Document Extraction API Comparison 2026: How to Choose Between Dokyumi, Textract, Document AI, Azure, Mindee, and Docparser

Every document extraction API demo looks the same: PDF in, JSON out, applause. The differences that decide whether you're happy six months later live elsewhere — in the pricing model, the setup effort, the shape of the output, and what happens when extraction is almost right. This comparison is organized around those four dimensions rather than a feature checklist.

A disclosure and a promise: Dokyumi is our product, so we have a horse in this race. The promise is that this post stays factual and structural — no invented benchmark numbers, no straw-man pricing figures for competitors (their rates change and depend on volume and features; check their pricing pages for current numbers). Where a competitor is genuinely the better fit, we say so.

The six contenders, in one paragraph each

AWS Textract is Amazon's OCR and document-analysis service: raw text, forms (key-value), tables, and specialized APIs for invoices/receipts and IDs, priced per page per feature. It's an infrastructure primitive — powerful, composable, and yours to assemble. We've written a fuller treatment in our Textract alternative guide.

Google Document AI takes a processor-based approach: pre-trained processors for common document types, custom processors you train yourself, priced per page/processor. Strong ML pedigree, GCP-native. Full comparison in our Document AI alternative guide.

Azure Document Intelligence (formerly Form Recognizer) is Microsoft's equivalent: prebuilt models for invoices, receipts, IDs, and W-2s, plus trainable custom models, priced per page. The natural choice if your stack is already Azure-committed.

Mindee offers pre-built extraction APIs for specific document types plus a builder for custom ones, with developer-friendly ergonomics and per-document pricing on its APIs.

Docparser is template-based: you define parsing rules per document layout, and it excels when your documents are genuinely uniform — the same vendor, the same format, forever. Priced by monthly parsing credits.

Dokyumi is schema-first: you define the fields you want (in plain English or from samples), use the shared extraction endpoint with a schema slug, and receive extracted data with confidence and validation details — monthly credit tiers (Free / $99 Starter / $499 Growth / quoted Enterprise), with one credit covering a document up to five pages and a 50-page self-serve cap.

Dimension 1: pricing model

This is less about who's cheapest — that depends entirely on your volume and mix — and more about which failure mode you prefer.

• Per page, per feature (Textract, Document AI, Azure): scales smoothly from zero, but bulk cost is a function of page counts you don't control and feature combinations you have to model. Budgeting requires a spreadsheet; surprises land on the invoice.
• Per document (Mindee) / per credit (Docparser): more predictable per unit, with plan tiers; specifics vary by document type and plan.
• Page-weighted credit tiers (Dokyumi): $99 covers 500 credits, $499 covers 3,000, and Enterprise is quoted — one credit covers up to five pages, longer documents use additional credits, and feature flags do not multiply the rate. The failure mode is hitting your tier ceiling, which is a plan-upgrade conversation rather than a bill spike.

Rule of thumb: metered pricing suits spiky or tiny volume and raw-OCR use cases; flat tiers suit sustained business pipelines where forecastability matters. The bulk-volume math is worked through in our bulk extraction guide.

Dimension 2: setup effort

How long from zero to first useful structured result?

• Cloud primitives (Textract, Document AI, Azure): you need a cloud account, IAM, SDK wiring, and — the real cost — post-processing code that turns key-value pairs and table geometry into your fields. For custom document types, add model training and labeled samples. Realistic setup is measured in days to weeks of engineering, and the result is a system you own and maintain.
• Docparser: fast when your layout is uniform; each new layout is a new template to build and maintain. Setup effort scales with layout diversity, which is the catch for anything vendor-facing like invoices.
• Mindee: quick for its pre-built types; custom types go through its builder.
• Dokyumi: describe your fields in plain English (or upload samples for AI schema inference), then send a document and schema slug to the shared extraction endpoint. Direct API calls return synchronously. Webhooks are separate and apply only to documents submitted through a configured upload site.

