7 document extraction APIs worth building on in 2026

Picking a document extraction API in 2026 comes down to one question: can you trust the output enough to act on it without someone re-reading the page? Here's the short version, before we get into the details: For extraction you can verify value by value, LandingAI's Agentic Document Extraction (ADE) grounds every field to the exact line it came from, and Reducto is its closest agentic peer. If your data has to stay inside AWS, Google Cloud, or Azure, the native service is the easy path. And if budget rules and you can self host, Docling and Unstructured do the job for free. Every tool below comes with a minimal snippet, so you can see how it is actually called rather than only what its pricing page claims. The 7 document extraction APIs, compared Each option is judged on the four things that decide whether an extraction survives production: how it structures its output, whether it grounds each value back to the page, where it can run, and how it prices at volume. LandingAI's Agentic Document Extraction (ADE) LandingAI's Agentic Document Extraction (ADE) is built around one idea: ground everything. Every element Parse returns comes with its own page, character range, and bounding box, so you can trace any extracted value back to exactly where it sits on the page. The second generation runs on the DPT-3 model family, and the whole output is built for code to consume, not for a person to skim. Parse itself returns reading-order Markdown plus a hierarchical breakdown: pages, elements, lines, table cells, each carrying its own stable id and its grounding. Hand that Markdown to Extract, and it pulls your fields into typed JSON where every value still traces back to its exact page location. On the infrastructure side: - Python and TypeScript SDKs, plus a CLI - An async Jobs API built for large batches - Single documents up to 6,000 pages or 1 GB - HIPAA-compliant processing with a BAA and a Zero Data Retention option, starting at the Team tier - SLAs, uptime guarantees, priority rate limits, and VPC or on-premises deployment on Enterprise Best for: developers who need to verify extraction field by field, especially in audit and regulated workflows. Install it with pip install landingai-ade . Here's a minimal Parse flow: submit a job, wait for it, read back the result. from landingai_ade import LandingAIADE client = LandingAIADE() # reads VISION_AGENT_API_KEY job = client.v2.parse_jobs.create( document_url="https://your-storage.example.com/invoice.pdf", service_tier="standard", ) done = client.v2.parse_jobs.wait(job.job_id, raise_on_failure=True) # done.result.markdown holds the reading-order Markdown, grounded per element print(done.result.markdown) From there, Extract takes that Markdown plus a schema you define and returns typed JSON, with every value still traceable back to its page location. The Extract API reference has the exact schema and request format if you want to go deeper. Reducto Reducto takes on the full document lifecycle in one platform: parse, extract, classify, split, and edit. It grounds every value to a bounding box citation, and it leans on multi-step layout analysis plus a self-correcting OCR pass to handle the pages other tools choke on. It returns layout-aware chunks that are ready for retrieval, so you skip a second re-chunking pass. It reports processing over four billion pages. Deployment options run cloud, VPC, on-premises, and air-gapped, and higher tiers add SOC 2 Type II, HIPAA, and Zero Data Retention. On the independently published LongExtractBench, it completed all 225 long documents at 99.6% precision and recall. Best for: regulated teams that lead their evaluation with hard documents. Trade-off: the advanced agentic modes cost more credits than basic OCR. Install with pip install reductoai . You upload a file, then run parse or extract against it: from pathlib import Path from reducto import Reducto client = Reducto() # reads REDUCTO_API_KEY upload = client.upload(file=Path("invoice.pdf")) result = client.extract.run( input=upload.file_id, instructions={"schema": { "type": "object", "properties": { "document_date": {"type": "string"}, "amount_due": {"type": "number"}, }, }}, ) print(result) LlamaParse LlamaParse is the parsing layer inside the LlamaIndex ecosystem, which makes it the obvious pick if you're already building retrieval and agents there. It reads visually complex pages using vision language models and hands back RAG-ready Markdown you can drop straight into a LlamaIndex pipeline. It covers 90+ formats and offers a few parse modes, from a fast pass on digital text to a slower, agentic mode reserved for the hardest pages. Because the integration with LlamaIndex is native, you skip the glue code most RAG projects end up writing by hand. Best for: RAG projects already standardized on LlamaIndex. Trade-off: credits are tiered, and which mode you pick affects the bill more than page count does. Install with pip install llama-cloud-services . A