Top Batch Document Processing APIs (2026)

Updated August 2026.

Document processing turns PDFs, scans, images, and other unstructured files into data that software can act on. It enables teams to automate invoice reconciliation, claims and underwriting, clinical-data capture, logistics operations, RAG ingestion, and document-grounded agents.

At batch scale, the question is not simply how many files an API accepts. The stronger platform is the one that can process each file asynchronously, return reliable structured output, isolate failures, route uncertain results to review, and give developers the controls to balance cost, latency, and accuracy.

TL;DR:

1. Extend: Best for production structured extraction, RAG ingestion, and agent workflows because its native batch job gives every file an independent async run that can feed validation and review.
2. LlamaParse: Best for LlamaIndex-native batch parsing into LLM-ready text or Markdown.
3. Reducto: Best when customer-VPC or air-gapped deployment and source citations outweigh the need for native routing and review.
4. Unstructured: Best for normalizing many file types into typed elements and chunks before retrieval or another extraction service.
5. Azure Document Intelligence: Best for Azure-committed enterprises using prebuilt, layout, or custom document models.
6. Google Document AI: Best for GCP-native batch processing from Cloud Storage into processor-specific JSON outputs.
7. Firecrawl: An adjacent web-scraping tool whose single-file document parsing is not a reliable substitute for a dedicated batch document API.

Try Extend's document processing platform.

Why batch throughput matters for document AI in 2026

Teams processing thousands of documents per day need job-level guarantees, not a fast single-file demo. The operational questions are concrete: How many files fit in one submission? Does the request return immediately? Can individual failures be identified and retried? Does the service expose a webhook or polling state? Can low-confidence outputs be held before they reach an ERP, data warehouse, or decision system?

Keeping batch API comparisons current matters because vendors change job limits, asynchronous behavior, retry policies, quotas, and pricing. The criteria below use current public documentation; where a limit or retry policy is not documented, the table says so.

Parsing vs. extraction: why the distinction drives this ranking

Parsing converts each file into ordered text and layout: Markdown, pages, tables, chunks, coordinates, or similar machine-readable content. Extraction returns fields against a schema, such as invoice_total, policy_number, or a complete array of transactions.

A parser can process thousands of PDFs quickly and still leave the team to write prompts, schemas, validation, and correction logic. That is why parsing-focused products rank lower for high-volume structured-data workflows even when their raw ingestion throughput is strong.

What to look for in a batch document processing API

Structured-field accuracy. Count missing values, wrong values, incomplete arrays, failed files, and timeouts. Parsing accuracy alone cannot establish extraction accuracy.

Cost per document at volume. Include every parse, extract, citation, agentic, and review operation. Subscription minimums and credits per page can change the effective price.

Latency and throughput. Measure queue time, processing time, concurrency, completion rate, and the delay introduced by low-priority batch service.

Confidence routing and human-in-the-loop (HITL) review. The API should let teams validate results, route exceptions, preserve citations, and release approved output without building a second quality-control system.

Batch contract and recovery. A raw maximum-files claim does not establish production throughput or reliability. Look for a native job object, independent per-file states, idempotency, retry behavior, webhooks, and failed-item retrieval so partial failures are recoverable without restarting the entire corpus.

Developer experience. Evaluate typed SDKs, CLI and MCP access, agent skills, webhook signing, idempotent resubmission, per-file status, and failure recovery. These determine how much orchestration code a team must own after the first successful request.

Compact batch endpoint comparison table

“Unconfirmed” means the current public documentation reviewed for this article does not establish the behavior. Maximum files per submission is included as an operational constraint, not as a proxy for accuracy, concurrency, completion rate, or effective throughput.

Ranked picks

1. Extend

Best for: High-volume structured extraction, production RAG ingestion, and agent workflows with confidence-based human review built into the batch pipeline.

What it is: Extend is a developer-first document processing platform for teams building production data pipelines, RAG systems, and agents. Its APIs and tools cover Parse, Extract, Classify, Split, Edit, Workflows, evaluations, and Review Agent, with access through typed SDKs, a CLI, MCP, and agent skills.

