking, local embeddings, and OCR. Designed to be the complete document-to-vectors pipeline.
+- **MarkItDown** -- A converter. Takes documents in, outputs Markdown. Intentionally lightweight and focused on one job: turning files into clean Markdown that LLMs can consume. Downstream processing is left to you.
+
+If you need a complete pipeline (extract, chunk, embed), Kreuzberg handles the full chain. If you just need Markdown for a prompt, MarkItDown does that with minimal setup.
+
+### Format Coverage
+
+Both cover common formats, with different long-tail reach.
+
+- **Kreuzberg (90+ formats)** -- PDFs, Office docs, spreadsheets, HTML, images (via OCR), email, archives, source code, structured data (JSON/YAML/TOML), plus LaTeX, Typst, BibTeX, Jupyter notebooks, EPUB, OrgMode, and more.
+- **MarkItDown (~25 formats)** -- PDFs, DOCX, PPTX, XLSX, HTML, XML, CSV, JSON, EPUB, Jupyter notebooks, MSG email, images, and ZIP archives. Covers the essentials.
+
+MarkItDown handles the formats you'll encounter most often. Kreuzberg also handles the ones you won't expect -- until you do.
+
+### OCR
+
+Different approaches to image-based text extraction.
+
+- **Kreuzberg** -- Tesseract + native PaddleOCR (ONNX-based, runs locally, no Python needed). Multi-backend pipeline with automatic quality-based fallback. All processing happens on your machine.
+- **MarkItDown** -- Can use **Azure Document Intelligence** for image and PDF extraction. Powerful when enabled, but requires an Azure account and sends documents to Microsoft's cloud. Without it, image OCR is limited.
+
+### Language Support
+
+A significant difference in how you integrate each tool.
+
+- **Kreuzberg** -- Native bindings for **16 languages**. Same performance and API from Python, TypeScript, Rust, Go, Java, Kotlin, C#, Ruby, PHP, Elixir, R, Dart, Swift, Zig, C, or Wasm in the browser.
+- **MarkItDown** -- **Python only**. If your backend is in Go or TypeScript, you'd need to wrap MarkItDown in an HTTP service or call it as a subprocess.
+
+### Downstream Processing
+
+What happens after extraction.
+
+- **Kreuzberg** -- Built-in **chunking** (recursive, semantic, markdown-aware), **local embeddings** (ONNX models, no API keys), token reduction, keyword extraction, and quality processing. Extraction output is ready for RAG pipelines.
+- **MarkItDown** -- Outputs Markdown and stops. Chunking, embeddings, and vector storage are your responsibility. This is by design -- it's a converter, not a pipeline.
+
+---
+
+## When to Use Kreuzberg
+
+- You need a **complete pipeline** from document to embeddings
+- Your stack includes **Go, TypeScript, Ruby, Java**, or other languages beyond Python
+- You want **local OCR** without cloud API dependencies
+- You need to handle **niche formats** like LaTeX, Typst, email files, or archives
+- You need **multiple output formats** (text, elements, per-page JSON) not just Markdown
+
+## When to Use MarkItDown
+
+- You just need **clean Markdown** to feed into an LLM prompt
+- You're in a **Python-only** environment and want the simplest possible setup
+- You're already using **Azure Document Intelligence** and want to leverage it for OCR
+- Your use case is **document-to-prompt conversion** without further processing
+- You value **minimal dependencies** and a small footprint
+
+---
+
+!!! Info "Benchmarks"
+
+    For extraction speed and quality comparisons between Kreuzberg and MarkItDown, see the [live benchmark dashboard](https://kreuzberg.dev/benchmarks).