Every benchmark runs against reference cohorts of modelled deal cohorts across 1,140 vendors, adjusted for deal size, region, industry, and signing period. Not survey data, not list-price math, not a vendor rep telling you what is typical.
The vendor knows exactly what every comparable company paid. You know what you paid last time. That asymmetry is the whole game, and it is why a rep can call a 12% discount generous when the market clears at 40%. Benchmarking closes the gap on your side of the table.
Three ways in, all reading the same living data layer.
Enter net price and deal size and see your standing against the cohort immediately. Bespoke tools for Microsoft EA, Oracle ULA, Salesforce, SAP RISE, and other flagships handle their real pricing units.
On Enterprise, a VendorBenchmark analyst reviews your pricing and terms against the cohort and delivers a written report: your standing, where to improve, the risks in the paper, and a clear target number.
Every saved scenario replayed against today’s market, so you see which contracts fund your savings target and where you sit above the median, priced per year.
Every benchmark opens a case file that ends with twenty plus moves on its own numbers: six Vera-drafted emails, negotiation briefs as executive PDFs, a 30 day share link, and alerts on your saved position.
The rank is math on real data, never a guess. The AI sits on top: it reads your contract to pull the fields that make the comparison fair, writes the analyst-grade narrative around the number, and drafts the next move. Every figure it prints is grounded in stored data and citation-tagged.
The number is defensible because of how the data underneath is built and governed.
Your rank is computed against modelled comparable deals, normalized for deal size, region, industry, and signing period, so a small mid-market deal is not benchmarked against a global enterprise agreement.
Contribute one anonymized outcome and unlock the realized uplift peers actually paid. Salted keys and a k=5 floor keep every contribution anonymous, and the metric is realized uplift, not a headline discount claim.
Contributed datapoints are graded by analysts before they influence the cohort, so one bad number cannot skew the market you are measured against.
A quarterly, versioned index with a public and keyed API, so you can reference a fixed, dated figure in a board paper without it shifting under you.
Your contracts and saved scenarios live behind Postgres row-level security, so your numbers feed your view and never leak into anyone else’s cohort or account.
Sourcing comparables, normalizing them, and writing the standing up used to be a multi-day analyst engagement per vendor. Here it is a single self-serve run, and the write-up and the next email come with it.
Estimate your own hours →The category is full of stale reports and anecdotes. The difference is the data and how live it stays.
A benchmark report priced two years ago, sold to everyone, and never adjusted for your size or timing is an anecdote in a PDF. Ours is computed on modelled deal cohorts and watched after you save it.
The classic engagement scopes one vendor, bills for the sourcing and the write-up, and ends when the invoice does. Here it is self-serve across 1,140 vendors, and the method is yours to re-run.
Discount-off-list calculators stop at a number the vendor controls. Your rank places that discount against the range comparable customers pay.
One line for the board paper, and the day to day the team actually feels.
1,140 vendors. Modelled deal cohorts. Analyst signed.