Document AI pricing in 2026: where the dollars actually go
The per-page rate for document AI ranges from $0.001 to $0.10 depending on the provider and what you are asking it to do. That is a 100x spread. Most buyers find this confusing because the providers do not publish the real cost drivers — they publish tier names and contact-sales buttons.
The cost is a function of three things: model size, infrastructure overhead, and the accuracy bracket you need. Understanding how those interact tells you which number is the right one for your workload.
Why pricing varies
A character-level OCR engine like Tesseract runs on CPU and costs almost nothing per page in raw compute. A frontier vision-language model like Llama-3.1-Nemotron-Ultra-253B requires GPU inference that costs real money — GPU-hours on H100s are not free, and per-token pricing on hosted APIs reflects that.
Infrastructure overhead adds another layer. AWS Textract bundles its table-extraction pricing with its broader document service. That means you pay for the full AnalyzeDocument pipeline even when your document is a clean digital PDF that needs only 5% of what Textract does. Landing AI does not publish per-page rates at all — their pricing is contract-negotiated, which typically means a minimum commitment in the five-figure range.
Accuracy bracket matters more than most buyers expect. Getting 99% character accuracy on a clean digital invoice is not the same problem as getting correct structure from a scanned clinical table with merged cells. The second problem requires a model 10–50x larger. The cost difference is roughly proportional.
The actual table
Public per-page rates as of 2026-Q2. Rates for AWS and Google are from their public pricing pages; verify before budgeting since they change.
| Provider | Approx. cost/page | Notes |
|---|---|---|
| Docira (Pro tier) | ~$0.012 typical | Multi-model routing; blended rate lower on simple pages |
| AWS Textract | $0.05–$0.10 | Tables/forms tier; AWS pricing page |
| Google Document AI | ~$0.10 | Form Parser processor; Google pricing page |
| Unstructured (hosted) | $0.001–$0.01 | Lower end is basic OCR; higher end is hi-res mode |
| Landing AI | No public pricing | Contact sales; contract-based, typically enterprise |
| Self-hosted vLLM | ~$0.001 at scale | Compute cost only; excludes engineering and ops overhead |
All rates are approximate 2026-Q2 public list prices. Verify against provider pricing pages before committing to a workload. Volume discounts apply at most providers.
What you are paying for
At $0.001/page, you are getting character-level text extraction from a document that already has a clean text layer. No structural reasoning. No table reconstruction. No merged-cell handling. For a corpus of clean digital PDFs where you only need the text, that is fine and the right choice.
At $0.10/page (Google Form Parser), you are getting a specialized model trained on form extraction with a full managed pipeline behind it. The price includes preprocessing, retry logic, result storage, and a well-maintained API. For US government forms and standardized invoices, Form Parser is accurate and the engineering lift is low.
At $0.012/page on Docira’s Pro tier, you are getting VLM-grade table extraction with routing metadata per page. The metadata — tier, provider, model, complexity score, confidence — is included in the response. Simple pages with digital text get routed to a $0.003/page Fast-tier model, which brings the blended rate below the nominal $0.012 on mixed corpora.
When the cheapest option is the right one
If your documents are clean digital PDFs and you need text but not structure, Unstructured’s basic tier or a local Tesseract setup is the correct answer. There is no reason to pay VLM rates for a document that a character-level OCR engine handles correctly.
The cases where the cheaper option becomes expensive are subtle. A corpus that is 80% simple and 20% complex tables looks cheap at $0.001/page until the 20% produces bad output that requires manual correction. At 10,000 pages/month and 4 minutes of human review per corrected page, that manual cost exceeds the per-page savings on the whole corpus.
The right cost calculation includes the cost of downstream errors, not just the per-page API charge.
How Docira’s per-page math works
The $0.012 Pro-tier figure is the typical rate for pages routed to the Expert tier. Fast-tier pages (complexity score below 0.3) cost $0.003. Pro-tier pages (score 0.3–0.8) cost $0.007. On a real mixed corpus, the blended rate is usually $0.005–$0.009.
Because the routing decision is logged per page, you can see exactly which pages triggered Expert-tier routing and why. That makes it possible to audit the cost distribution, not just the total invoice.
The routing trace is the receipt. Every line item is attached to a complexity score and a confidence value. If you are running a high-volume pipeline and want to reduce cost, you can inspect which pages are routing to Expert and whether the accuracy gain on those pages justifies it for your use case.