Batch execution accepts 1–1,000 inputs for Parse, Extract, Classify, or Split. The request returns a batch object immediately, creates an independent asynchronous run for every file, and exposes completion through webhooks or polling. Extraction batches reference a versioned extractor, so one schema configuration applies across the job.

Confidence routing can combine extraction confidence, external-data validation, conditional workflow steps, and the Review UI. That creates a safe exception path: trusted results continue automatically while ambiguous results carry their document context into review.

Structured-extraction evidence is separate from the batch contract. On LongArray-Extract, Extend MAX achieved 99.2% mean per-document accuracy, completed all 45 documents, and averaged 301 seconds—2.8x faster than the closest competing system in that evaluation. These results measure complete repeated-record extraction, not batch-endpoint throughput.

Production fit spans consequential operational records rather than text ingestion alone. Brex uses Extend to process millions of financial documents across Bill Pay and receipt parsing, reaching greater than 99% accuracy on certain tasks. Flatiron uses Extend Split and Extract for complex next-generation sequencing reports, replicating six months of work in two weeks across a network of five million people with cancer. These are production-scale proof points, not claims that either customer uses the batch endpoint.

Pros:

• Dedicated batch endpoints for Parse, Extract, Classify, and Split with up to 1,000 files per call.
• Individual runs isolate per-file results and metadata inside a batch.
• Workflows, validation, conditional routing, evaluations, and review sit on the same platform.
• Light Parse and Performance Parse provide explicit cost, latency, and accuracy controls before extraction.
• Typed SDKs, a CLI, MCP, and agent skills support both conventional applications and agent-native workflows.
• Zero data retention availability for all plans, plus Cloud, Bring Your Own Cloud (BYOC), and Hybrid deployment options.

Cons:

• Batch mode is optimized for bulk throughput rather than user-facing latency; single-file asynchronous endpoints are the better fit for interactive flows.
• Extract, Classify, and Split batches reference an existing processor instead of accepting a new inline configuration, so teams publish the processor before submitting the job.

Pricing: Extend pricing includes 10,000 free credits, then $0.0125 per credit on PAYG. Scale is $500/month with 50,000 included credits and $0.01 additional credits. Light Parse uses 0.5 credits per page and Performance Parse uses 2 credits per page. The pricing page's published extraction example totals five credits per page—three for extraction plus two for automatic parsing—or $0.0625 per page at the PAYG rate.

2. LlamaParse (LlamaIndex)

Best for: Developers already in the LlamaIndex ecosystem whose batch output is primarily LLM-ready text or Markdown.

What it is: LlamaParse provides asynchronous parsing tiers and a dedicated Batches pathway. Its rate-limit documentation separates File Upload, Parse Upload, Parse, and Classify traffic, so pipeline capacity depends on operation and plan rather than one universal request limit. LlamaIndex announced a 10,000-file batch experience in July 2026, but this refresh did not find that maximum in the current API reference; treat it as an announced, unconfirmed limit until verified in your account.

Pros:

• Native connection to LlamaIndex parsing, indexing, retrieval, and agent workflows.
• Batch-level UI and APIs reduce custom fan-out code.
• Multiple parser tiers and enterprise hybrid-cloud deployment.

Cons:

• It is parsing-first; schema extraction uses a separate LlamaExtract surface.
• Confidence routing and a document review queue are not native batch-parse features.
• The 10,000-file maximum needs confirmation against current plan and API documentation.

Pricing: Public pricing lists Free with 10,000 credits, Starter at $50/month with 40,000 credits, Pro at $500/month with 400,000 credits, and Enterprise custom. Credits per page vary by parser tier. See Extend vs. LlamaParse for the direct product and benchmark comparison.

3. Reducto

Best for: Teams whose overriding requirement is a customer-VPC, on-premises, or air-gapped deployment and that can own routing and review outside the API.

