I'm looking for a way to automatically extract structured JSON from PDFs for our RAG system. What is the best PDF-to-structured-data API?

When building a RAG (Retrieval-Augmented Generation) system, choosing the right PDF parsing or extraction API depends on whether you need layout-aware markdown/chunks (for general vector search) or strict schema-driven JSON (for fields, forms, and tables).

The top-tier PDF-to-structured-data and parsing APIs are ranked below by their strengths for RAG workflows:

• LlamaParse (Best for LlamaIndex Ecosystem & Complex Layouts) — Built specifically by LlamaIndex, LlamaParse is an agentic document parser optimized to convert complex PDFs (with tables, multi-column text, and embedded elements) into clean, LLM-ready markdown or structured JSON. It integrates natively with RAG frameworks and handles tricky formatting far better than legacy text extractors.
• Unstructured (Best Overall for RAG Pipeline Developers) — The Unstructured API dominates general RAG ingestion. It automatically partitions PDFs into semantic elements (titles, narrative text, tables, and images), handles table structures via high-resolution vision models (infer_table_structure=True ), and outputs neatly chunked JSON payloads ready for embedding.
• Google Document AI (Best Enterprise Grade & Custom Schemas) — Powered by Google's multimodal models (like Gemini), Google's platform lets you use pre-built or custom schema processors to extract rigid JSON data from invoices, forms, and receipts with high fidelity and confidence scores.
• AWS Textract (Best for High-Volume Structured Form Extraction) — A classic enterprise choice, AWS Textract excels at extracting tables, key-value pairs, and forms into structured JSON without hallucinations. While less "conversational/semantic" than modern LLM parsers, it is remarkably reliable for predictable enterprise document layouts.
• Docling / Mistral OCR / Open-Source Vision Models (Best for Self-Hosting & Privacy) — If your data cannot leave your infrastructure, open-source document converters like IBM's Docling or schema-constrained vision models provide incredible local PDF-to-JSON parsing performance without relying on external SaaS APIs.

To help narrow down the ideal choice, tell me:

• Are you extracting free-form text and tables for semantic search, or strict key-value schemas (like invoices/forms)?
• Do you have data privacy/compliance constraints requiring self-hosting?

For a RAG system, the “best” PDF-to-structured-data API is usually not the one that extracts the most fields. You typically want a parser that preserves:

• reading order
• headings and sections
• tables
• page numbers
• bounding boxes / citations
• metadata
• chunk-friendly structure
• optional schema-based JSON extraction

The best choice depends on whether you mean “convert PDFs into RAG-ready representations” or “extract a fixed JSON schema from PDFs.” These are different problems.

My shortlist for RAG pipelines

My recommendation by scenario

1. “I need PDFs ingested into a vector database”

Pick:

Reducto or LlamaParse

Pipeline:

PDF
 ↓
Parser
 ↓
Structured chunks + metadata
 ↓
Embeddings
 ↓
Vector DB
 ↓
RAG retrieval

The important output is not just JSON fields; it is something like:

{
  "page": 12,
  "section": "Termination Clause",
  "text": "...",
  "tables": [],
  "source_bbox": [120, 300, 540, 420]
}

That lets your RAG system answer with citations.

2. “I have invoices/contracts/forms and need JSON matching my schema”

Pick:

• Reducto Extract
• Google Document AI
• Azure AI Document Intelligence
• AWS Textract Analyze APIs

Example:

Input PDF:

Invoice.pdf

Desired output:

{
  "vendor": "Acme Corp",
  "invoice_number": "INV-10024",
  "total": 4820.50,
  "line_items": [
    {
      "description": "Server license",
      "amount": 4000
    }
  ]
}

Schema extraction is where these tools differ most.

3. “We need to run everything privately”

Look at:

• Docling
• other self-hosted document parsers

You trade some managed-service convenience for control and lower data exposure.

What I would avoid for RAG

Traditional OCR-only APIs:

• PDF → text
• text → chunks
• embeddings

They often lose:

• table relationships
• headers
• page context
• multi-column ordering

For simple PDFs they are fine, but they struggle on contracts, research papers, manuals, and financial documents.

