A complete, neutral reference for Amazon Bedrock Data Automation (BDA) in 2026: what it is (a single managed API that turns documents, images, audio, and video into structured output — extracted fields, summaries, transcripts, and more), standard output vs custom output via blueprints, how it feeds RAG and Knowledge Bases as the multimodal parser, how it compares to Textract, Comprehend, Rekognition and Transcribe, the use cases (intelligent document processing and media processing), what it costs — and how AWS credits make the whole build $0.
Bedrock Data Automation (BDA) is the part of Amazon Bedrock that turns "make sense of this file" — whatever the file is — into a single managed API call. The clearest one-line definition: it is a generative-AI-powered service that converts unstructured multimodal content (documents, images, audio, and video) into structured, application-ready output.
Almost every real workload eventually hits the same wall: the data that matters is unstructured. Invoices and contracts arrive as PDFs with tables and signatures. Claims come with photos. Customer interactions are recordings. Product and training content is video. None of it is a tidy row in a database, and software cannot act on a scanned PDF or an hour of audio directly — something has to read it first and emit fields, text, or labels a program can use. That conversion step — unstructured to structured — is the unglamorous, expensive plumbing behind a huge share of AI and automation projects.
Historically you built that step by wiring together a different specialized service for each file type: Amazon Textract for OCR and form/table extraction from documents, Amazon Comprehend for entities and sentiment over the resulting text, Amazon Rekognition for objects and moderation in images and video, and Amazon Transcribe for speech-to-text on audio — then your own code to stitch the outputs together, normalize them, and handle the cases each model got wrong. Every modality was its own integration, its own output format, and its own thing to operate.
BDA collapses that into one API and one consistent, structured response. You send it a document, image, audio file, or video, and it returns a JSON payload with the content already interpreted: a document comes back with its text, tables, and a summary; an image with a caption, detected text, and tags; audio with a transcript; video with chapter/scene segmentation, transcripts, and summaries. Because it is generative-AI-powered rather than template-driven, it handles complex, varied, real-world inputs — multi-column layouts, mixed tables and prose, low-quality scans, accented speech — far better than the rigid, per-template tools that came before it.
It is worth being precise about the shape of the value. BDA is not "a model" you prompt; it is a managed extraction service with two output modes (covered next) that you call through the Bedrock APIs (and the AWS Console, SDKs, and CLI). It outputs structured data, not a chat answer — JSON your application consumes — and it handles the orchestration, model selection, and post-processing internally. The point is to delete the multi-service glue, not to give you another raw model to manage.
Amazon Bedrock Data Automation is a single managed, generative-AI-powered API that turns unstructured documents, images, audio, and video into structured output — extracted fields and tables, summaries, transcripts, and scene/object labels — replacing the per-modality glue of Textract + Comprehend + Rekognition + Transcribe with one consistent JSON response.
BDA is genuinely multimodal: the same API accepts four kinds of input and returns a structured result tuned to each. Knowing what comes back by default for each modality is the fastest way to see where BDA fits in a pipeline.
You submit an input — a file in Amazon S3, typically — and tell BDA which kind of content it is (or let a project route it). What you get back depends on the modality. Here is the practical rundown of each, with the kind of structured output BDA produces:
Two things make this multimodal coverage more than a checklist. First, the output format is consistent across modalities — structured JSON with the interpreted content and confidence-style metadata — so downstream code does not need a bespoke parser per file type. Second, BDA is designed to process at scale: you point it at content in S3 and it runs asynchronously, which is what you want when the input is thousands of documents or hours of video rather than a single file in a request/response. The exact fields and capabilities per modality expand over time, so confirm current output details in the AWS Bedrock documentation when you scope a build.
BDA has two output modes, and the difference between them is the difference between "read this and tell me what is in it" and "extract these exact fields in this exact shape." Choosing the right one — and knowing how blueprints work — is the core design decision.
