Best Document Processing APIs for Developers in 2026

The first API call is the easy part. The work that follows decides a document processing API: the reconstruction code, the schema validation, the retries on a malformed table, and the audit path for when someone asks where a value came from.

For a developer, the deciding question is how much of that downstream work the API absorbs, and how cleanly it drops into the stack you already run. A high benchmark score matters far less than either.

This guide groups the leading options into three families, dedicated extraction platforms, retrieval-native tools, and hyperscaler services, and compares them on API design, SDK coverage, output format, and source traceability.

Quick answer

The right API depends on what you are building. For grounded, structured output that feeds an agent or a RAG pipeline with each value traced to its source, a dedicated platform such as LandingAI ADE, Reducto, or Extend fits best.

For document processing as one stage of a retrieval pipeline, LlamaParse and Unstructured slot in with Markdown and typed-element output. For teams standardized on a cloud, AWS Textract, Azure AI Document Intelligence, and Google Document AI keep the work inside AWS, Azure, or Google Cloud.

The eight APIs at a glance

The LandingAI rows come from LandingAI's own documentation. The other rows describe each vendor's documented developer surface, so confirm current SDKs and pricing on each vendor's site before you build.

Dedicated extraction platforms

These are API-first products whose whole job is turning documents into structured data. They tend to offer composable operations, grounded output, and deployment options beyond a single cloud.

LandingAI Agentic Document Extraction

Agentic Document Extraction (ADE) is an API-first platform from LandingAI that converts documents into structured, source-cited data. You compose it from discrete operations: Parse turns a document into structured content, and Extract pulls fields against a JSON schema you define. Classify, Section, and Split are available in preview, which LandingAI advises against running in production.

Parse returns a nested hierarchy of pages, blocks, and table cells plus a Markdown representation, and tables come back as HTML so merged cells survive. Every block carries grounding to a page, a character range, and a bounding box, so a value traces to its exact source.

Official landingai-ade libraries cover Python and TypeScript, asynchronous jobs handle large documents, and the current model is DPT-3 Pro. Deployment runs from hosted SaaS in the US and EU to a Snowflake Native App and a VPC or on-premises container on the Enterprise plan.

Also read: How LandingAI ADE makes every extraction defensible through visual grounding

Reducto

Reducto is an API-first platform for document parsing and extraction. Parse, Extract, Split, Edit, and Classify are offered as standalone endpoints that you can also compose into pipelines. Parsing preserves layout and reading order with layout-aware chunking, and Extract returns structured JSON against a schema you supply.

It covers Python, Node.js, and Go alongside a REST API. Results open in a viewer where you inspect output at the bounding-box level, and its r-1 model handles grounding, which gives you a review path.

Private deployment options suit teams with stricter data requirements.

Also read: LandingAI ADE vs Reducto document extraction platform comparison

Extend

Extend is a managed platform spanning parsing, extraction, classification, splitting, and editing, with the widest SDK coverage in this group. Its libraries cover Python, TypeScript, Java, and Go, and its developer tools include a REST API, a CLI, and an MCP server.

Parsing returns clean Markdown or blocks for RAG and agents, and extraction pulls structured JSON from documents such as invoices, IDs, and contracts.

The managed platform suits teams that want several document operations handled in one place rather than stitched together across tools.

Retrieval-native tools

These tools are built to feed retrieval and ingestion pipelines. They lean toward Markdown and typed elements rather than schema-bound field extraction, so they fit a stage in a larger pipeline.

LlamaParse

LlamaParse is the document parsing service on the LlamaIndex platform, built for LLM pipelines. Its layout-aware parsing recovers tables from PDFs, scans, and images with cell structure preserved, and it returns clean Markdown, text, or JSON.

It offers Python and TypeScript access. Its processing tiers trade cost against accuracy, and a cost optimizer routes simpler pages to cheaper tiers, so simpler documents cost less than the top rate.

It fits teams already building retrieval on the LlamaIndex stack, where the parsed output flows into the rest of the framework.

Also read: LandingAI ADE vs LlamaParse: 2026 complete comparison

Unstructured

Unstructured is an open-source library and hosted platform for preprocessing documents into a RAG-ready shape. It partitions a document into typed elements such as Title, NarrativeText, and Table, and Table elements carry an HTML representation through a text_as_html field.

