How to Read an E-Discovery Quote Line by Line

The per-GB rate is one line; the bill is set by the others.

Before any rate is weighed, ask the vendor what matter it imagined when it wrote the quote, for a vendor's assumptions are, to be sure, the most generous line on the page. The law offers the careless buyer little rescue. Proportionality entered the federal discovery rules in 1983, as Robert Keeling and Ray Mangum of Sidley Austin recount in Judicature, yet it governs what a party must produce, not what a vendor may charge to produce it.

Initial disclosures fall due within 14 days of the Rule 26(f) conference. That is usually before anyone has read the quote twice. Rule 26(b)(2)(B) does let a party resist ESI that is not reasonably accessible because of undue burden or cost, and courts have now and then moved the bill: a 2025 review of the case law records more than $700,000 in scanning costs shifted in Allergan and $75,000 in Tasigna. Those were exceptions.

I confess a reluctant admiration for a presumption that each side pays its own way, since it keeps discovery quarrels from becoming fee quarrels, and it leaves the responding firm, most gallantly, holding every invoice. So the burden returns to the four lines on the page, and first to processing, where the gigabyte is weighed before anyone has agreed how to weigh it.

What Line Items Make Up an E-Discovery Quote?

An e-discovery quote is a bundle of separate meters: processing, monthly hosting, user seats, project management hours, export, and now AI review, each counting a different unit.

Before reading any rate, sort the quote into its lines:

1. Find what each line counts: gigabytes, documents, users, hours, or prompts.
2. Note whether the line is charged once, every month, or on every use.
3. Mark any line the vendor bills apart from the headline rate.

In 2022, a practitioner on the r/ediscovery forum described a cloud vendor charging $45 per GB per month for processing, hosting, users and analytics, with project management fees billed separately, and admitted not knowing whether that gigabyte was measured compressed or expanded. A law firm in the same thread charged $1 per document for the whole service. Two quotes, two units. Neither, it must be confessed, can be laid beside the other without translation.

An itemized quote is the more candid arrangement, though hardly the easier to read. One 2020 practitioner account put a cloud platform's hosting at $30-45/GB per month, ingest and processing at $20-$40/GB, and project managers at around $250/hr. Each of those figures is honest; none of them, taken alone, is the price of the matter.

The newest line is the least settled. Practitioners in a 2025 r/ediscovery thread described vendors billing per prompt, per document, or, as one commenter put it, "price per prompt per document", while others sell tokens up front. Recent pricing research finds hybrid and per-document models each at roughly 28% of GenAI review pricing. At the opposite pole sits flat monthly self-service pricing, with no separate charge for import, OCR or purging.

An analysis of 5 sources shows one pattern: every vendor meters something, and none meters the matter. In practice, the unit decides the bill. A rate without its unit is not yet a price.

The common assumption is that the lowest per-GB figure wins; to be sure, it wins the first month. I would put the point more plainly for a small firm, because Relevant e-Discovery was built for exactly that buyer: enterprise-grade defensibility at small-matter economics, operable and affordable for a solo or small firm on a single messy matter. The last meter matters as much as the first, a subject I take up in why leaving an e-discovery vendor costs so much, and it leaves the awkward question of why a low headline rate so seldom produces a cheap matter.

Why Doesn't a Low Per-GB Rate Mean a Cheap Matter?

Review, not hosting, is where matter cost concentrates: manual review runs dollars per document and roughly $19K/GB, while AI-assisted review runs cents per document.

Contrary to the shape of most quotes, the per-GB line is seldom the heaviest one. Hosting and processing are priced in gigabytes because gigabytes are easy to count; review is priced in documents and hours because that is where the labour lives, and labour, as more than one managing partner has remarked over a budget, does not compress. A firm that negotiates gallantly over a few dollars of hosting and then accepts review by the document has, it must be confessed, won the skirmish and mislaid the war.

Buyers sense this even when quotes do not show it. In a January 2026 r/ediscovery thread, one buyer called Relativity "a major contender in the space" but "pretty expensive," while another commenter's organization runs "Rel server with Epiq" and found it affordable. A third had seen "a lot of teams move off Reveal or look for something more cost-predictable than Relativity." The platform name fixes neither the price nor its predictability.

Predictability is worth paying for. A quote you cannot forecast is not a budget.