What it is: Reducto exposes async Parse, Extract, Split, and Edit calls with job IDs, polling, and webhooks. Its SDK documentation shows client-managed concurrency for large collections rather than a fixed-size batch object. Extraction can wrap values with page, bounding box, source text, and confidence citations.

Pros:

• Async job submission scales independently across files.
• Citations make values traceable to source regions.
• VPC, on-premises, and air-gapped Enterprise options support private workloads.

Cons:

• Citations and chunking are mutually exclusive in the current Extract API.
• Review queues and confidence-based routing require application code or another system.
• Public maximum batch size and automatic retry semantics are not fixed in the documentation reviewed.

Pricing: Public pricing includes 15,000 free credits, then $0.015 per credit. Growth and Enterprise are custom-priced; agentic parsing, extraction, and latency optimization consume additional credits. See Extend vs. Reducto for the direct comparison.

4. Unstructured

Best for: Teams standardizing many file types into a common preprocessing format before retrieval or extraction.

What it is: Unstructured partitions documents into typed elements, then applies chunking, enrichments, embeddings, and connectors. Hosted on-demand jobs accept multiple local files and expose job state and failed-file details. This is a normalization layer, not a schema-bound extraction endpoint.

Pros:

• Open-source library can run locally or self-hosted.
• Hosted workflows support source and destination connectors.
• Failed-file inspection makes partial job failures visible.

Cons:

• On-demand jobs are limited to 10 files, 10 MB per file, and five concurrently running jobs in current documentation.
• Confidence routing and human review are not native.
• Structured field extraction requires a downstream service or custom model step.

Pricing: The open-source library is free to operate on your infrastructure. Commercial API pricing separates pay-as-you-go usage and Business plans; cost depends on strategy and enrichments. See Extend vs. Unstructured for the direct comparison.

5. Azure Document Intelligence

Best for: Enterprises standardized on Azure that need prebuilt, layout, or custom document models at scale.

What it is: Azure Document Intelligence runs asynchronous analyze jobs for Read, Layout, prebuilt, and custom models. It integrates with Blob Storage, Event Grid, Functions, Logic Apps, and Azure identity, so the main advantage is ecosystem fit rather than a unified cross-cloud document workflow.

Pros:

• Broad model catalog and Azure-native authentication, storage, monitoring, and events.
• Confidence and geometry are available for many detected fields and elements.
• Commitment pricing and supported containers address large enterprise workloads.

Cons:

• Batch limits and capabilities vary by model, API version, region, and pricing tier.
• Confidence routing and review orchestration require Azure services or custom code.
• Cloud lock-in is material for storage, events, identity, and operations.

Pricing: Azure pricing is region-dependent; representative public rates are about $1.50 per 1,000 pages for Read, $10 for layout/prebuilt analysis, and $30 for custom analysis.

6. Google Document AI

Best for: Teams on Google Cloud that want managed batch processing tied to GCP data pipelines.

What it is: batchProcess reads documents from Cloud Storage, returns a long-running operation, and writes JSON results back to Cloud Storage. Document AI publishes a 5,000-file system limit, while current SDK guidance says to create batches of at most 1,000 files; processor-specific page limits and a five-concurrent-batch quota also apply.

Pros:

• Strong fit with Cloud Storage, IAM, Workflows, Pub/Sub, BigQuery, and monitoring.
• Public limits and page pricing are relatively detailed.
• Separate OCR, layout, form, custom extraction, classifier, and splitter processors.

Cons:

• Teams must compose storage, processor, retry, validation, and routing behavior.
• Human Review is deprecated.
• Processor-specific page and language limits complicate a universal batch contract.

Pricing: Public pricing lists Enterprise OCR at $1.50 per 1,000 pages, Layout Parser at $10, and Form Parser or Custom Extractor at $30 for the first one million monthly pages. See Extend vs. Google Document AI for the direct comparison.

7. Firecrawl

Best for: Web-scraping workflows that can tolerate a less reliable, adjacent path for PDFs—not multi-file batch document processing.