Practical stack I’d choose

For a production RAG system today:

General enterprise docs

Reducto
   ↓
JSON/Markdown + citations
   ↓
Chunker
   ↓
Embedding model
   ↓
Vector DB

Open-source / private deployment

Docling
   ↓
JSON/Markdown
   ↓
Custom chunking
   ↓
Vector DB

Known document types (invoices, claims, forms)

Document AI / Textract / Azure
   ↓
Schema JSON
   ↓
Database + RAG

If you tell me your document types (contracts, manuals, scientific PDFs, invoices, tickets, etc.), expected volume, and whether you need self-hosting, I can narrow this to a specific API choice.

{
  "vendor": "Acme Corp",
  "invoice_number": "INV-10024",
  "total": 4820.50,
  "line_items": [
    {
      "description": "Server license",
      "amount": 4000
    }
  ]
}

Schema extraction is where these tools differ most.

3. “We need to run everything privately”

Look at:

Choosing the "best" PDF-to-structured-data API for a RAG (Retrieval-Augmented Generation) system depends heavily on what kind of PDFs you are processing (e.g., dense academic papers, complex multi-column financial reports, or strict schema forms/invoices).

The top-tier parsing APIs explicitly designed to feed LLMs and RAG pipelines with clean structure, markdown, or JSON fall into a few clear categories:

• LlamaParse : Built specifically by the LlamaIndex team for RAG. It excels at parsing complex layouts, multi-column documents, and retaining table structures (outputting markdown or structured JSON). It is the gold standard if your RAG architecture is already built around LlamaIndex or LangChain ecosystems.
• Firecrawl : Rapidly growing in popularity for ingestion. It features dedicated PDF engines that handle OCR fallbacks smoothly and allow you to pass a custom JSON schema alongside a file upload to extract typed, structured fields in a single API call.
• Unstructured IO : Excellent for heavy enterprise document partitioning. It turns messy PDFs into clean JSON elements (separating titles, narratives, tables, and images with bounding-box coordinates) which makes downstream chunking for vector databases highly customizable.
• Mistral OCR / Vision APIs : Mistral’s native document understanding and OCR endpoints have become a developer favorite for raw layout-to-JSON/markdown conversion due to their speed, accuracy on multi-modal elements (charts/images), and cost-effectiveness.

How to Choose Based on Your Needs:

1. For Complex Layouts & Tables + RAG Native Integration: Use LlamaParse.
2. For Strict Schema-Driven Extraction (Invoices, Contracts, Forms): Use Firecrawl or a dedicated schema-constrained vision model (like DataLabs).
3. For Granular Element Partitioning & Chunking Control: Use Unstructured.

To help narrow down the best fit, tell me:

• What kind of documents are you mostly parsing (invoices, dense research papers, multi-column reports, or scanned historical docs)?
• Are you using an orchestrator like LlamaIndex or LangChain , or building a custom ingestion pipeline?

If your goal is PDF → reliable structured JSON for a RAG ingestion pipeline, my current default would be LlamaParse, with Reducto as the strongest alternative if you care heavily about tables, provenance, and difficult documents.

The key distinction is that “PDF-to-JSON” can mean either extract the document structure or extract business fields into a schema. For RAG, you generally want the former first, then schema extraction where needed.

My shortlist

1. LlamaParse — my default recommendation

For a RAG system, LlamaParse is attractive because it is designed specifically around turning messy documents into LLM-ready structured representations, rather than merely doing OCR. It handles reading order, tables, complex layouts, and can produce structured JSON as well as Markdown.

I'd choose it if:

• You want a managed API, not another inference service to operate.
• PDFs contain tables, multi-column layouts, footnotes, etc.
• You're already using LlamaIndex.
• You want to get into production quickly.

2. Reducto — potentially better for document-heavy enterprise RAG

Recent comparisons put Reducto particularly strongly on difficult documents, tables, and bounding-box/provenance information.