Dimension 3: output shape

The most underrated dimension. Ask of any candidate: whose vocabulary is the JSON in?

Textract and its cloud peers return their structures — blocks, key-value pairs, table cells with geometry, per-feature response envelopes. It's complete and it's honest, but mapping it to vendor_name and total_amount is your code, and that mapping layer is where these integrations quietly get expensive. Prebuilt processors (Document AI, Azure, Mindee) return typed fields for the document types they support — great when your document is on the list, and a gap when it isn't.

Schema-first systems target the fields you named and check the result against the types you declared. Dokyumi returns model-produced data plus a confidence map, a validation block, and processing metadata in the same envelope across document types. A review result can contain validation errors, so verify those before treating the data as schema-valid.

Dimension 4: review workflows

Extraction is probabilistic; production systems need a plan for the uncertain minority. The questions to ask: Does the API tell you which fields are uncertain? Is there a first-class status that separates "trust it" from "check it"? Or are you thresholding raw confidence numbers yourself?

The cloud primitives return per-item confidence scores and leave the workflow to you — build your own thresholding, flagging, and review UI. Human-in-the-loop tooling exists in those ecosystems but is its own integration. Dokyumi's successful extractions return completed or review; inspect both validation.low_confidence_fields and validation.errors on review results. Hard failures use a non-2xx error envelope. Whatever you choose, do not skip this dimension — our guide to extraction accuracy and validation explains why confidence handling matters more than headline accuracy claims.

So which should you choose?

• You're an AWS/GCP/Azure-native team with engineers to spend and unusual scale or compliance needs: the cloud primitives (Textract, Document AI, Azure Document Intelligence) give you maximum control. Budget real time for the mapping and review layers.
• Your documents are perfectly uniform and few in layout: Docparser's template model is simple and effective. Revisit if vendor variety grows.
• Your document type is on a prebuilt list and you want typed fields fast: Mindee (or a cloud prebuilt processor) is a quick path.
• You want schema-defined fields from supported PDF and image inputs, with confidence and validation details: evaluate Dokyumi on representative layouts, inspect every review result, and compare the current credit tiers with your document mix.
• You're feeding a RAG pipeline with clean text rather than extracting fields: that's a different job — see our LlamaParse comparison for where the line sits.

Migration considerations: switching later is real work

One more dimension worth weighing before you commit: how expensive is leaving? Document extraction APIs are sticky in a specific way — not through contracts, but through the code shaped around their output.

If you build on a cloud primitive, your mapping layer (blocks-and-geometry in, your fields out) encodes that vendor’s response structure everywhere. Migrating means rewriting it against a new structure and re-validating every document type. Template systems are sticky through the templates themselves: dozens of hand-built layouts that do not port anywhere. Schema-first systems hold the coupling at the schema definition — your field names and types — which is the part you would have to specify for any successor anyway.

Practical advice regardless of what you pick: keep your own canonical field vocabulary in your database rather than adopting the vendor’s response as your storage format, archive source documents so a future re-extraction is possible, and when you do switch, dual-run both APIs on the same live documents for a couple of weeks and diff the results — it is the only migration test that means anything.

And if you are consolidating from several tools (one for invoices, one for statements, a template tool for the legacy formats), note that the consolidation itself is often the win: one response envelope, one review workflow, one bill. That is a large part of why teams end up on schema-first platforms even when a point solution scored fine on its single document type.

How to actually decide

Don't decide from comparison posts — including this one. Every serious contender has a free tier or trial. Take your twenty ugliest real documents (not your cleanest), run them through your two or three shortlisted APIs, and score three things: how much code stood between the raw response and your database schema, how the system behaved on the documents it got wrong, and what next month's bill would be at 10× volume. An afternoon of that beats any vendor's benchmark table — ours included.

Dokyumi's free tier is 25 credits a month with 2 schemas, no card required — run your worst documents through it and see where the confidence scores land.