Apache Tika vs Docling

Detailed side-by-side comparison to help you choose the right tool

Apache Tika

🔴Developer

Document Processing AI

Open source text extraction framework that pulls content and metadata from over 1,000 file formats. Free, battle-tested, and maintained by the Apache Software Foundation since 2007.

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Starting Price

Free

Docling

🔴Developer

Document Processing AI

IBM-backed open-source document parsing toolkit that converts PDFs, DOCX, PPTX, images, audio, and more into structured formats for RAG pipelines and AI agent workflows.

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Starting Price

Free

Feature Comparison

Scroll horizontally to compare details.

FeatureApache TikaDocling
CategoryDocument Processing AIDocument Processing AI
Pricing Plans1 tiers15 tiers
Starting PriceFreeFree
Key Features
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling

Apache Tika - Pros & Cons

Pros

  • Supports 1,000+ file formats, far more than any competitor
  • Free and open source with no usage limits
  • 17 years of production-proven stability
  • REST server mode integrates with any language
  • Active maintenance with regular releases (latest: September 2025)

Cons

  • Requires Java runtime and self-hosted deployment
  • No AI-powered structure understanding for complex PDFs
  • Lacks modern NLP features (sentiment, chunking, classification)
  • Output from tables and multi-column layouts is often messy
  • Java dependency management can create friction

Docling - Pros & Cons

Pros

  • Best-in-class PDF parsing with accurate table extraction, formula detection, and multi-column layout understanding
  • Runs entirely locally with zero cloud dependency — critical for teams handling sensitive or regulated documents
  • MIT license with no usage limits, no pricing tiers, and no vendor lock-in
  • First-class integrations with LangChain, LlamaIndex, CrewAI, and MCP protocol for immediate use in existing AI stacks
  • Actively maintained by IBM Research with aggressive release cadence and growing LF AI & Data Foundation backing

Cons

  • CPU-only parsing can be slow on large PDFs — GPU acceleration with Granite-Docling model is faster but requires more setup
  • Python-only ecosystem means Node.js or Java teams need to wrap it as a microservice or use the MCP server
  • Advanced models (Granite-Docling VLM, Heron layout) require downloading multi-hundred-MB model weights

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🔒 Security & Compliance Comparison

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Security FeatureApache TikaDocling
SOC2
GDPR
HIPAA
SSO
Self-Hosted✅ Yes✅ Yes
On-Prem✅ Yes✅ Yes
RBAC
Audit Log
Open Source✅ Yes✅ Yes
API Key Auth
Encryption at Rest
Encryption in Transit
Data Residency
Data Retentionconfigurableconfigurable
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