How to Track Stripe MRR in Supabase — Without Writing SQL
If you built on Supabase, your Stripe data probably lands in a subscriptions or customers table via webhook edge functions — the pattern every Supabase-plus-Stripe starter kit ships with. The rows are all there next to auth.users, but the Supabase dashboard gives you a table view, not an MRR chart, and writing the SQL yourself is exactly the job you don't have time for. These five plain-English questions turn those synced tables into a revenue dashboard.
“What is our current MRR and how has it changed month over month for the last 12 months?”
The growth curve everyone asks about first. Your webhook-synced subscriptions table has all the raw material — statuses, price IDs, amounts — but turning it into a monthly series means normalizing intervals and filtering out incomplete and past_due states. One question replaces that whole modeling exercise.
You get: A 12-month MRR trend chart with month-over-month growth rates and the current total.
“How much new MRR did we add this month, and from which customers?”
New MRR with names attached tells you whether marketing is working and who your product is resonating with right now. Joining subscription rows back to auth.users for emails and signup dates makes the number concrete — these people, this week, this money.
You get: A total of new MRR this month plus a list of the new paying customers behind it.
“What is our MRR broken down by plan, and which plan is growing fastest?”
Supabase starter-kit schemas usually store a price or plan ID per subscription, which makes this segmentation sit one join away. Knowing where revenue concentrates — and where momentum is — is how you decide which tier to invest in and which to sunset.
You get: A table of MRR per plan with share of total and recent growth rate for each.
“How long does it take new signups to convert to paid, and what is that worth in MRR?”
This is where Supabase quietly shines for analytics: signup timestamps live in auth.users and payment starts live in your subscriptions table, so time-to-paid is a single join. A lengthening gap is an early warning that your trial or onboarding is losing its punch.
You get: An average and median days-to-paid figure plus the monthly MRR attributable to recent conversions.
“Which paying customers have failed or past-due payments right now?”
Involuntary churn — expired cards, failed charges — silently eats several percent of MRR at most SaaS companies. Your synced Stripe statuses already flag these accounts; surfacing them weekly turns a silent leak into a recoverable list.
You get: A list of at-risk accounts with status, amount due, and how long payment has been failing.
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Frequently asked questions
Do I need SQL skills to use these questions?
No — you paste the question into a chat box as written. The AI reads your Supabase schema (including auth.users if your role can see it), writes the SQL, runs it read-only, and shows you the result with the query available for inspection.
Is connecting my production Supabase project safe?
Yes. Access is read-only by default, so nothing writes to your project. Create a read-only Postgres role in the Supabase SQL editor for analytics, and note that if that role is subject to RLS you should exempt it, or revenue counts may be silently filtered.
My Stripe webhook sync occasionally misses events. Will MRR still be right?
MRR is computed from whatever is in your tables, so a gappy sync means gappy numbers — but you can ask directly, e.g. "are there subscriptions with no recent webhook update?" to find and quantify the drift before trusting the headline figure.
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