What Does AI Document Processing Cost per Reviewed Document?

Quick answer: Cost per reviewed document is inference, plus infrastructure and operations, plus the human time to check and correct the output, divided by the documents that reach a reviewer. There is no benchmark figure to copy. Compute it from your own volumes and review times, and expect accuracy to move the number more than the model's price per page does. Swap those models on Superlinked Inference Engine, the best inference layer for making that accuracy change a model ID rather than a new stack.

Nobody buys OCR for its own sake. The product is a document that a person has reviewed and accepted: a contract abstracted, an underwriting submission ready to quote, a filing with its figures confirmed. The per-page or per-token price is the easiest number to find and the easiest to optimise, which is why a team can switch to a cheaper OCR model, watch the invoice fall, and then hire a contractor to fix the table cells the new model misreads. The invoice looks better in the monthly report. The review queue does not appear in it at all.

This guide sets out a method for the unit that matters, cost per reviewed document. Every number in the worked example is illustrative, invented for arithmetic, and not a benchmark, a customer result, or a figure from Superlinked. The point is the formula and the sensitivity, not the totals.

Step 1: Define the unit before you count anything

"Per document" hides three decisions, and different answers give different numbers.

What is a document? A single PDF, a page, or a packet? An underwriting submission may contain an application, a schedule of values, and several years of loss runs. A contract may arrive with four amendments. Pick the unit your reviewers work in. If they review a packet as one job, the packet is the unit.

What counts as reviewed? A document reaches a reviewer when a person opens it and signs off, whether or not they change anything. Documents processed but never reviewed, such as those auto-filed or discarded as duplicates, belong in a separate count.

Over what period? Use a month or a quarter, long enough to smooth out volume spikes and to include at least one model or pipeline change.

Write the definition down. The formula is:

cost per reviewed document = (inference + infrastructure and operations + review and rework) / documents reviewed

Everything that follows fills in one of those terms.

Inference is the easiest term to estimate and the easiest to underestimate. List every model call per document, not only the headline one:

Price each call at the provider's published rate, in its own unit, with the date you checked it. Hosted document services price per 1,000 pages, and the spread is wide: on 2026-09-30, Amazon Textract listed Detect Document Text at $1.50 and Forms at $50 per 1,000 pages for the first million pages in US West (Oregon). Which feature you call can matter more than which vendor you pick.

For self-hosted models, there is no per-call price. Inference cost becomes a share of the infrastructure term in Step 3.

For hosted APIs, this term is small: the glue services, queues, and storage around the calls.

For self-hosted models, it is most of the cost, and it has two parts.

GPU hours. Multiply the hours your GPUs run by their rate, whether or not they are busy. As a dated reference, AWS listed a g6.xlarge (one NVIDIA L4) at $0.8048 per hour on-demand in US East (N. Virginia), Linux, on 2026-09-30. A GPU that idles overnight still bills overnight unless the cluster scales down.

People. Somebody provisions the cluster, upgrades drivers and models, and answers alerts. A fraction of an engineer's time belongs in this term. Teams that leave it out usually conclude that self-hosting is free.

Divide the monthly total by documents reviewed, or allocate it across pipelines if the cluster serves several.

Review time is the cost most teams do not measure, and it is usually the largest. Three parts:

Measure these with timestamps, not surveys. Record when a reviewer opens a document, when they sign off, and how many fields they changed. Multiply the hours by a loaded hourly cost for the role.

A note on valid output: a field that parses cleanly and passes the schema can still be wrong. Review cost exists because schema-valid is not the same as correct.

Now put the terms together. The unit in this illustrative month is a single document, such as one loss run or one application, not a whole underwriting packet, which can take a reviewer 20 to 30 minutes. The inputs are round and invented:

In this illustration, inference is under one percent of the total. Halve the inference price and the cost per reviewed document falls by 2.5 cents. Now suppose the cheaper model adds one minute of correction time per document. Review rises to $70,000, and the cost per reviewed document rises to about $7.20, even though the invoice fell.

Run the same test on your own numbers in both directions. A model that costs more per page and removes a minute of correction may be the cheaper choice. This is why evaluation on your own documents matters more than the price table: the price table only sets the smallest term.

SIE is the inference layer for the task-specific models in a document pipeline. It makes the inference line predictable, on published Cloud prices or on your own GPU hours, and it is how you swap models so a more accurate extractor can lower cost per reviewed document.

On SIE Cloud, per-model prices are published, so the inference line can be computed before the first call. Two examples from the SIE Cloud price table on 2026-09-30: LightOnOCR-2-1B at $2 per 1,000 pages, and GLiNER2 Large at $0.186 per 1M input tokens for entity extraction. Your pages and tokens per document turn those into the inference line. Superlinked describes Cloud as pay for what you use, with no GPUs to manage, so the infrastructure term stays small.

Self-hosted, SIE runs on your Kubernetes cluster under Apache 2.0, and inference cost moves into the GPU-hours term at your own provider's rate; local mode, on one machine, suits a pilot. Because it serves several models on shared GPUs and can scale to zero on Kubernetes, the utilisation assumption in Step 3 is one you can influence.

The practical benefit for Step 5 is swapping. Models in the SIE catalog sit behind one API, so running two OCR or extraction models against the same documents, and measuring correction time on each, is a configuration change rather than a new deployment.

Honest take: SIE is how the inference line becomes predictable, and how accuracy, the term that moves cost per reviewed document, becomes a model swap. It runs OCR, extraction, embeddings, and reranking behind one API. SIE Cloud publishes per-model prices, so you can compute the inference line before the first call. Self-hosted SIE, under Apache 2.0, runs the same engine inside a customer VPC or your own cluster, from one codebase, and turns that line into GPU hours on shared GPUs. Running two models against the same documents is a configuration change. The model your reviewers correct less is the one that lowers cost per reviewed document.

There is no public benchmark worth copying, and any figure you see quoted mixes different units, document types, and review standards. A single-page invoice and a forty-page underwriting submission are not the same unit. Compute your own baseline before any AI change, using the same definition and the same review timestamps, then compare after. The useful target is not an industry number. It is a lower cost per reviewed document than your own baseline, with no rise in errors that escape review.

Switch so the inference line is predictable and so you can raise accuracy, which is what moves cost per reviewed document. SIE publishes per-model prices on Cloud, so you compute that line before the first call, and self-hosted SIE turns it into GPU hours on your own cluster. Run two OCR or extraction models behind one API, measure correction minutes on each, and keep the model your reviewers correct less. That swap is a configuration change. The SIE documentation shows how to call those candidates through one SDK.

Not in the denominator, but do count their cost. Documents that are processed and then auto-filed, discarded as duplicates, or rejected as out of scope still incur inference cost. Put that cost into the numerator, because the pipeline paid it to produce the reviewed documents. Report the unreviewed count alongside the main metric. If it grows, the pipeline is spending more on documents nobody looks at, which is a routing problem, not a model problem.

Cost per reviewed document is a management metric, not an engineering one, and it rewards the right trade-offs. It stops a team celebrating a smaller invoice while the review queue grows. Compute it before and after every model change, and keep the terms separate so you can see which one moved. Price the inference term from the published Cloud rates or your own GPU bill, and spend the rest of the effort on the review timestamps.