Every BDA request resolves to one of two modes: standard output, which gives you BDA's sensible defaults for the modality with zero configuration, or custom output, which uses a blueprint to return data shaped precisely to your application. They are not mutually exclusive across a workload — many pipelines use standard output for some content and blueprints for the documents that need exact fields.
Standard output is what BDA returns out of the box. For a document that means the extracted text, tables, and a summary; for an image, a caption, detected text, and tags; for audio, a transcript; for video, chapter segmentation with transcripts and scene summaries. You configure little or nothing — you send the file and get back a rich, structured representation. Standard output is the right call when you want to understand or index content rather than pull specific fields from it: feeding documents and media into search or RAG, generating summaries, building a content catalogue, or doing first-pass triage. It is also the fastest way to see what BDA can do with your actual data before you invest in blueprints.
Custom output is where BDA becomes a precise extraction engine. A blueprint is a schema you define that tells BDA exactly which fields to extract and what to call them — for an invoice, fields like invoice_number, vendor_name, line_items, subtotal, tax, total, and due_date. You describe each field (often in natural language, which is what lets the underlying model find it even when the layout varies), set its type, and the JSON comes back shaped to that schema rather than as generic text. Blueprints can also include normalization and validation — formatting a date consistently, coercing a number, or flagging a field that failed a rule — so the output is closer to something you can write straight to a database.
AWS provides a catalogue of sample blueprints for common document types (invoices, receipts, IDs, bank statements, and more) that you can use directly or clone and adapt, and you can author your own from scratch for documents specific to your business. The power of blueprints is that one blueprint generalizes across layout variation — because BDA reads the document with a model rather than matching pixel positions, the same invoice blueprint works across hundreds of vendors' invoice formats, which is exactly where the old template-based tools broke down.
In practice you organize this work into a BDA project: a reusable configuration that bundles your output settings and which blueprint(s) to apply, so your pipeline calls the project rather than re-specifying configuration on every request. A project can route different inputs to different blueprints and standardizes how a body of content is processed. Think of the blueprint as the schema and the project as the deployable unit that wires blueprints to your processing jobs.
Use standard output to understand, summarize, or index content (RAG, catalogues, search, triage) — no configuration, rich defaults. Use custom output via blueprints when you need specific named fields in a specific shape (invoices, forms, IDs, contracts) — one blueprint generalizes across layout variation. Package both into a project for production.
One of the most important places BDA shows up is not as a standalone tool but as the front end of a retrieval pipeline. If you are building RAG over real-world content — especially messy PDFs, images, or media — BDA is how that content becomes clean, retrievable text.
Retrieval-augmented generation (RAG) only works if the source content can be parsed into clean text to embed and retrieve. That is easy for tidy text files and hard for the documents organizations actually have: layout-heavy PDFs full of tables, scanned pages, slide decks, images that carry meaning, and audio/video that contains the answer but is not text at all. Garbage parsing produces garbage chunks, and no embedding model or vector store can rescue retrieval from a corpus that was extracted badly.
BDA is selectable as the multimodal parser inside Amazon Bedrock Knowledge Bases for exactly this reason. When you build a Knowledge Base over complex or mixed-media content, you can have BDA do the parsing step — turning each document, image, or media file into a faithful structured/text representation (tables preserved, scenes transcribed) before the Knowledge Base chunks, embeds, and stores it. The result is dramatically better retrieval over the kinds of corpora where naive text extraction fails, and it extends RAG beyond documents to images and media that a text-only parser could never index.
Even outside Knowledge Bases, BDA's structured output is a natural RAG and analytics feed. You can run BDA to extract and transcribe a corpus, then write the results to a vector store, a search index, or a database yourself — the same way you would use the Knowledge Bases Retrieve API for managed ingestion while owning the rest of the pipeline. The pattern is the same: BDA handles the unstructured-to-structured conversion; the retrieval or analytics layer handles the rest. See the Bedrock Knowledge Bases and RAG on AWS siblings for the full retrieval architecture; this page is about the extraction layer that sits in front of it.