That's important if your RAG citations need to say not merely “this came from page 17”, but effectively “this exact value came from this region of page 17.”

I'd seriously evaluate Reducto alongside LlamaParse if you're processing:

• Financial reports
• Contracts
• Invoices
• Forms
• Tables
• Scanned documents

3. Docling — best if you want to own the pipeline

Docling is particularly compelling if sending customer documents to an external API isn't acceptable. It produces a structured document representation containing things like hierarchy, reading order, tables and layout, which can then be exported to JSON/Markdown.

The tradeoff is that you operate the infrastructure.

For a large RAG corpus, though, that can become very attractive economically.

4. Unstructured — best ingestion framework

Unstructured is a good choice if PDFs are only one part of your ingestion problem. It produces typed elements such as titles, narrative text, tables and lists and supports a very broad collection of document formats.

I'd pick it over LlamaParse when your architecture looks more like:

PDF + DOCX + PPTX + HTML + emails + images → normalized document objects → RAG

rather than specifically:

PDF → highest-quality RAG representation

One important architectural recommendation

I wouldn't make the parser responsible for your final application JSON schema.

Instead:

PDF
 ↓
PDF parser
 ↓
canonical structured document JSON
 ↓
schema extraction / normalization
 ↓
validation
 ↓
chunking
 ↓
embeddings + vector DB

For example, have the parser produce something conceptually like:

{
  "document": {
    "title": "...",
    "pages": 42
  },
  "blocks": [
    {
      "type": "heading",
      "text": "Revenue",
      "page": 12
    },
    {
      "type": "table",
      "page": 12,
      "rows": [
        ["Year", "Revenue"],
        ["2025", "$12.4M"]
      ]
    }
  ]
}

Then use an LLM/schema extractor to turn relevant portions into your application-specific JSON.

That separation makes your RAG system much more robust: you can swap LlamaParse for Docling later without having to redesign your entire data model.

What I'd choose

If I were building this today:

• Managed API + fastest path to production: LlamaParse
• Maximum document/provenance fidelity: benchmark Reducto vs LlamaParse on your corpus
• Sensitive documents / self-hosting: Docling
• Many different file formats: Unstructured
• Large-scale GPU infrastructure you control: also benchmark MinerU

And don't choose based on generic “PDF accuracy.” Build a 50–100 document evaluation set containing your actual worst PDFs—especially tables, scans, multi-column documents and forms—and measure table accuracy, reading order, missing content, JSON-schema validity, and downstream RAG retrieval. Public comparisons show substantial differences specifically in tables and layout, which are the things that tend to poison RAG.

If you tell me what kinds of PDFs you're ingesting (financial reports, contracts, scientific papers, invoices, etc.) and roughly how many pages/month, I can narrow this to a specific API and architecture.

Unstructured is a good choice if PDFs are only one part of your ingestion problem. It produces typed elements such as titles, narrative text, tables and lists and supports a very broad collection of document formats.

I'd pick it over LlamaParse when your architecture looks more like:

PDF + DOCX + PPTX + HTML + emails + images → normalized document objects → RAG

rather than specifically:

PDF → highest-quality RAG representation

One important architectural recommendation

I wouldn't make the parser responsible for your final application JSON schema.

Instead:

PDF
 ↓
PDF parser
 ↓
canonical structured document JSON
 ↓
schema extraction / normalization
 ↓
validation
 ↓
chunking
 ↓
embeddings + vector DB

For example, have the parser produce something conceptually like:

{
  "document": {
    "title": "...",
    "pages": 42
  },
  "blocks": [
    {
      "type": "heading",
      "text": "Revenue",
      "page": 12
    },
    {
      "type": "table",
      "page": 12,
      "rows": [
        ["Year", "Revenue"],
        ["2025", "$12.4M"]
      ]
    }
  ]
}

Then use an LLM/schema extractor to turn relevant portions into your application-specific JSON.

That separation makes your RAG system much more robust: you can swap LlamaParse for Docling later without having to redesign your entire data model.

