RevenueCat vs Lightdash
Side-by-side trajectory, velocity, and editorial themes.
Stretching from subscription infrastructure into hybrid subs+ads revenue tracking, with paywalls getting smarter.
RevenueCat is broadening from subscription-only to subscription-plus-ads with in-app ad revenue tracking now in public beta — apps using AdMob or AppLovin can send ad events through the SDK and see ad and sub revenue side by side. Paywalls have gained meaningful logic depth (Paywall Rules to show/hide components by intro-offer eligibility or custom variables) and the iOS/Android fallback paywall now auto-styles using the app icon's dominant color. Operational tooling has caught up: archived offerings/products/entitlements, OAuth token visibility and revocation, predicted-LTV winners in Experiments.
The product is moving from 'subscription billing infra' to 'mobile monetization platform.' Ad revenue tracking is the headline because it changes who RevenueCat is for — every freemium app with mixed monetization, not just sub-driven apps. Paywall Rules suggest the company is going deeper on the merchandising layer rather than ceding it to MMP-adjacent tools. The Experiments-side LTV predictions and locale-aware paywalls signal continued investment in the optimization story.
Expect the in-app ad revenue beta to GA with deeper SDK support for more ad networks, more sophisticated Paywall Rules conditions (likely user-segment and behavioral triggers), and tighter Experiments + ad-revenue correlation as customers compare hybrid monetization mixes.
Lightdash chips away at the SQL barrier with NL-to-formula table calcs and metric-tree visualization.
The release cadence is high and the work spans three areas: lowering the technical barrier (spreadsheet-style formulas in table calculations, plain references to grand totals), enriching what a chart and dashboard can express (color palettes at every scope, row/column limits, rich-text table cells), and self-serve operability (default user spaces, expiring preview projects, dashboard-version rollbacks that include chart configs). The Canvas now hosts persistent metric trees, hinting at a heavier semantic-layer story.
Lightdash is positioning between a dbt-native semantic layer (where SQL-fluent analysts live) and a self-serve BI tool (where business users live). The intent-driven formula editor and reference-total functions chip away at the SQL prerequisite for table calculations, while Saved Trees push the metric model into something visually editable. Underneath, the platform is doing the unglamorous self-serve work — personal spaces, palette hierarchies, preview hygiene — that BI products need to survive in larger orgs.
Expect the formula editor to grow into broader AI-assisted authoring (filters, joins, custom dimensions) and Saved Trees to evolve into a more general semantic-layer view that consumes from dbt and produces governance artifacts. Color and palette work suggests embedded/customer-facing BI ambitions next.
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