When AWS Textract is the right call
Most teams who search for a Textract alternative are not unhappy with its OCR. They are looking because the job changed around it. Before you shortlist anything, check whether these four still hold:
- You are AWS-native — IAM, S3 triggers, Step Functions, and billing are already in place, and one more managed service costs nothing in operations.
- The job is OCR, forms, and tables — text with geometry, key-value pairs, table cells, and query answers from a page are Textract's core, and it does them well.
- Your documents are in its prebuilt set — expenses such as invoices and receipts, identity documents, and lending packets have purpose-built analysis.
- You will own the pipeline — you have the engineers to turn blocks into fields, to add validation, and to wire Amazon A2I for human review.
If all four hold, staying is the better decision, and nothing below will change that.
Where it breaks
- Output is blocks, not your schema — Textract returns text blocks, key-value pairs, table cells, and query answers. Mapping those to a typed object with the fields your system of record expects is a second step, in your code or in an LLM.
- Semantic fields beyond the layout — a payment term inside a paragraph, a party identified by role, a value derived from two others. Queries help; they are not a schema.
- Validation, review, and audit are assembled, not included — low-confidence routing, business rules, reviewer identity, and a per-field trail are stitched together from A2I, Step Functions, a database, and logs.
- Documents outside the prebuilt set — a custom form, a mixed packet, a 60-page statement. Generic analysis runs, but accuracy and cost are yours to measure, page by page.
- You are not, or no longer, only on AWS — the primitives are cloud-bound, and so is the pipeline you built around them.
Amazon's own answer to the first three is Bedrock Data Automation, which sits above Textract and returns schema-shaped output. It is worth a look if you are staying in AWS. Checked 11 November 2026.
Seven alternatives
Ordered by how often teams leaving Textract tell us they shortlisted them, each linked to a side-by-side page where we have one. We cite no prices, because they change faster than this page does.
1. Google Document AI
The closest like-for-like move for a hyperscaler buyer: pretrained processors for invoices, receipts, and identity documents, a custom extractor trained on your own samples, and a layout parser for retrieval. Consider it when you run on Google Cloud or are multi-cloud and want a managed processor per document type. The workflow layer is still assembled from cloud services. Cogneris vs Google Document AI.
2. Azure Document Intelligence
The broadest prebuilt catalog among the hyperscalers (invoices, receipts, identity documents, tax forms, contracts, and more) plus custom models, now part of Azure AI Foundry. Consider it when your documents are in that catalog and your stack is Microsoft. Same build-it-yourself workflow as Google. Cogneris vs Azure Document Intelligence.
3. Reducto
Developer-first parsing and schema extraction with citations back to the source region. Consider it when parsing quality on dense layouts, tables, and long documents is the first thing you measure, and when the consumer is an agent or a retrieval pipeline. Validation, review, and audit stay yours. Cogneris vs Reducto.
4. Extend
Parse, extract, classify, split, edit, and verify primitives with multi-step workflows and a dashboard for iterating on a schema. Consider it when you want to compose your own pipeline from primitives and see each step, instead of stitching AWS services. Cogneris vs Extend.
5. LlamaParse
LlamaIndex's parsing service: documents to markdown and structured chunks, with LlamaExtract adding schema-based extraction. Consider it when the destination is a LlamaIndex retrieval pipeline rather than a system of record. Cogneris vs LlamaParse.
6. Mistral OCR
An OCR model served as an API: pages in, markdown with tables and images out, with strong multilingual coverage. Consider it when Textract's OCR is the part you want to replace and you will prompt a second model for the fields. It is OCR, not extraction, so schema, confidence, and review remain downstream work.
7. ABBYY Vantage
Low-code enterprise IDP with a catalog of document skills and a designer for the workflow around them. Consider it when operations, not engineering, will own the document workflow and a suite is the expected shape. The opposite trade from Textract: less to build, less to control from code. Cogneris vs ABBYY Vantage.