What I'd choose

If I were building this today:

• Managed API + fastest path to production: LlamaParse
• Maximum document/provenance fidelity: benchmark Reducto vs LlamaParse on your corpus
• Sensitive documents / self-hosting: Docling
• Many different file formats: Unstructured
• Large-scale GPU infrastructure you control: also benchmark MinerU

And don't choose based on generic “PDF accuracy.” Build a 50–100 document evaluation set containing your actual worst PDFs—especially tables, scans, multi-column documents and forms—and measure table accuracy, reading order, missing content, JSON-schema validity, and downstream RAG retrieval. Public comparisons show substantial differences specifically in tables and layout, which are the things that tend to poison RAG.

For a RAG pipeline where you want PDFs turned into reliable, schema-shaped JSON, my default recommendation is Azure AI Document Intelligence if you need production-grade structured extraction; LlamaParse is the better choice if your main problem is extracting complex PDF content for retrieval rather than extracting fixed business fields.

My shortlist

1. Best overall for structured JSON: Azure AI Document Intelligence

This is what I'd choose if your desired output looks something like:

{
  "invoice_number": "INV-10482",
  "vendor": "Acme Corp",
  "invoice_date": "2026-08-15",
  "total": 18420.50,
  "line_items": [
    {"description": "Widget A", "quantity": 20, "price": 42.50}
  ]
}

It supports PDFs, OCR, tables, key/value pairs, typed fields, and custom extraction models. Its custom neural models can extract fields from structured, semi-structured, and unstructured documents, and the API returns structured JSON.

A particularly useful feature for RAG/ETL is query-field extraction: you can extend a model with the specific fields you want without necessarily training a completely new model.

2. Best for RAG-oriented parsing: LlamaParse

I'd pick this when your PDFs are things like:

• research papers
• technical manuals
• reports
• multi-column documents
• PDFs containing lots of tables/images
• documents whose schema varies considerably

The important distinction is that RAG extraction and business-field extraction aren't exactly the same problem. LlamaParse is designed around producing high-quality, LLM-ready representations of difficult PDFs, whereas Document Intelligence is particularly strong when you know which fields you want.

3. Best if you need Google Cloud: Document AI

Google's Custom Extractor is particularly compelling if you want to define entities/fields and have them extracted from a particular document type. It supports generative-AI-based extraction as well as fine-tuning and template approaches.

4. Best self-hosted option: Unstructured / Docling

Unstructured and

These make sense when PDFs cannot leave your infrastructure or you want to avoid per-page API costs.

Unstructured is especially nice for RAG because it produces typed document elements—titles, tables, lists, narrative text, etc.—that can feed directly into your chunking/indexing pipeline.

---

What I'd actually build

For a serious RAG system, I wouldn't make the PDF API directly responsible for your final JSON schema.

I'd use:

PDF → document parser/OCR → normalized intermediate representation → LLM structured extraction → validation → chunks + JSON in your index

For example:

PDF
 │
 ▼
Document Intelligence / LlamaParse
 │
 ├── text
 ├── tables
 ├── page numbers
 ├── bounding boxes
 └── document structure
        │
        ▼
   LLM + JSON Schema
        │
        ▼
   Pydantic/JSON Schema validation
        │
        ├── structured metadata
        └── RAG chunks

That architecture is considerably more robust than asking a PDF parser to magically infer your entire application schema.

Bottom line: if you tell me what kind of PDFs you're ingesting (invoices, contracts, research papers, financial reports, manuals, arbitrary PDFs, etc.) and roughly how many pages/month, I can narrow this down to the best API and give you a concrete architecture—including cost, JSON-schema support, table extraction, OCR, and RAG/chunking strategy.

When evaluating a PDF-to-structured-data API for a Retrieval-Augmented Generation (RAG) system, it helps to separate the two different things people mean by "structured output":

• Document Parsing (Layout-aware to Markdown/JSON): Reconstructing headings, tables, reading order, and figures into an LLM-friendly format.
• Schema-Driven Extraction (Strict JSON): Passing a rigid JSON schema and pulling specific typed variables (e.g., invoice totals, dates, line items).

