Reducto is the complete agentic document platform for AI teams shipping production AI on messy real-world documents. Bedrock Data Automation (BDA) is AWS's higher-level document AI layer built on top of Textract and the Bedrock model platform. The pitch is that AWS customers can move beyond raw Textract primitives into a more packaged document automation experience without leaving the AWS account.

That positioning makes sense on paper. In practice, the question is what the packaging sits on: BDA inherits Textract's primitives, and the gap between those primitives and a document-native platform is where this comparison gets decided.

What Bedrock Data Automation is and where it could be strong

BDA layers document automation on top of the Textract OCR/extraction primitives and the Bedrock model marketplace. AWS-native customers can route documents through it, pull structured outputs, and integrate with the rest of their AWS stack — IAM, S3, Bedrock-hosted models for downstream reasoning, the AWS billing relationship, and the existing security review the customer has already done with AWS.

Where BDA could genuinely win:

• AWS-native procurement. No new subprocessor, no new MSA, AWS Marketplace billing, existing credits applicable. For an AWS-standardized buyer, that path of least resistance matters.

• Integration with the Bedrock model layer. If the downstream reasoning step is already a Bedrock-hosted model, keeping the document step in the same surface area is operationally cleaner than crossing into a separate vendor.

• Determinism from cloud primitives. Like the other cloud providers, BDA inherits the deterministic behavior of the underlying Textract-style primitives rather than the soft judgment of a VLM-first approach.

• An easier upgrade path from Textract. For customers who started on raw Textract and outgrew it, BDA is potentially a more direct next step than swapping to a third party.

If AWS surfaces BDA effectively to its existing Textract base and the product depth catches up, it could become a real default for AWS-native procurement buyers.

Where Reducto wins today

BDA is a more direct starting point than raw Textract, but it inherits Textract's primitives — and the dimensions buyers evaluate hardest in production (figures and charts, checkboxes, sub-region citations, long-document depth) are exactly where those primitives fall short. Reducto treats each of them as first-class.

Reducto wins today because:

• You can verify it in a day. Test Reducto on your own documents and see grounded results — every extracted value carries a bounding-box citation back to its source region on the page.

• Document depth is the product, not a layer. Reducto is built document-native from the ground up — orchestrated frontier and in-house models, agentic VLM multipasses, layout parsing, structured extraction, and citation regions are the product. There is no upstream OCR primitive whose limitations bleed through.

• The full toolkit ships together. Five APIs — Parse, Extract, Classify, Split, Edit — plus the Reducto Studio platform, across 30+ filetypes, all in one place. No stitching together AWS services per document task.

• Performance on hard documents. Tables that span pages, figures and charts with extractable datapoints, checkboxes, handwriting, multilingual content — the long tail that cloud primitives have historically struggled with.

Figure extraction, checkboxes, and handwriting have historically been weak points for cloud OCR primitives, and BDA inherits that lineage. Unless the buyer specifically values AWS-native procurement above document quality, the comparison usually resolves in Reducto's favor on the merits.

Named-customer proof

Reducto is trusted by leading AI teams at companies like Harvey, Scale AI, and Vanta for production document workflows, with over 4 billion pages processed in production. Many of these teams run inside AWS — Reducto deploys hosted, in your VPC, on-premises, or fully air-gapped — so the AWS-native argument is rarely about infrastructure. It is about procurement preference, and that is a separate decision from product fit.

Honest stance on benchmarks

Vendor benchmarks (Reducto's included) carry bias toward the vendor publishing them. One independent exception is worth knowing: on LongExtractBench — independently audited, validated, and published by micro1 — Reducto ranked first of seven systems with 99.6% precision, 99.6% recall, and zero failures across 225 long documents (micro1 didn't measure BDA, so treat that as verification of Reducto's accuracy claims rather than a head-to-head). Beyond that, the only benchmark that actually predicts production performance is the one you run on your own documents. We encourage every prospect to bring 20 to 50 representative documents, run them through Reducto and BDA side by side, and compare extraction quality, citation accuracy, handling of the long tail (tables, checkboxes, handwriting, figures), and the operational story — how long does the first integration take, how do new document types get onboarded, what does the support relationship look like when something breaks in production at 3 a.m.

Complement, displace, or coexist

For AWS-standardized enterprises with strict cloud-vendor consolidation rules, BDA may need to stay in the stack for procurement reasons. Many of those customers still add Reducto for the document AI layer that needs depth, citations, and adaptability — the cases their AWS-native tooling cannot reach. Reducto can run in your AWS VPC, which keeps the data residency story clean.

For teams whose strategic surface is the AI product (not the cloud relationship), Reducto displaces BDA outright on the document step.

Most optimal, not the cheapest

Reducto is not the cheapest option, and the AWS Marketplace billing path can make BDA appear simpler on the invoice. For concrete anchors: BDA's published document pricing runs roughly $0.010 per page for standard output to $0.040+ per page for custom output (blueprints past 30 fields add per-field charges), while Reducto starts with 15,000 free credits, then $0.015/credit — a standard parse is 1 credit/page and includes text, layout, tables, and OCR, and batch processing costs 20% less with a 12-hour completion guarantee. The relevant comparison is total cost of ownership — engineering time to get to production accuracy, time to onboard new document types, the ongoing maintenance of stitched-together services. Teams that have done the comparison on real workloads usually find that Reducto's document depth and complete toolkit shorten the path to production by enough to offset per-page pricing.

How to decide

You are likely a fit for Bedrock Data Automation if your organization is strictly AWS-standardized, document AI is one capability among many in your AWS roadmap, your document mix is well-known and not changing fast, and procurement simplicity outranks extraction depth.

You are likely a fit for Reducto if you are building production AI on real-world documents, you need grounded outputs your agents and reviewers can trust, your document mix is drifting or expanding, and you want one platform that handles the full parse-to-extract-to-edit lifecycle across 30+ filetypes.

Reducto wins on the merits: per-value bounding-box citations, Deep Extract's agentic self-verifying extraction, zero failures across 225 long documents on LongExtractBench (independently audited, validated, and published by micro1), deployment from hosted to fully air-gapped, and over 4 billion pages processed in production.