Compare
Reducto vs Google Document AI
Reducto is the agentic document platform: one zero-shot API for every document, deployable from hosted to air-gapped. Document AI is Google Cloud's OCR service, built around per-document-type processors.
Last updated
Helping everyone from startups to Fortune 10 enterprises unlock their data.
- Harvey
- Scale AI
- Newfront
- Medallion
- Vanta
- Legora
- Rogo
- Levelpath
- JLL
- Vise
- Laurel
- Toast
- Mercor
- Zip
- Anterior
- Supio
At a glance
How Reducto and Google Document AI compare
Reducto wins on zero-shot accuracy, platform breadth, and deployment flexibility. Document AI wins on GCP ecosystem integration and pretrained processors for standard document types.
| Dimension | Reducto | Google Document AI |
|---|---|---|
| Category | Full platform: parse, extract, split, classify, and edit in one API. | Cloud OCR service: 60+ pretrained and custom processors on Google Cloud. |
| Setup model | Yes: Zero-shot: any document, structured output; no training or routing. | Partial: Pick or train a processor per document type, plus routing logic. |
| Table extraction | Yes: 0.90 on RD-TableBench; merged cells, multi-level headers, rotated tables. | Partial: 0.81 on RD-TableBench; degrades on complex table structures. |
| Structured extraction | Yes: Deep Extract: 99.6% precision and recall on micro1's benchmark. | Partial: Processor-based extraction; no spatial citations on extracted values. |
| Enterprise readiness | Yes: SOC 2 Type II, HIPAA, zero data retention; VPC to air-gapped. | Partial: GCP-grade compliance; Google Cloud only, no on-prem or air-gapped. |
| Agent tooling | Yes: MCP server, CLI, Python/Node.js/Go SDKs, and Studio. | Yes: Tight Vertex AI integration; output needs post-processing for LLM pipelines. |
| Pricing | From $0.015/page pay-as-you-go; 15,000 free credits. | Per-processor rates for OCR, forms, and specialized processors. |
Parse one of your hardest documents in Studio and compare the output side by side.
The comparison in depth
Where the differences actually show up
- Zero-shot vs processor-per-document-type
- Document AI's core abstraction is the processor: a pretrained model for invoices, W-2s, or IDs, or a custom one you train and maintain. That works well when your documents fit the catalog. When they don't, it creates real overhead: routing logic to pick the right processor, custom training for anything unusual, and maintenance as document formats drift. Reducto is zero-shot: one API handles any document type at up to 99–100% accuracy on complex documents, because the pipeline orchestrates 12+ models per document rather than relying on a single pretrained model per type.
- Extraction accuracy, measured
- An independent benchmark commissioned by Reducto and conducted by micro1 evaluated extraction systems on 225 real, human-validated documents. Reducto Deep Extract ranked #1 on all four dimensions (100% coverage, 99.6% precision, 99.6% recall, 99.3% leaf accuracy) and completed every document with zero failures. On tables specifically, Reducto scores 0.90 on RD-TableBench to Document AI's 0.81. Benchmarks are a starting point, not a verdict: the numbers that matter are the ones on your own documents, which is why we encourage head-to-head evals.
- The hard 20%: figures, handwriting, checkboxes
- Google's base OCR is genuinely strong; the gap opens on the content that breaks pipelines. Reducto's standard parse handles figures and charts (converting charts to structured tabular data), mixed handwritten and printed text on the same page, and checkbox detection with state and position, with no separate processor required. In Document AI, figure and chart extraction is limited outside purpose-built processors, handwriting often means routing to a dedicated processor, and checkbox accuracy is inconsistent across styles. If your documents are clean and standardized, you may not notice; if they're scanned forms and real-world paperwork, this is where accuracy compounds.
- Output built for LLM and RAG pipelines
- Document AI predates the LLM era, and its output shows it: teams typically write post-processing to turn processor responses into chunks, markdown, or schema-shaped JSON that agents and RAG systems can use. Reducto returns LLM-ready structured output natively: reading order, block types, table structure, and per-field citations with bounding boxes, reviewable in Studio. Reducto uses frontier models rather than competing with them; the point is a document-specific pipeline that gets model-ready data out of messy files, so your downstream models start from clean input.
- Deployment beyond one cloud
- Document AI runs on Google Cloud, full stop: a strength if you're GCP-native, a blocker if you're not. Reducto is SOC 2 Type II and HIPAA compliant (BAA available) with zero data retention, and deploys hosted, in your VPC on any major cloud, on-prem, or fully air-gapped. Teams at Harvey, Scale AI, and Vanta run Reducto in production, and the platform has processed 4B+ pages.
- Pricing you can predict
- Document AI prices per processor: OCR, form parsing, and specialized processors each carry their own rates, so total cost depends on how documents route through your pipeline. Reducto is pay-as-you-go from $0.015/page with 15,000 free credits, and one price covers the whole platform: parse, extract, split, classify, and edit. For mixed document workloads, a single meter is easier to forecast than a matrix of processor rates.
- Migrating from Document AI
- Most migrations collapse processor routing into a single API call: where Document AI needs per-type processors and the glue code between them, Reducto handles the same documents zero-shot. The docs cover Python, Node.js, and Go SDKs, and Studio lets you validate output on your real documents before switching production traffic. Teams typically delete routing and post-processing code rather than port it.
Which fits your team
Who should pick which
Different tools fit different stacks. Here's the honest split.
Choose Reducto if…
- Your documents are complex (irregular tables, figures and charts, handwriting, checkboxes, scans), where processor-based OCR accuracy falls short.
- You want zero-shot processing without selecting, training, or maintaining processors per document type.
- You need deployment beyond Google Cloud: VPC on any major cloud, on-prem, or air-gapped, with SOC 2 Type II, HIPAA, and zero data retention.
- You're feeding LLM, RAG, or agent pipelines and want citation-backed, model-ready output instead of post-processing processor responses.
- Your workflow extends beyond OCR into extraction with citations, splitting, classification, or document editing.
Google Document AI may be a fit if…
- Your team is standardized on Google Cloud and wants document processing inside a single GCP billing and procurement workflow.
- Your workload is dominated by standard document types (invoices, receipts, W-2s) that map cleanly to Google's pretrained processors.
- You mainly need solid base OCR on common formats, and document complexity is low enough that processor accuracy limits don't bite.
FAQ
Common questions
Keep comparing
More comparisons
Reducto vs Gemini
An agentic document platform that orchestrates frontier models inside a production pipeline vs a general-purpose frontier LLM used raw for document work.
Read the comparisonReducto vs Extend
A complete agentic document platform with benchmark-leading extraction and flexible deployment vs a managed document workflow product.
Read the comparisonReducto vs Pulse
A complete agentic document platform proven at enterprise scale vs a focused document parser with a financial-documents emphasis.
Read the comparisonDocument work starts here
See the difference
Sign up with 15,000 free credits, run your hardest documents through both tools, and compare the output side by side.