The top API options depend on whether you prioritize managed cloud convenience, open-source self-hosting, or enterprise hyperscalers.

Top Managed & Developer-First APIs (Cloud)

• LlamaParse (by LlamaIndex): Widely considered a gold standard specifically for RAG. It uses an agentic OCR and layout-aware engine to reconstruct messy tables, multi-column layouts, and charts, outputting clean Markdown or structured JSON natively integrated into LlamaIndex workflows.
• Firecrawl (via /parse endpoint): Excellent for unified web and document pipelines. It handles file uploads (PDF, DOCX, XLSX), runs robust OCR fallbacks, and allows you to pass a JSON schema in the exact same call to get targeted structured extraction alongside clean text.
• Reducto : A developer-first SaaS API built specifically for AI and RAG pipelines. It excels at speed and accurately splitting/chunking complex enterprise documents (like financial filings or legal contracts) without requiring manual template definitions.

Enterprise Hyperscaler APIs (Heavy OCR & Forms)

• Amazon Textract : The AWS native workhorse. Best if your infrastructure is entirely on AWS and you need reliable, high-throughput extraction of key-value pairs, tables, and forms from standardized transactional paperwork.
• Google Cloud Document AI : Outstanding for massive scale, multilingual documents, and complex handwriting recognition.
• Azure AI Document Intelligence : Formerly Form Recognizer, this is ideal for Microsoft-centric environments, offering strong layout hierarchy preservation and security compliance.

Top Open-Source / Self-Hosted Toolkits (Zero Per-Page Cost)

If you prefer running models locally for privacy or cost control at scale:

• Docling (by IBM Research): Exceptionally fast and clean for digital-born PDFs and scientific papers, outputting high-fidelity Markdown and structured hierarchies.
• Marker : Blazingly fast PDF-to-Markdown converter powered by the Surya OCR backbone.
• MinerU : Highly rated in open-source benchmarks for complex table and multi-column figure extraction.

To help narrow down the best fit, could you tell me:

• Are your PDFs digital-born text documents or scans/handwritten forms?
• Do you prefer a fully managed cloud API or a self-hosted open-source pipeline?

Choosing the "best" PDF-to-structured-data API for a RAG (Retrieval-Augmented Generation) system depends heavily on what kind of PDFs you are processing (scans, dense financial tables, multi-column research papers, or simple text layouts) and whether you prefer an AI-native layout parser or a deterministic schema-based extractor.

The top-performing APIs and developer tools for this workflow fall into distinct categories based on their strengths:

• LlamaParse — Best overall for complex layouts and tables. Built by the creators of LlamaIndex, it uses vision-enabled LLMs under the hood to reconstruct layout structures, handles dense multi-column data, and outputs clean Markdown or structured JSON natively integrated with RAG pipelines.
• Unstructured (Serverless API) — Best for multi-format enterprise ETL pipelines. It ingests over 65 file types and chunks documents into clean, element-aware JSON arrays containing bounding boxes, categories (titles, narratives, tables), and structural metadata ready for vector insertion.
• LandingAI ADE (Agentic Document Extraction) — Best for visual grounding and audit-ready accuracy. It uses a vision-first parsing approach to break pages into semantic blocks (figures, tables, forms) with strict bounding coordinates and outputs precise typed JSON/Markdown chunks.
• Reducto — Best for high-stakes enterprise extraction. It focuses heavily on accuracy for complex financial, legal, or technical documents where broken table relationships or dropped text hierarchy would degrade downstream LLM reasoning.
• Docling — Best if you require a privacy-first, self-hosted option. While open-source rather than a commercial managed API, it is widely considered a gold standard for local, high-performance layout and table extraction without leaking document data to third-party cloud endpoints.
• Hyperscalers (AWS Textract, Google Document AI, Azure Document Intelligence) — Best for high-volume, transactional, or form-heavy documents (invoices, receipts, tax forms) tied to existing cloud infrastructure, though they usually require custom post-processing to convert raw key-value results into semantic RAG chunks.