BDA is the multimodal parser for your RAG pipeline — selectable inside Bedrock Knowledge Bases or run standalone — turning layout-heavy PDFs, images, audio, and video into clean, structured, chunk-ready text so retrieval works over content a text-only parser could never index.
BDA overlaps with the older AWS AI services it can replace, and the natural question is when to use BDA versus the specialized service. The honest answer: BDA is the unified, generative-AI-powered successor for most new multimodal extraction work, while the point services remain strong for narrow, high-volume, deterministic tasks.
The older services are each single-modality specialists. Amazon Textract does OCR and form/table extraction from documents — excellent at pulling text, key-value pairs, and tables, but document-only and historically tuned around specific document structures. Amazon Comprehend is an NLP service for text — entities, key phrases, sentiment, language, PII detection, and custom classification — but it works on text you already have, not on the raw file. Amazon Rekognition handles image and video analysis — objects, faces, moderation, text-in-image — and Amazon Transcribe does speech-to-text. To process a mixed pile of content with these, you orchestrate several of them and merge the results.
BDA's pitch is unification plus generative understanding. One API spans all four modalities, returns a consistent structured format, and — because it reads content with foundation models rather than fixed templates — generalizes across layout and quality variation that breaks template-based extraction. For a new workload that needs to handle varied documents, or that touches more than one modality, or where you want named-field extraction that survives format drift, BDA is usually the simpler and more capable choice, and it is the path AWS is investing in for multimodal content understanding.
The point services still earn their place in specific situations: an extremely high-volume, narrow, latency-sensitive task on a single modality where a specialized model is cheaper or faster per unit; an existing production pipeline already built on Textract or Transcribe that works well and does not need re-platforming; or a need for a very specific capability one of the point services exposes. The pragmatic guidance: reach for BDA first for new multimodal or document-understanding work, and keep the specialized service when you have a narrow, deterministic, high-scale job it already does well. Confirm current capabilities and overlaps in the AWS documentation, since these services evolve.
| Service | Modalities | What it returns | Approach | Best for |
|---|---|---|---|---|
| Bedrock Data Automation | Documents · image · audio · video | Unified structured JSON: fields, tables, summaries, transcripts, scene labels | Generative-AI, generalizes across layout/quality | New multimodal + document-understanding work; named-field IDP; RAG parsing |
| Amazon Textract | Documents only | OCR text, key-value pairs, tables | Specialized document ML | High-volume, narrow OCR/forms on known document types |
| Amazon Comprehend | Text only (input already text) | Entities, sentiment, key phrases, PII, classification | Specialized NLP | NLP over text you already extracted; custom text classifiers |
| Amazon Rekognition | Image · video | Objects, faces, moderation, text-in-image | Specialized vision ML | Narrow, high-scale image/video detection tasks |
| Amazon Transcribe | Audio | Speech-to-text transcript | Specialized ASR | Pure, high-volume transcription pipelines |
BDA's two flagship use-case families map to its two hardest modalities: documents (intelligent document processing) and media (audio and video understanding). These are where it most cleanly replaces a pile of custom code.
IDP is the highest-volume use case. Any business that receives documents at scale — invoices in accounts payable, claims in insurance, statements and IDs in financial-services onboarding, contracts in legal, forms in healthcare and government — needs to turn those documents into structured data that flows into a system. BDA with blueprints does this: define the fields once, point BDA at the document stream, and get clean JSON per document, with normalization and validation, even as formats vary across senders. It replaces both rigid template tools (which break on a new layout) and manual data entry (which is slow and error-prone). Common patterns: AP automation (extract invoice fields, match to POs), onboarding/KYC (read IDs and proofs), claims intake (read forms plus attached photos), and contract analytics (pull terms, dates, parties).