Where Cogneris fits
Disclosure — this is our site, which is why Cogneris sits outside the numbered list. Cogneris replaces the second step and the stitched workflow: typed JSON against your schema with per-field confidence and citations, validation rules, review queues for low-confidence fields, webhook delivery, portal intake for the people sending the documents, tenant controls, and a per-field audit trail with model version, prompt version, reviewer ID, and validation status. Pay-per-page, with 400 free credits and no card.
Where we fall short against Textract: we are not AWS-native, so integration is REST and signed webhooks rather than IAM and S3 triggers; we are a narrower vendor with a shorter track record; and we are not the cheapest raw OCR primitive if OCR is all you need. Choose Cogneris when the workflow around extraction is what you need to buy. See the extraction API or the platform.
At a glance
| Option | Strongest at | Consider when |
|---|---|---|
| AWS Textract (stay) | OCR, forms, tables, queries; expense, identity, and lending analysis | AWS-native, documents in the prebuilt set, and you own the pipeline |
| Google Document AI | Pretrained processors, custom extractor, layout parser | You run on GCP or multi-cloud and want a managed processor per type |
| Azure Document Intelligence | Broadest prebuilt catalog, custom models | Microsoft stack and documents in the catalog |
| Reducto | Parsing quality, schema extraction, citations | Dense layouts and long documents feeding agents or retrieval |
| Extend | Composable primitives with workflows and a dashboard | You want to compose your own pipeline and see each step |
| LlamaParse | Markdown and chunks for LlamaIndex, LlamaExtract for schemas | The destination is a retrieval pipeline |
| Mistral OCR | Multilingual OCR to markdown as a model | You are replacing the OCR step only |
| ABBYY Vantage | Low-code skills and workflow designer | Operations owns the workflow and wants a suite |
| Cogneris (our product) | Extraction plus validation, review, portals, and audit trail in one API | The workflow is what you need to buy, not only the fields |
Cogneris vs AWS Textract, side by side
AWS Textract is strongest around OCR, forms, tables, and AWS-native document analysis. Cogneris is built for teams that need document AI to become production workflow infrastructure: schema-based extraction, citations, validation, human review, webhook delivery, tenant controls, and audit trail.
| Capability | Cogneris | AWS Textract |
|---|---|---|
| Primary fit | Document AI platform for API-first workflows and portals | OCR, forms, tables, and AWS-native document analysis |
| Extraction output | Typed JSON, confidence, citations, validation, audit metadata | Blocks, key-value pairs, table cells, and query answers |
| Workflow layer | Review queues, portal intake, reminders, webhooks, QA state, per-field confidence | Assembled from A2I, Step Functions, and other AWS services |
| Engineering control | REST API, schemas, async jobs, validation rules, per-tenant controls | SDKs and IAM inside AWS; strong where its product model matches your use case |
| Best buyer | Engineering, product, operations, and compliance teams sharing one document workflow | AWS-native teams with a use case that maps tightly to Textract's core product |
Choose Cogneris when
You need extraction, validation, review, audit trail, and portal workflow in one API-first platform.
Choose AWS Textract when
Your use case maps directly to Textract's strongest product surface and you already accept building the workflow in AWS.
Evaluation tip
Test with your hardest 25 documents, not a demo set. Compare field accuracy, citation quality, latency, review effort, and total platform work.
How to run the evaluation
Take your hardest 25 documents, not a demo set, and run the same files through Textract and each alternative. Compare field accuracy, citation quality, schema stability, latency, review effort, and the total work to reach your system of record. Ask whether each platform returns source citations with every field, how schema changes are versioned, what happens to low-confidence fields, how webhook retries are signed, and whether audit logs include model version, prompt version, reviewer ID, and validation status. Our document extraction benchmark guide describes the method, and we run a 7-day evaluation that produces an accuracy and latency report you can show your CTO.