What AI Coding 100,000 Documents Costs in Your Own AWS Account

Only the cloud's page meter comes with a public price list. The other lines on an AI review bill do not.

Coding 100,000 documents with AI inside your own AWS account produces three bills, not one: a metered cloud charge, a vendor license and human validation hours.

AI coding means a model reads each document and tags it for relevance, issue and privilege. Own-account means that work runs under your keys, so Amazon bills you directly: Amazon Bedrock for the model's tokens, plus storage, the search index and compute.

I would first ask whoever administers your AWS account whether this matter's charges can be separated from the firm's other cloud spending, because a bill you cannot read by matter cannot be checked or passed on. Own-account processing also buys something no price list shows: Relevant e-Discovery states that it runs with no vendor retention and no model training, so nothing a firm feeds it trains anyone's model or waives privilege.

Consider how people rate cloud software they plainly adore. On Gartner Peer Insights, Box holds a 4.4 overall rating from 573 ratings, with 45% at five stars, 44% at four stars and 0% at one star. Nobody, apparently, hates the product. One complaint that does surface concerns the licensing.

Review pricing has the same habit. The first question for any quote is therefore its unit: per document, per gigabyte, per token, a flat monthly subscription or a hybrid of them. Technology-assisted review, the previous generation, earned its savings through tiered staffing, so ask any vendor quoting a saving which baseline it was measured against. Relevant e-Discovery states its own fee position plainly: cents per document for AI-assisted review, dollars per document for manual review, a gap the buyer can see on their own matter. So begin with the one line Amazon itemizes for you.

What does your own cloud account charge for the AI first pass?

Your own account bills extraction per page and inference per token, under your own keys, and the extraction route you choose moves that bill more than the model does.

Before pricing a single token, settle four things:

1. Count pages, not documents, because extraction services bill by the page.
2. Pick the extraction route first: plain OCR, layout conversion, or page images sent straight to a vision model.
3. Store every extraction output, so a rerun never pays for the same page twice.
4. Price inference from the provider's current rate page on the day you budget, because no published Amazon Bedrock rate is quoted in this analysis.

The per-page rule is not a technicality. In 2025, a developer on Google Cloud's community forum woke up to a budget warning after a PDF tool was invoked 26 times in a single day. Commenters explained, with the patience of people who had also once woken up to a budget warning, that every Document AI service bills per page, not per request. The same 2025 thread priced Google's Enterprise Document OCR at $1.50 per 1,000 pages for the first 5 million pages, with async processing at 60 cents per 1,000 pages. One commenter summarized the learning curve with admirable economy: 80 bucks spent before figuring it out, and $1.50 for the rest of the job afterward.

Volume can also be bought ahead: in a December 2025 r/AZURE thread, one team said it handled roughly 2.6M pages of burst ingestion in about 12 hours and bought a pre-paid quantity for a first-month discount to absorb it. Teams running Azure pipelines report the Read OCR model at $1.5 per 1,000 pages, the Layout model at $10 per 1,000, and page images sent directly to GPT-4o vision at roughly $0.05 to $0.07 per page. One engineer's cost modeling found that letting the language model do everything was an order of magnitude more expensive than converting to Markdown first and calling the model second. Another reported processing an organization's 1 million files for about $1,000 instead of $20,000.

In practice, your page count sets the floor. Reruns are what quietly raise it.

The common assumption is that the model is the expensive part. On extraction, the reverse is closer to the truth: the cheap meter is OCR, and the costly habit is mailing photographs of documents to a model that then has to squint at them, page by page, at a premium.

There is an honest trade-off buried here. Async jobs and self-hosted open-source OCR are cheaper and also slower, which matters very little for a one-time matter ingestion and enormously for anything a partner wants tomorrow.

Where the meter runs is the other half of the question. Our in-account AWS deployment runs under the client's own keys, so extraction and inference charges land on the firm's own invoice instead of disappearing inside someone else's price. For a fuller account of what that changes, see who controls what in a managed service versus your own AWS account.

Managed platforms fold that meter into a license, and buyers notice. A reviewer on Gartner Peer Insights who rated Box a full 5.0 still called its account licensing "confusing and quite expensive for the product provided." The grievance was the licensing, not the file sharing it paid for.

Set side by side, 4 sources show one pattern: per-unit meters are cheap, and bills grow through volume, repetition and licensing terms. Which brings the ledger to the line most vendors would rather quote as one tidy number per document.

Why does the vendor license hide inside a per-document price?

AI-assisted review runs cents per document against the dollars per document of manual review, yet most vendor quotes never show which cents are cloud meter and which are license.

The ComplexDiscovery Winter 2026 eDiscovery Pricing Survey, built on 53 responses, found that hybrid and per-document models tie at 28.3% each as the primary way GenAI review is sold. Per-GB pricing trails at 11.3%. Per-token pricing and flat monthly subscriptions each sit at 5.7%, with outcome-based deals at 3.8%. The survey's analyst read the split plainly: providers are "absorbing token cost variability and presenting buyers with higher-order pricing units."

Start with the model: which one does the quoted price assume, and what does the price become if the vendor changes it? Ask, too, whether the price assumes one platform or two: Relevant e-Discovery describes enterprise tools as supplying Bates numbering, privilege and chain of custody, newer AI entrants as supplying cited answers and chronologies, and its own product as the combination.

Then ask for the same quote with the model's usage billed to your own AWS account, and see how much of the per-document price remains. Somewhere inside it sit token consumption, GPU infrastructure and model licensing, the cost drivers the survey analysis names, baked together at an even temperature and served by the slice. The labels on the newer pricing units are no clearer: 64.2% of respondents said per-GB GenAI pricing either does not apply to them or is unknown.