The second family is media. Organizations sit on huge archives of audio and video — call recordings, meetings, lectures, broadcast and marketing footage, user-generated content — that are effectively opaque to software. BDA makes them legible: transcribe and summarize calls for QA and analytics; segment and caption video into searchable chapters; generate descriptions and metadata for media catalogues; auto-moderate user-generated images and video; and produce transcripts and summaries that feed search and RAG. For media companies, e-learning platforms, contact centres, and any product with a large content library, BDA turns a passive archive into a searchable, analyzable, structured asset without a human reviewing every minute.
Beyond those two families, BDA is increasingly used as a component inside larger GenAI systems: as the parser that feeds a Knowledge Base (RAG over real documents and media), as a tool an agent calls to read an attachment mid-workflow, or as the ingestion step in an analytics pipeline that lands structured output in a data warehouse. In these cases BDA is not the product — it is the reliable unstructured-to-structured stage that everything downstream depends on.
Getting from zero to a working BDA pipeline is a short path — the service is managed, so most of the effort is in modelling your output, not operating infrastructure. Here is the shape of a typical setup.
The end-to-end flow has a small number of moving parts, and you can stand up a prototype in the Console before writing any code:
A few practical notes. Build blueprints iteratively against your messiest real samples, not clean examples — the value of BDA is handling variation, so test it on the documents that broke your old pipeline. Plan a human-in-the-loop path for low-confidence extractions in high-stakes IDP (payments, compliance) rather than auto-committing everything. And remember the output is structured data, not a finished feature — the engineering after BDA (validation rules, downstream wiring, review UX) is where a production system is won or lost, which is exactly the work a vetted AWS partner can compress. Confirm current setup details, limits, and regional availability in the AWS Bedrock documentation.
BDA does not have a single price; it is priced by what you process, by modality, with a premium for custom extraction. Understanding the shape tells you where the money goes and why AWS credits cover all of it during the build.
BDA pricing is usage-based and per-modality. Documents are billed per page; images per image; and audio and video per minute of content processed. On top of that, custom output (blueprints) carries a higher rate than standard output — you pay a premium for precise field extraction versus default understanding — and richer processing (for example, deeper video analysis) costs more than lighter processing. So a back-of-envelope estimate is "volume × per-unit rate for that modality × (standard vs custom)," plus the usual Amazon S3 storage for your inputs and outputs and any compute you run around it. Exact figures are best read off the AWS pricing page, since rates differ by modality and processing depth and change over time — treat the structure here as the durable part and the numbers as something to confirm. (Representative as of 2026 — check the AWS Bedrock pricing page for current rates.)
Two cost patterns are worth internalizing. First, modality drives cost: a corpus of thousands of single-page documents is cheap; hours of video processed with deep scene analysis is the expensive end, because per-minute media processing adds up. Match the processing depth to what you actually need. Second, standard vs custom is a real lever: if you only need to index or summarize content, standard output is cheaper than running every file through blueprints — reserve custom extraction for the documents where named fields genuinely matter. At pilot scale — proving an IDP or media use case on a sample set — the spend is typically modest; it grows with the volume and the processing depth you put into production.
Which is exactly why so many teams build this on AWS credits and pay nothing out of pocket. BDA usage — standard and custom output across every modality — is credit-eligible and draws down your AWS credits automatically, as does the S3 storage and any surrounding compute, the embeddings/vector store if it feeds a Knowledge Base, and the inference if a model acts on the output. The relevant pools are AWS Activate (commonly up to $100K for institutionally-funded startups), a dedicated Bedrock / generative-AI POC pool ($10K–$50K) aimed squarely at proving out a use case exactly like document or media automation, and the competitive Generative AI Accelerator (up to $1M). Most of these pools are partner-filed through the AWS Partner Network rather than a public form — which is the gap CloudRoute fills: we match you to the right pool for your stage and to a vetted AWS DevOps/ML partner who files the credit application and builds the BDA pipeline (blueprint design, the S3 + async wiring, the Knowledge Base or database integration, and the human-review path). The customer pays $0 — AWS funds the credits, AWS pays the partner through engagement-funding programs, and the partner pays CloudRoute a routing commission. See AWS credits for generative-AI startups and Bedrock POC funding for the full mechanics.
