Unstructured vs. Graphlit: PDF Extraction Tool vs. Semantic Context Platform

Here's what most teams discover too late: extracting text from PDFs is only 10% of the problem. The other 90% is turning that extracted text into something your AI can actually use — chunked appropriately, embedded for semantic search, enriched with entities, and connected to your other data sources.

Unstructured.io is excellent at that first 10%. It's one of the best PDF extraction tools available. But extraction is where it stops.

Unstructured is a specialized document parsing and extraction tool. Graphlit is a semantic infrastructure platform that handles extraction AND everything that comes after — chunking, embedding, entity extraction, knowledge graphs, hybrid search, and conversational AI.

This comparison will help you understand when you need Unstructured's focused extraction capabilities vs. when you need Graphlit's end-to-end platform.

Table of Contents

1. TL;DR — Quick Feature Comparison
2. Understanding the Platforms
3. PDF Extraction Capabilities
4. What Happens After Extraction
5. Data Connectors and Sources
6. Deployment and Integration
7. Pricing Comparison
8. Use Cases: When to Choose What
9. Final Verdict

TL;DR — Quick Feature Comparison

Understanding the Platforms

What is Unstructured?

Unstructured.io is a document parsing and extraction platform that transforms unstructured documents (PDFs, Word docs, PowerPoints, images) into structured data elements. Think of it as a specialized ETL tool for documents.

Unstructured excels at:

• Partitioning: Breaking documents into elements (titles, text, tables, images)
• OCR: Extracting text from scanned documents and images
• Table detection: Identifying and extracting tabular data
• Layout analysis: Understanding document structure

The platform offers multiple extraction strategies:

• Fast Pipeline: Rule-based NLP, optimized for speed ($1/1,000 pages)
• Hi-Res Pipeline: Model-based extraction for complex documents ($10/1,000 pages)

Unstructured is available as open-source, a hosted SaaS API, and through AWS/Azure marketplaces for VPC deployment.

What is Graphlit?

Graphlit is an end-to-end semantic infrastructure platform for AI applications. We handle the entire pipeline from data ingestion through conversational AI.

When you ingest a document into Graphlit:

1. Extraction: We parse the document using your choice of extractors (Azure AI Document Intelligence, Claude, Deepseek, or Reducto)
2. Chunking: Content is semantically chunked with layout awareness
3. Embedding: Chunks are automatically embedded for vector search
4. Entity Extraction: People, organizations, places, and events are identified
5. Knowledge Graph: Entities and relationships are connected
6. Search Index: Content is indexed for hybrid search
7. RAG Ready: Content is immediately available for AI conversations

Graphlit isn't just an extraction tool — it's the infrastructure layer that makes your documents AI-ready.

PDF Extraction Capabilities

Extraction Quality Comparison

We've benchmarked PDF extraction across multiple services. For complex documents with tables, charts, and mixed formatting:

Unstructured Hi-Res Mode: Good text extraction, but table structure can be inconsistent. Returns HTML that requires conversion to Markdown.

Graphlit LLM Mode (Claude Sonnet): Most accurate results, especially for complex tables. Native Markdown output with proper structure preservation.

For a detailed comparison with raw Markdown output samples, see our PDF extraction benchmark.

Extractor Flexibility

Unstructured provides two pipelines:

• Fast: Quick, rule-based extraction
• Hi-Res: Slower, model-based extraction for complex documents

Graphlit lets you choose your extractor based on document type and quality needs:

• Azure AI Document Intelligence: Fast, reliable, good for standard documents
• Claude/Anthropic: Best for complex layouts, tables, and visual understanding
• Deepseek: Cost-effective alternative for high-volume processing
• Reducto: Specialized for structured document extraction

This flexibility means you can optimize for cost, speed, or quality depending on your use case — without changing your integration.

What Happens After Extraction

This is where the platforms diverge completely.

Unstructured: Extraction Stops Here

Unstructured gives you extracted document elements in JSON format:

{
  "type": "Title",
  "text": "Financial Statements",
  "metadata": { "page_number": 1, "coordinates": {...} }
}

To build an AI application, you still need to:

• Convert output to your preferred format
• Implement chunking strategies
• Set up a vector database
• Generate and store embeddings
• Build entity extraction pipelines
• Create search infrastructure
• Implement RAG conversations

Unstructured focuses on doing extraction well and leaving the rest to you.

Graphlit: Complete Semantic Infrastructure

Graphlit handles the entire pipeline automatically:

import { Graphlit, Types } from 'graphlit-client';

const client = new Graphlit();

// Ingest a PDF — extraction, chunking, embedding, entities all automatic
const result = await client.ingestUri(
    "https://example.com/report.pdf",
    "Q3 Financial Report",        // name
    undefined,                    // id
    undefined,                    // identifier  
    true,                         // isSynchronous
    { id: workflowId }           // workflow (specifies extractor, enrichment)
);

// Content is now:
// - Extracted to Markdown
// - Semantically chunked
// - Embedded for vector search
// - Entity-enriched (people, organizations, places)
// - Indexed for hybrid search
// - Ready for RAG conversations

// Query contents with filters
const contents = await client.queryContents({
    types: [Types.ContentTypes.File],
    fileTypes: [Types.FileTypes.Document]
});

// RAG conversation with content context
const response = await client.promptConversation(
    "What were the key financial highlights?",
    conversationId,
    { id: specificationId }
);

No additional infrastructure. No integration work. Document goes in, AI-ready content comes out.

