A/B testing tooling is a traffic-volume question first and a stats-engine question second. Buyers arrive at a comparison page knowing roughly how many monthly visitors they need to test against and whether they need frequentist or Bayesian inference, sequential testing, multi-armed bandits, or feature flags layered with experiments. Vendors gate traffic and stats features behind tiers, and the engines vary materially: VWO and Optimizely lean Bayesian, Convert leans frequentist, GrowthBook supports both.

SleekRank reads one matrix with tool slug, starting price, monthly traffic cap, stats engine, targeting features as a delimited list, and a focus tag. Each row drives the per-tool page and every pair the tool appears in. Tag mappings push traffic caps into the hero, list mappings render targeting features into a checklist, and selector mappings inject the engine note into the comparison block.

When Optimizely changes its enterprise tier or Statsig adjusts its starter caps, you edit the row and flush the cache. The corpus catches up. Adding PostHog Experiments to a corpus that already covers VWO, Optimizely, Convert, and Statsig is one row plus the four pair pages it multiplies into.

A/B testing tooling is a traffic-cap question, and the caps move. VWO restructured its MTU pricing more than once, Optimizely shifted to custom-only on its top tiers, Convert tweaked its monthly visitor bands, Statsig and GrowthBook introduced and adjusted free tiers as they competed for product-led-growth teams. The buyer who arrives at an A/B testing comparison page is usually thinking about a specific traffic level (twenty thousand monthly visitors for a marketing site, half a million for a SaaS app, ten million events for a product-analytics-led team) and the answer they need is which tool fits at that volume without overage charges.

A page that shows last year's MTU price is worse than no page at all because the buyer clicks through, finds the discrepancy at the vendor, and bounces. A/B testing affiliate revenue depends on that click converting at the vendor, so trust on the page is paid trust. The freshness problem also affects stats engine claims.

Vendors ship engine upgrades on irregular cadences (Bayesian sequential testing, regression-adjusted estimators, CUPED variance reduction), and pages that miss those updates look stale to buyers who already saw the change announced. SleekRank does not solve research; it solves the propagation, so the cell you edit on Tuesday is reflected on every per-tool and pair page by Wednesday's cache cycle.