per page (documents) + per image + per minute (audio/video) — with a premium for custom output (blueprints) and deeper processing — plus S3. Representative as of 2026; confirm current rates on the AWS pricing page. All of it is AWS-credit-eligible, which is why the build can be $0 while you prove the workload out.
The single most consequential design choice in a BDA pipeline is standard versus custom output. Here is how the two modes compare on the dimensions that actually drive the decision. Cost notes are representative as of 2026 — confirm current rates on the AWS pricing page.
| Dimension | Standard output | Custom output (blueprints) |
|---|---|---|
| What it returns | Modality defaults: doc text + summary, image caption + tags, audio transcript, video chapters + scenes | Exactly the named fields you define, in your JSON shape |
| Configuration | None — send the file, get rich defaults | Author a blueprint (fields, descriptions, types, validation) |
| Handles layout variation | Yes — reads content, not templates | Yes — one blueprint generalizes across formats |
| Relative cost | Lower per unit | Higher per unit (premium for precise extraction) |
| Normalization / validation | Not the point — raw understanding | Built into the blueprint (format dates, coerce numbers, flag failures) |
| Best for | Indexing, summarizing, RAG parsing, triage, catalogues | IDP with specific fields: invoices, IDs, forms, contracts |
| Typical companion | Bedrock Knowledge Bases / search | A database, ERP, or downstream workflow |
Situation: The team was drowning in invoice data entry. Their suppliers sent invoices in dozens of different layouts — some clean PDFs, many scanned, a few photographed — and the rigid template-based OCR tool they had tried broke every time a new vendor format appeared, so a person ended up re-keying the exceptions. They also had a backlog of supplier contracts they wanted to extract key terms and dates from. An internal attempt to stitch together OCR plus their own field-matching code had stalled, and they did not want to burn runway on processing costs and engineering time while still proving the workflow out.
What CloudRoute did: CloudRoute matched them in under 24 hours to an EU AWS partner with document-automation experience. The partner built it on Bedrock Data Automation: authored a custom invoice blueprint (cloned from AWS's sample and adapted) that extracted vendor, invoice number, line items, tax, total, and due date — and generalized across all 30+ layouts because BDA reads the document with a model rather than matching positions; added normalization so dates and amounts came back consistent, and a confidence threshold that routed low-confidence extractions to a human-review queue instead of auto-committing. A second blueprint handled the contracts. The structured JSON landed straight in their ledger via an async pipeline reading from S3. In parallel, the partner filed a Bedrock POC credit application plus an Activate Portfolio application to fund the build.
Outcome: Within three weeks, invoices flowed in as structured data across every vendor format, manual re-keying dropped to the small share that hit the review queue, and the contract backlog was extracted into searchable fields — and the entire processing cost (BDA per-page extraction, S3, the surrounding compute) was covered by the approved credits, so the team paid $0 during the build and early rollout. CloudRoute's commission was paid by the partner from AWS engagement funding, not by the customer.
volume: ~8k invoices/mo across 30+ layouts · time to live: < 3 weeks · credits secured: POC + Activate · out-of-pocket during build: $0
Whatever a Bedrock Data Automation pipeline would cost — per-page document extraction, per-minute media processing, blueprints, S3 — AWS credits can cover it. CloudRoute routes you to the right credit pool (Activate up to $100K, Bedrock POC $10K–$50K, GenAI Accelerator up to $1M) and a vetted AWS partner to design the blueprints, wire the S3 + async pipeline, integrate it with a Knowledge Base or your database, and add the human-review path. Customer pays $0.