minimal parse returns Markdown you can feed straight into a pipeline: from llama_cloud_services import LlamaParse parser = LlamaParse() # reads LLAMA_CLOUD_API_KEY documents = parser.load_data("invoice.pdf") print(documents[0].text) Docling Docling comes out of IBM Research and now lives under the Linux Foundation as an MIT-licensed open source project. If you want strong layout-aware parsing but need to run it yourself, this is the strongest free option available. It converts PDFs, Office files, HTML, and images into one unified, traceable representation, entirely on your own infrastructure. It ships as a Python library, a CLI, an HTTP service, and an MCP server, which makes it a natural fit for air-gapped environments. There's no per-page API cost either, which matters if you're ingesting at high volume on a tight budget. Best for: teams comfortable owning their own infrastructure. Trade-off: you're running and tuning the pipeline yourself, with no vendor SLA to fall back on. Install with pip install docling . It converts a file locally and exports Markdown: from docling.document_converter import DocumentConverter converter = DocumentConverter() result = converter.convert("invoice.pdf") print(result.document.export_to_markdown()) Unstructured Unstructured pairs an open source library with a hosted platform, and it's built for breadth. If you need to pull in many file types rather than squeeze maximum accuracy out of one, this is the tool. It partitions PDFs, emails, HTML, and Office files into typed, metadata-rich elements ready for chunking. Connector support is broad on both ends, sources and destinations, which makes it easy to pull mixed document sets into a vector store. It also exposes element-level coordinates, and the hosted platform adds SOC 2 Type II, HIPAA, and in-VPC deployment. Best for: large-scale, multi-format RAG ingestion. Trade-off: table fidelity and reading order on complex, multi-column layouts need more tuning here than on the vision-first platforms. Install with pip install "unstructured[pdf]" . One call partitions a file into typed elements: from unstructured.partition.auto import partition elements = partition(filename="invoice.pdf") for el in elements: print(el.category, el.text) AWS Textract AWS Textract is Amazon's managed OCR and form extraction service, and if your stack already lives on AWS, it's the path of least resistance. It reads printed and handwritten text, pulls key-value pairs and tables, and returns geometry coordinates for every block it detects. Pricing runs per feature and per page, across Detect Text, Analyze Tables, Analyze Forms, and Analyze Queries. It's tightly wired into the rest of the AWS stack too. Best for: high-volume, AWS-native pipelines with fairly predictable documents. Trade-off: the output is raw, so restoring reading order and semantic structure is work you write yourself. It runs through boto3, and it returns blocks rather than your schema: import boto3 client = boto3.client("textract") with open("invoice.png", "rb") as f: resp = client.analyze_document( Document={"Bytes": f.read()}, FeatureTypes=["FORMS", "TABLES"], ) # resp["Blocks"] holds LINE, KEY_VALUE_SET, TABLE, and CELL blocks Turning those blocks into your target JSON is a step you write yourself, often with a second model call. Google Document AI Google Document AI is Google Cloud's document processing suite, organized around processors for OCR, form parsing, and a handful of specialized document types. If you're standardized on GCP, this is the natural choice. Each processor returns structured fields with bounding regions, and the specialized parsers handle common formats like invoices and receipts right out of the box. Pricing runs per processor and per page, depending on which ones a given workflow calls. It wires directly into BigQuery, Vertex AI, and the rest of the Google stack. Best for: managed extraction inside GCP. Trade-off: it stays inside its home cloud, so your data stays resident in GCP too. You call a processor you have created in the console: from google.cloud import documentai client = documentai.DocumentProcessorServiceClient() name = client.processor_path("PROJECT_ID", "us", "PROCESSOR_ID") with open("invoice.pdf", "rb") as f: raw = documentai.RawDocument(content=f.read(), mime_type="application/pdf") result = client.process_document( request=documentai.ProcessRequest(name=name, raw_document=raw) ) print(result.document.text) How the 7 stack up at a glance | Tool | Output | Grounding | Deployment | Pricing | Best for | |---|---|---|---|---|---| | LandingAI ADE | Markdown + JSON elements | Element-level (page, character range, bounding box) | Cloud, VPC, on-prem | Credit-based, priced per model and tier | Verifiable, audit-ready extraction | | Reducto | Layout-aware chunks | Bounding box citations | Cloud, VPC, on-prem, air-gapped | Credit-based, complexity-tiered | Hard docs, regulated teams | | LlamaParse | RAG-ready Markdown | Layout bounding boxes | Managed cloud, enterprise options |