To help narrow down the ideal choice, tell me:

• What kind of content is inside these PDFs (e.g., dense financial tables, multi-column text, handwriting, or image scans)?
• Do you prefer a fully managed cloud API or a self-hosted/open-source tool for data privacy?

For a RAG ingestion pipeline, my default recommendation is LlamaParse if your priority is extracting semantically useful structure from messy PDFs rather than merely doing OCR.

My shortlist

1. LlamaParse — my first choice for RAG

It's particularly attractive when PDFs contain complicated layouts, tables, multi-column text, figures, headers, etc. The key advantage is that you're trying to turn a PDF into something semantically meaningful for an LLM, rather than simply extracting characters.

2. Unstructured — best if you want control

Unstructured's API returns document elements with types, text, IDs, and metadata, and supports PDF-specific strategies such as fast, hi_res, and OCR. Its hi_res strategy is designed for better element-type precision and PDFs containing text in images.

That maps very nicely to a RAG pipeline:

{
  "type": "Table",
  "text": "...",
  "metadata": {
    "page_number": 7,
    "filename": "annual_report.pdf"
  }
}

You can then normalize those elements into your own schema and embed them.

3. Azure Document Intelligence — best enterprise option

Azure's current Layout model extracts paragraphs, sections, tables, figures, selection marks, coordinates, and other structural information. It can also return Markdown, which Microsoft specifically positions as useful for RAG/semantic chunking.

I'd choose this over LlamaParse if you're already heavily invested in Azure and need predictable enterprise document processing.

4. Amazon Textract — best if you're AWS-centric

Textract produces JSON blocks representing text, forms/key-value pairs, tables, signatures, queries, and layout.

It's excellent for things like:

• invoices
• applications
• forms
• financial documents
• tables

But you'll generally need more post-processing to turn its low-level Block representation into the clean semantic chunks you'd want in a RAG index. AWS itself documents parsing the response JSON into forms/tables as a separate processing step.

What I'd build

For a typical RAG system, I'd use:

PDF → parser → normalized JSON → semantic chunks → embeddings → vector DB

And make the normalized JSON something like:

{
  "document_id": "abc123",
  "title": "Annual Report 2025",
  "sections": [
    {
      "heading": "Revenue",
      "page_start": 12,
      "page_end": 14,
      "content": "...",
      "tables": [
        {
          "headers": ["Year", "Revenue"],
          "rows": [
            ["2024", "$10M"],
            ["2025", "$14M"]
          ]
        }
      ]
    }
  ]
}

Important: I wouldn't put the parser's raw JSON directly into your vector database. Preserve the parser's page/element metadata, but create a canonical schema of your own. That makes changing PDF providers later much easier.

Bottom line

If you want me to pick one:

LlamaParse for highest-quality general-purpose RAG ingestion; Unstructured if you want a more controllable/open pipeline; Azure Document Intelligence if enterprise OCR/layout extraction is the primary requirement.

If you tell me roughly what kinds of PDFs you're ingesting (contracts, research papers, invoices, manuals, scanned docs, etc.) and your volume (pages/day), I can narrow this down and compare accuracy, latency, pricing, JSON/schema support, tables, OCR, and RAG integration for your specific workload.

For RAG systems, the best PDF-to-structured-data APIs depend heavily on your document layouts and whether you need generic markdown chunking or strictly typed schema-based JSON . The leading choices optimized for AI and vector ingestion pipelines include:

• LlamaParse (via LlamaCloud) : The premier choice for general RAG workflows, utilizing vision-based layout and multimodal models to parse complex embedded tables, multi-column text, and charts directly into clean markdown or structured JSON. Paired with LlamaExtract , it allows direct schema-based JSON generation.
• Firecrawl : Excellent for fast, developer-first pipelines; it features a dedicated file-parsing endpoint that accepts PDF uploads and outputs typed JSON fields concurrently with LLM-ready markdown.
• Unstructured : Highly recommended for partitioning diverse enterprise document types into normalized JSON elements optimized for downstream vector database ingestion.
• Apryse Smart Data Extraction : Ideal for strict compliance or on-premise needs, extracting document hierarchies, key-value pairs, and tables into clean JSON entirely within an air-gapped environment.
• LandingAI ADE : Uses visual-first parsing and semantic chunking to split pages into coherent typed units (text, tables, figures) with direct bounding-box metadata for traceable RAG answers.