Data Connectors and Sources

Unstructured Connectors

Unstructured provides ingestion connectors for:

• Cloud storage (S3, Azure Blob, GCS)
• Databases (MongoDB, Elasticsearch)
• SaaS platforms (Salesforce, SharePoint)
• Local file systems

These connectors pull documents into Unstructured for extraction. You still need to build the pipeline for what happens next.

Graphlit Connectors

Graphlit provides end-to-end connectors that handle ingestion, processing, and continuous sync:

• Communication: Slack, Discord, Microsoft Teams, Email (Gmail, Outlook)
• Development: GitHub issues/discussions, Linear, Jira
• Documents: SharePoint, Google Drive, Dropbox, OneDrive, Notion
• Media: RSS feeds, podcasts, YouTube
• Web: Site crawling, URL ingestion

Each connector includes:

• Automatic sync scheduling
• Incremental updates
• Webhook support for real-time ingestion
• Content type detection and routing

Connect once, and new content flows into your knowledge base automatically.

Deployment and Integration

Unstructured Deployment Options

• Open Source: Self-host with full control (free, you manage infrastructure)
• Serverless API: Hosted SaaS ($1-10/1K pages)
• Platform: Enterprise SaaS with workflow orchestration
• Marketplace: Deploy in your AWS/Azure VPC

The open-source option is powerful for teams with DevOps resources who want to control costs at scale.

Graphlit Deployment

• Cloud-native SaaS: Fully managed, no infrastructure to operate
• Multi-tenant isolation: Per-user data separation built-in
• Global availability: Edge deployment for low latency

Graphlit focuses on being infrastructure you don't have to think about. No databases to scale, no models to deploy, no pipelines to maintain.

Pricing Comparison

Unstructured Pricing

Graphlit Pricing

Important: Graphlit credits include extraction, embedding, entity extraction, search, and conversations. Unstructured pricing covers extraction only — you'll have separate costs for vector databases, embedding APIs, and other infrastructure.

Total Cost of Ownership

For a typical RAG application processing 10,000 pages/month:

Unstructured-based stack:

• Unstructured Hi-Res: ~$100/month
• Vector database (Pinecone/Weaviate): $70-200/month
• Embedding API (OpenAI): ~$20/month
• Entity extraction: DIY or additional service
• Search infrastructure: DIY or additional service
• Engineering time: Significant

Graphlit:

• Pro plan: $149/month (includes everything)
• No additional infrastructure costs
• No integration engineering

Use Cases: When to Choose What

Choose Unstructured If:

• You only need extraction: Your pipeline is built, you just need better PDF parsing
• You have existing infrastructure: Vector DB, embedding pipeline, search — all set up
• You want open-source control: Self-host to minimize costs at scale
• You need VPC deployment: Compliance requires data stays in your network
• You're building custom ETL: Unstructured fits into your existing data pipeline

Choose Graphlit If:

• You need end-to-end infrastructure: Extraction through conversation, fully managed
• You want multiple extractors: Switch between Azure AI, Claude, Reducto based on document type
• You need knowledge graphs: Entity extraction and relationship mapping out of the box
• You want 30+ data connectors: Not just PDFs — Slack, GitHub, email, feeds, all integrated
• You're building AI applications: RAG, semantic search, conversational AI — ready to use
• You want to ship fast: No infrastructure to build, maintain, or scale

Integration Example

Unstructured: Extraction Only

from unstructured.partition.pdf import partition_pdf

# Extract elements from PDF
elements = partition_pdf("report.pdf", strategy="hi_res")

# Now you need to:
# 1. Convert elements to your format
# 2. Chunk appropriately
# 3. Send to embedding API
# 4. Store in vector database
# 5. Build search layer
# 6. Implement RAG logic

Graphlit: Complete Pipeline

import { Graphlit, Types } from 'graphlit-client';

const client = new Graphlit();

// Ingest PDF — extraction, chunking, embedding, entity extraction all automatic
const result = await client.ingestUri("https://example.com/report.pdf");

// Query documents with filters
const contents = await client.queryContents({
    types: [Types.ContentTypes.File],
    search: "quarterly revenue"
});

// RAG conversation ready
const response = await client.promptConversation(
    "What were the key financial highlights?",
    conversationId,
    { id: specificationId }
);

Final Verdict

Unstructured and Graphlit solve different problems:

Unstructured is a best-in-class extraction tool. If you need to parse PDFs and have infrastructure to handle everything else, Unstructured delivers excellent extraction quality with flexible deployment options.

Graphlit is semantic infrastructure. If you need to turn documents into AI-ready knowledge — with search, entity extraction, knowledge graphs, and conversational AI — Graphlit provides the complete platform.

The question isn't which is better. It's what you're building:

• Building a custom data pipeline with existing infrastructure? → Unstructured
• Building AI applications and need everything from ingestion to conversation? → Graphlit

Many teams start with Unstructured for extraction and realize they need to build significant infrastructure around it. Graphlit eliminates that infrastructure work, letting you focus on your application instead of your data pipeline.

Explore Graphlit Features:

• Document Processing — PDF extraction with multiple backends
• Content Ingestion — Multi-source data ingestion
• Building Knowledge Graphs — Automatic entity extraction
• Complete Guide to Search — Hybrid semantic search

Learn More:

• Graphlit Documentation
• Unstructured Documentation
• PDF Extraction Comparison
• Schedule a Demo
• Join our Discord

Extraction is step one. What matters is what you build with the extracted content.