Executive Summary

Many subscription businesses—from bootstrapped SaaS startups to 500-employee scaleups—still "guess" core metrics because billing data is scattered across payment processors, event stores, and spreadsheets; finance and product teams end up with divergent MRR, churn, and LTV numbers and spend hours reconciling them. With roughly 1.6 million subscription businesses and inconsistent definitions of revenue events, the problem is operational and analytical: teams need a single source of truth that runs where their canonical data already lives. You could build a Postgres-native toolkit (extension + SQL library + reference schemas and tests) that computes standardized MRR, ARR, churn, cohort LTV and related metrics directly against canonical tables, plus lightweight embeddable dashboards and vetted connectors to common billing systems. Packaging it as an on-prem/Postgres-first extension with optional managed hosting and audit trails would give teams real-time accuracy, versioned metric definitions, and the ability to run analytics inside existing data governance and security boundaries. This market is attractive now because the subscription economy is expanding and product teams increasingly prefer DB-first analytics and embedded, in-app metrics; the addressable analytics spend is roughly $9.6B (1.6M businesses × ~$6K/year) and your market and revenue scores are high (92/100 and 86/100 respectively). There is a clear timing advantage in aligning with the trends toward privacy, lower TCO for analytics, and real-time decisioning. To stand out you'll need rigorous, well-documented SQL semantics, robust handling of billing edge cases (proration, discounts, multi-currency, cancellations), and connectors that reconcile rather than duplicate external billing truths; those are strengths for a Postgres-native approach but also practical challenges. Expect medium competition and a longer B2B adoption curve driven by integration work and trust-building, so prioritize battle-tested compliance, sample production dashboards, and migration tooling to reduce onboarding friction.

Market Opportunity

Stop guessing SaaS metrics — compute MRR, churn & LTV in PostgreSQL targets a $9.6B = 1.6M subscription businesses x $6K avg spend on analytics/metrics/year total addressable market with medium saturation and a year-over-year growth rate of 18% CAGR in analytics & embedded BI for SaaS.

Key trends driving demand: Subscription-economy expansion -- more businesses need recurring revenue metrics and standardization across billing models; DB-first movement -- teams keeping canonical data in Postgres want analytics close to the source for real-time accuracy; Embedded analytics -- product teams expect in-app metrics, increasing demand for lightweight, on-prem/postgres-native solutions; AI-assisted SQL generation -- reduces onboarding friction by mapping product schemas to standard metric calculations.

Key competitors include Baremetrics, ProfitWell (by Paddle), ChartMogul, Metabase (and SQL-first BI tools: Redash/Mode) — adjacent workaround.

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Stop guessing SaaS metrics — compute MRR, churn & LTV in PostgreSQL

Executive Summary

Market Validation

Market Opportunity

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