AWS Textract alternative

Textract finds fields.Meibel fills your schema.

Define the output once and get the same shape back from every document, at any length. Then search it, query it in SQL and build agents on it, on one platform.

1 pageTextract's limit for a synchronous PDF request.
3,000 pagesIts asynchronous limit, a quota AWS lists as fixed.
1 schemaOn Meibel, the same output shape for every document, up to 1 GB.

Meibel vs AWS Textract

One is a set of OCR APIs.
One runs the whole workload.

AWS Textract

OCR and form-extraction APIs, priced per feature: text detection, layout and tables, forms, and queries. It returns blocks, key-value pairs and tables with a confidence score per element. Search, a warehouse, agents and a review step are other services or your own code.

Meibel

Documents become data. Data becomes agents. Meibel reads any document zero-shot into a schema you define, stores it for search and SQL, scores every step, and runs agents on it inside our guardrails, we call them Hardrails.

What you still build

Count what sits around
the OCR call.

RequirementMeibelAWS Textract

Output in your schema

Define it once. It holds across every document type, zero-shot.

Forms returns the key-value pairs it detects. Queries answers one question per call. The mapping to your schema is your code.

Long documents

No practical page ceiling, up to 1 GB, same schema out.

1 page synchronous, 3,000 pages and 500 MB asynchronous.

Handwriting

Read alongside typed text, stamps and annotations.

Documented for the standard English alphabet and ASCII symbols.

Search and SQL

Semantic search and SQL over the extracted tables, on one corpus.

Not in scope. The index, warehouse and query layer are yours.

Acting on a low score

Every step scored on the checks you choose. You decide what a low score triggers: a retry, a person or a gate.

A score per element. Routing is your code; human review is Amazon Augmented AI, a separate service.

Agents and isolation

Agents built, versioned and run on the same data, inside Hardrails that set what each one can see and call.

Not in scope. IAM controls who calls the API, not what a model reads at query time.

What Meibel adds

One platform from the page
to the agent.

01 · DOCUMENTS

One schema, every format.

A PDF, a spreadsheet, an email or a photo of a form comes back in the same shape, each value scored and located.

02 · DATA

A corpus you can query.

Extracted tables land in a database. Agents write SQL, so the database does the math, not the model.

03 · AGENTS

Agents that show every step.

Run them in Claude, Copilot, your own apps or in batch, with every step scored and sourced.

What teams ask before they switch.

Which of you is cheaper?

On plain OCR, Textract: Detect Document Text is $0.0015 a page. On form and table extraction, Meibel: Textract's Forms with Tables is $0.065 a page, against $0.008 for a Meibel text page and $0.04 for an advanced page. Neither number includes the search, warehouse, review and agent layers around it.

We're on AWS. Does this mean leaving?

No. Meibel runs in Meibel Cloud or on your own infrastructure, so your storage, your warehouse and the models you already call stay where they are. LLM costs pass through at cost.

What about data use?

By default, AWS may use content processed by Textract to improve its services unless your organization sets an AI services opt-out policy. It's one policy at the organization root, and worth setting. Meibel is SOC 2 Type II and HIPAA compliant.

When is Textract the right choice?

When plain OCR on short documents is all you need. It's reliable and cheap. Teams move to Meibel when they need their own schema, long documents, and the layers above the parse.

Try it on the file that timed out

Bring the document your pipeline couldn't finish.

The 4,000-page case file. The scanned form with handwriting in the margin. Get it back in your schema, scored and sourced.

Based on AWS's public documentation, retrieved September 2026: pricing, limits, FAQ, AI services opt-out. We update this page when products change. See the use cases.

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