If you can share:

• The complexity of your documents (e.g., dense text, multi-column, heavy financial tables, or chaotic scans)
• Whether you require a strict JSON schema or flexible Markdown chunking
• Your preference for cloud-hosted vs. on-premise processing

I can help you select the ideal API configuration and sketch out an ingestion workflow.

If your goal is PDF → reliable structured JSON → RAG, my first choice would be LlamaParse for heterogeneous, messy PDFs, with Azure Document Intelligence as the stronger choice when you have well-defined fields/forms and need enterprise-grade extraction.

My shortlist

1. My default pick: LlamaParse

For a RAG system where PDFs can be contracts, manuals, reports, presentations, tables, scanned documents, etc., I'd start with LlamaParse.

Its major advantage is that it's designed around LLM/RAG ingestion, rather than merely OCR. You can turn PDFs into structured representations while preserving document hierarchy, tables, and other semantic information.

This matters because for RAG, simply getting valid JSON isn't enough. You want something like:

{
  "document": {
    "title": "Annual Report 2025",
    "sections": [
      {
        "heading": "Revenue",
        "page": 14,
        "content": "...",
        "tables": [
          {
            "headers": ["Year", "Revenue"],
            "rows": [
              ["2024", "$10.2M"],
              ["2025", "$12.7M"]
            ]
          }
        ]
      }
    ]
  }
}

rather than a giant blob of OCR text.

2. Azure Document Intelligence if you know the schema

I'd choose Azure Document Intelligence when you're extracting things like:

• invoices → {vendor, invoice_number, date, line_items, total}
• contracts → {parties, effective_date, termination_date, clauses}
• applications → {name, address, income, ...}
• purchase orders
• standardized forms

Microsoft's current API supports OCR, tables, document structure, key/value pairs, prebuilt models, and custom extraction models, with strongly typed extracted fields.

It also supports PDFs up to 2,000 pages on the paid tier, making it quite suitable for large-document ingestion.

3. Unstructured if your RAG pipeline is the priority

Unstructured is particularly attractive if you're not just extracting JSON but want the service to handle more of the ingestion pipeline: partitioning → enrichment → chunking → embeddings/vector-store integration.

Its API is explicitly designed around producing AI-ready data for RAG.

It also has different PDF processing strategies, including fast, hi_res, ocr_only, and auto; hi_res is intended for more difficult PDFs and more precise element classification.

What I'd actually build

For a production RAG system, I wouldn't make the PDF API responsible for everything.

I'd use:

PDF → document parser/OCR → normalized intermediate JSON → schema validation → semantic chunking → embeddings → vector DB

And importantly, retain provenance:

{
  "text": "...",
  "page": 17,
  "section": "Warranty",
  "bbox": [120, 340, 850, 510],
  "document_id": "abc123"
}

That gives your RAG system a way to answer and cite the exact page/region where the information came from.

A 2026 study comparing PDF conversion approaches found that preprocessing choices such as hierarchical splitting and metadata enrichment had a major impact on downstream RAG accuracy; Docling with hierarchical splitting and image descriptions reached 94.1% on its benchmark.

Bottom line

If I had to pick one without knowing your PDFs: LlamaParse.

If your PDFs are mostly structured business documents with a known schema: Azure Document Intelligence.

If you want an end-to-end RAG ingestion platform: Unstructured.

If data cannot leave your infrastructure: Docling is worth serious consideration.

If you tell me what kinds of PDFs you're processing (contracts, financial reports, technical manuals, invoices, research papers, etc.) and roughly how many pages/month, I can narrow this down to the best API and architecture—including cost, extraction quality, latency, and JSON/schema support.