The rules of the game make the review line weightier still. Since the 2015 amendments, discovery must be relevant and "proportional to the needs of the case," yet the responding party is presumed to bear its own costs, and cost-shifting stays the exception rather than the rule. The review bill, in other words, is almost always yours. Budget for it as such.

The second thing the per-GB line hides is risk. A cheap review that cannot be defended is not cheap; it is merely deferred, and the invoice arrives later in the form of a motion. Defensibility rests on matters a quote rarely mentions: whether originals stay unaltered, whether every action is logged, and whether privileged material can slip into a production. It rests, too, on whether the model doing the coding stays the same from start to finish, a question worth raising whenever an AI review vendor can swap models mid-matter.

At Relevant e-Discovery, our process keeps immutable originals with content hashing, append-only audit trails, documented chain of custody, and a fail-closed privilege gate on production, so the review holds up if opposing counsel challenges it. Our design also joins the defensible spine of enterprise platforms (Bates numbering, privilege, chain of custody) to the cited Q&A and chronologies of the newer AI entrants, so a buyer is not paying two vendors for two halves of one job. The difference in review cost is one a buyer can test on their own matter rather than take on faith.

Which brings us, by a road less romantic than one might wish, to the practical business of laying two quotes side by side before anything is signed.

How Should You Compare E-Discovery Quotes Side by Side?

Price every vendor against one benchmark matter, total the cost over its whole life, and test the AI line on your own messy collection before signing.

The method has four steps, and none of them requires a procurement department:

1. Fix one benchmark matter. A commenter in a 2020 r/ediscovery thread on vendor pricing suggested comparing 10GB of data, for 6 months, with 5 users; I would substitute the size, duration and team of the matter actually on your desk.
2. Ask for a cumulative price. Each vendor totals processing, every month of hosting, seats, project management hours, productions and AI for that one scenario, and states its billing unit and any annual minimum.
3. Test the AI line on a sample. Run it on about 50 of your own documents before committing the collection.
4. Read the terms that carry no price. Ask where processing runs, who holds the encryption keys, what the vendor retains, and whether your data trains anyone's model.

The same 2020 thread explains why only the cumulative figure is worth comparing. Annual deals could cost "significantly less per GB" yet carried minimum payments; ingestion-based plans charged for processing on upload, while monthly hosting plans absorbed processing up to a volume cap; and some vendors charged extra for TAR (technology-assisted review, in which software ranks documents by likely relevance). Ask each vendor, in writing, whether its rate assumes an annual term, and what the monthly rate becomes without one. The research section above walks through these findings. Nothing else does.

The sample test deserves more ceremony than buyers usually give it. Practitioners in a 2025 discussion of per-prompt pricing were plain that you "don't just feed 1.5TB into it"; you refine prompts on small sets of about 50 documents first, because per-prompt AI is, as one put it, "pay per play." The sample tells you how many prompts the work needs and what each one costs. That is a forecast, not a hope.

Benchmarks are also ruined by surprise re-collections, the forgotten custodian or the cloud account nobody exported, so it pays to know what one-click cloud collection tools miss before the first gigabyte is counted.

At Relevant e-Discovery, our answer to the fourth step is that processing runs single-tenant, or inside your own AWS account under your own keys, with no vendor retention and no model training, so nothing you feed it trains anyone's model or waives privilege. Our demonstration follows the third step as well: bring a messy collection and a hard question, and watch the tool read, code and cite your own evidence rather than a case study about someone else's.

With four cumulative totals in hand, the remaining labour is to read each one line by line, beginning with the line that most often misleads, which is the gigabyte itself.

What Should You Price Before You Sign an E-Discovery Quote?

Before signing, get the vendor's billing unit, any annual minimum, the AI review unit, and a written total for your own matter's expected size, duration and team.

The market, it must be confessed, is scarcely better informed than the buyer. In the Winter 2026 eDiscovery Pricing Survey run by ComplexDiscovery and EDRM, 18.9% of practitioners answered "do not know" when asked the per-GB price of technology-assisted review, which the survey's authors read as a capability folded quietly into platform subscriptions. The research section above walks through these findings. The same survey found GenAI review settling into a competitive zone of $0.11-$0.50 per document.

Ask for the total. I expect that metered AI charge to become the fifth line every quote must answer for, and the buyer who insists on one figure across the life of the matter will be the only party at the table who knows, before the invoices begin their gracious procession, which of the lines was doing the charging and which was merely wearing the headline.