You run it locally, in a container, or through its hosted API and workflow jobs. Processing strategies control OCR: the ocr_only strategy runs the document through Tesseract, while the fast strategy reads a digital text layer for documents that already have one.

It is a preprocessing layer rather than a schema-bound extraction API, so it fits configurable ingestion pipelines that feed retrieval.

Also read: LandingAI ADE vs Unstructured

Hyperscaler services

These are the document services inside AWS, Azure, and Google Cloud. They fit teams already committed to that provider, where identity, storage, and billing are already solved.

AWS Textract

Amazon Textract is a managed document analysis service that detects text and analyzes structure. It returns tables as Block objects whose relationships include child cells, merged cells, titles, and footers, and it reads both typed and handwritten text from image and PDF files.

You call it through the AWS SDKs, and it wires into S3, Lambda, and IAM.

Its output is a JSON block graph that your application reassembles into the structure it needs, so the reconstruction work stays on your side.

Also read: LandingAI ADE vs AWS Textract

Azure AI Document Intelligence

Azure AI Document Intelligence, formerly Form Recognizer, is a machine-learning OCR service. Its Read model extracts printed and handwritten text, and its Layout model returns tables as cells with row and column spans and bounding regions.

On top of those, a broad catalogue of prebuilt models covers common business documents, and custom models handle the document types the prebuilt set misses.

You integrate through the Azure SDKs across several languages, which suits teams already standardized on Microsoft and Azure.

Also read: LandingAI ADE vs Azure Document Intelligence

Google Document AI

Google Document AI is a processor-based platform. A processor sits between the document and a machine-learning model, and you select and create the processors you need, then chain them into a pipeline for your document types.

It digitizes documents with OCR, identifies key-value pairs in forms and tables, and returns a structured Document object for each processor. Custom processors extend it to document types beyond the prebuilt set.

You integrate it with Cloud Storage, IAM, and the rest of Google Cloud through the Google Cloud SDKs.

Matching the API to your work

The families map cleanly to how teams actually decide.

Reach for a dedicated platform when structured, grounded output is the product you are building on, and you want composable operations and deployment flexibility across clouds. LandingAI ADE fits here when source traceability matters, Reducto and Extend when broad SDK coverage and several operations in one place matter more.

Reach for a retrieval-native tool when document processing is one stage of a RAG system rather than the whole job. Reach for a hyperscaler service when you are committed to AWS, Azure, or Google Cloud and want the work to stay inside it.

Whichever family you shortlist from, integrate the top two against a representative sample of your own documents before you commit. How faithful the output is, how much reconstruction code you write, and whether you can trace a value back to its source will separate them faster than any public benchmark.

Frequently asked questions

What is the difference between parsing and extraction?

Parsing converts a whole document into structured content such as text, tables, and layout. Extraction pulls specific fields, like an invoice total or a contract date, into a structured schema. Platforms such as LandingAI ADE, Reducto, and Extend expose both as separate operations you compose.

Which APIs offer official SDKs beyond Python?

LandingAI ADE ships Python and TypeScript libraries. Reducto adds Node.js and Go, and Extend adds Java and Go on top of Python and TypeScript. The hyperscaler services expose document processing through their general cloud SDKs, which span many languages.

How do these APIs handle large documents?

Most offer asynchronous jobs, where you submit a large or batch workload, receive a job reference, and poll for the result rather than holding a synchronous request open. LandingAI ADE, Reducto, and Extend all expose job-based processing, and the hyperscaler services provide their own async batch operations.

Which output format feeds an LLM or RAG pipeline most directly?

Structured JSON and Markdown that preserve document structure feed a pipeline with the least glue code, while plain text loses it. LandingAI ADE and Reducto return structured JSON with grounding, LlamaParse returns Markdown, text, or JSON, and Unstructured returns typed elements with HTML tables, all of which keep structure intact for a vector database or an agent workflow.

Also read: Document extraction for RAG: preparing structured outputs for vector databases

How do the dedicated platforms compare against the hyperscaler services?

A dedicated platform leads on grounded output and composable operations, while a cloud service fits teams already standardized on AWS, Azure, or Google Cloud. For a full side-by-side that puts LandingAI ADE against all three hyperscalers, see the comparison below.

Also read: AWS Textract vs Azure Document Intelligence vs Google Document AI vs LandingAI ADE