The same analysis names $0.11 to $0.50 per document as the emerging competitive zone for GenAI review, and it tells buyers to find out what that fee actually covers. Before signing any per-document quote, ask three questions:

1. Does the fee cover model inference alone, or quality control as well?
2. How are exception documents, the ones that fall out of the AI workflow, billed?
3. Is reporting included, or priced separately?

Contract terms move the number as much as the rate does, which is why it pays to read how an AI review vendor can swap models mid-matter before comparing quotes side by side.

Our own position on fees is that the economics should be demonstrable on the buyer's own matter, not asserted in a brochure, against a manual review benchmark of roughly $19K/GB. At Relevant e-Discovery we built the platform to be operable and affordable for a solo or small firm on a single messy matter, the segment the large enterprise platforms price out.

Who sees that difference is another question entirely. Axiom's 2024 study of more than 600 senior legal leaders, summarized by Amy Swaner in early 2026, found that 79% of law firms use AI to boost efficiency, while only 6% pass the savings to clients and 34% charge premium rates for AI-enhanced work. Swaner's own illustration is a document review that once cost $200,000 and now runs $30,000, with clients rarely seeing the whole saving.

A blended price protects whoever sets it. Itemized lines protect whoever pays it. And none of the three questions above prices the people who will check the machine's work.

How much human validation does AI coding still need?

Validation is the line billed at human rates, and it does not shrink with token prices; hashed originals, append-only audit trails and a fail-closed privilege gate are what make it defensible.

The costs that never make it into a per-document quote are well catalogued. In a 2024 r/ediscovery thread titled "Is AI too expensive?", practitioners listed the "additional cost of set up," "QC stages with senior reviewers," and recurring charges for rerunning models. One commenter said quality control on AI output meant samples and elusion tests at roughly "triple" the level used for existing technology, especially for privileged information. An elusion test, for the blessedly uninitiated, is a statistical sample drawn from the pile the system set aside, to find out how much that mattered was left lying in it.

Another commenter summarized the mood with the bluntness of someone typing at the end of a very long day: "no one trusts AI." A third put the economics in one line. AI review "still requires QC...so someone billing hourly."

That is the line I would budget first. Ask the vendor how large a sample its own protocol draws and who is expected to read it, then put that person's hourly rate beside the number.

The same 2024 thread shows how fragile the savings arithmetic is. In 2024, its original poster priced AI first-pass review at $0.20 per document and called it 10 times cheaper than manual review, calculated at $60/hour and 30 documents per hour. A practitioner replied in that 2024 thread that 30 documents per hour was "pretty low" for first-pass review. Raise that baseline, and the gap narrows before a single validation hour is counted.

Technology-assisted review went through the same reckoning a decade earlier. Henry M. Sneath of Houston Harbaugh writes that TAR cuts cost and time against manual linear review by some estimates 40 to 60%, and that the savings come from a tiered model: lower-rate first-level reviewers handle large batches, while higher-rate second-level reviewers spot-check smaller ones. Validation uses statistical sampling to measure recall, the percentage of relevant documents found, and precision, the percentage of retrieved documents that are actually relevant.

For a 100,000-document matter, the validation line has three parts to price:

1. Sampling and elusion testing on the documents the AI set aside.
2. Second-level attorney review of privilege calls, which even vendors selling AI review say should stay under attorney oversight.
3. Written documentation of the process, because a challenged review will likely need affidavit support from counsel or the vendor.

None of the sources in hand measures how many validation hours a 100,000-document matter takes. On fees, Relevant e-Discovery's own answer is that the economics are demonstrable, not asserted: AI-assisted review runs cents per document against the dollars per document of manual review, a difference the buyer can see on their own matter. Ask Relevant e-Discovery to show it on your collection, then price the validation hours in your own protocol at your own reviewers' rates, because they depend on the sample design and the share of privileged material. For a closer look at what those reviewers should be checking, see where AI relevance scores come from and what they hide.

Documentation is where this spend can be contained. Our platform keeps immutable originals with content hashing, append-only audit trails, documented chain of custody and a fail-closed privilege gate on production, so the record a challenge would demand already exists when opposing counsel asks for it.

The machine's work is billed in cents. How much of it gets checked can be argued over before a single hour is billed: in the 2025 In re Insulin Pricing Litigation ruling Sneath describes, plaintiffs wanted pre-set criteria for when review could or must pause for validation, while defendants wanted to pick the stopping point themselves.

Which line on the bill decides what own-account review costs?

Not the tokens. On fees, Relevant e-Discovery's answer is that AI-assisted review runs cents per document against the dollars per document of manual review, a difference the buyer can see on their own matter.

Ask Relevant e-Discovery to show that difference on your own collection. Which of the other lines grows fastest is then a property of the matter: the longer a case stays open, the more monthly hosting it accrues, and the more privileged material it holds, the more attorney checking it needs.

The old numbers explain why. In 2012, RAND examined 57 large-volume cases at eight very large companies and found that review took a median 73 percent of the cost of producing electronic documents, with processing at 19 percent. People were the bill. Machines were the side dish.

I expect AI coding to keep moving that weight, not erase it. The machine reads the first pass for cents, apparently without complaint, and the hours migrate to sampling, spot checks and privilege review. Meanwhile, what the client pays is a separate decision: Amy Swaner describes firms holding their rates, adding an AI premium, or landing somewhere in between.

So ask for three lines on any quote before you sign:

1. The cloud meter, billed by Amazon to your account.
2. The vendor license, stated on its own.
3. The validation hours, with who performs them and at what rate.

Relevant e-Discovery's answer on fees is that the economics should be demonstrated, not asserted: cents per document for AI-assisted review against dollars for manual review, shown on your own matter. Bring the collection. Then decide which of the three lines your own client will see.