What it is: Firecrawl's /batch/scrape accepts URL lists and can parse linked PDFs, while /parse accepts one uploaded local or private file per request. It is adjacent to this category because the dedicated parse endpoint does not expose a multi-file batch contract.

Pros: One API family covers crawl, scrape, batch scrape, and single-file parsing into LLM-ready formats.

Cons: No dedicated multi-file /parse batch endpoint, native confidence routing, or human review queue is documented, so teams must build their own fan-out, recovery, and quality controls for uploaded documents.

Pricing: Public pricing includes 1,000 free credits monthly; page operations and PDF parsing can consume separate credits, making document-job cost less predictable than a dedicated page-based batch API.

How the picks compare

Read across each row for the strength of the documented fit; “conditional” means the capability is partial, plan-dependent, or requires additional orchestration.

Which pick fits your use case

• Structured-extraction-heavy operations: Extend provides batch Extract plus validation, conditional routing, and human review.
• Production RAG and agent workflows: Extend combines parsing, extraction, evaluations, workflow controls, and review on one platform, so ingestion quality can be managed before documents reach retrieval or an agent.
• LlamaIndex-native parsing: LlamaParse fits teams that primarily need to turn a large collection into text or Markdown inside an existing LlamaIndex stack.
• Azure- or GCP-committed enterprises: Azure Document Intelligence or Google Document AI keeps the batch pipeline inside the selected cloud's storage, IAM, events, and billing.
• Audit-sensitive workflows: Extend is the stronger fit because citations, validation, conditional routing, evaluations, and review stay in the same workflow. Consider Reducto when private deployment is the overriding requirement and your team can build the routing and review layer.
• Web-scraping-adjacent needs: Firecrawl fits URL batches, but it is not a dependable substitute for a multi-file document-processing endpoint.

Why Extend leads for high-volume batch processing

Extend leads because the batch contract and the quality-control path are part of the same system. Each extraction submission creates independent runs, keeps metadata attached to every file, and can feed validation and review before structured output reaches a system of record. That is a more complete high-volume contract than parsing files quickly and leaving error detection to downstream code.

Brex uses Extend across millions of financial documents for Bill Pay and receipt parsing. Flatiron uses Split and Extract to process complex next-generation sequencing reports, reproducing six months of work in two weeks. The relevant production benefit is not a logo by itself; it is evidence that the same processing primitives can handle consequential records across finance and healthcare.

Try Extend's batch APIs.

How we evaluated these APIs

We reviewed public endpoint references, limits, rate-limit pages, pricing, and workflow documentation for this August 2026 update. The ranking weighs structured-field accuracy, cost, asynchronous throughput, per-file failure handling, confidence routing, human review, and developer experience. Parse versus extract is explicit because a high-throughput parser does not by itself provide reliable structured records, and extraction-quality evidence does not establish batch throughput.

Extend published LongArray-Extract, the extraction benchmark cited above. Its dataset is public on Hugging Face, making the evaluation open source and independently reproducible; we encourage teams to run the evaluation on their own representative documents. No single public benchmark compares every batch endpoint's accuracy, cost, and throughput, so the ranking combines benchmark evidence with documented product behavior and pricing.

FAQs

What is the difference between parsing and extraction in a batch job? Batch parsing returns ordered text and layout for many files. Batch extraction returns named, schema-bound values for many files. A pipeline may parse first and extract second, but the outputs and accuracy metrics are different.

What does a maximum-files-per-job limit mean in practice? It is the number of files one submission accepts, not the account's total daily throughput or concurrent capacity. A 50,000-file backfill with a 1,000-file job limit needs at least 50 submissions plus idempotency, status tracking, and partial-failure recovery.

How do confidence routing and HITL work? The pipeline evaluates a field, document, or external validation result against a threshold. Passing results continue automatically; exceptions enter a review queue with the source document and citations, then rejoin the workflow after approval or correction.

Where should I go for Textract-specific batch guidance? See AWS Textract: when to use it and when to choose an alternative for its asynchronous operations, Block output, pricing, and AWS integration trade-offs.