Requirements

• Run from the pollinations repository root.
• Install jq and sops.
• Query with enter.pollinations.ai/observability/scripts/tb-prod.sh "<sql>" (below written as tb-prod.sh). It uses the production read token from SOPS; the staging workspace has no real revenue.
• Enable set -o pipefail before piping query output so a failed query fails the whole pipeline.

Revenue queries must filter successful Stripe checkout events so asynchronous payment methods are counted exactly once.

Weekly pack revenue

tb-prod.sh "SELECT toStartOfWeek(timestamp) AS week, round(sum(amount_cents) / 100, 2) AS revenue_usd, count() AS purchases FROM stripe_event WHERE payment_status = 'paid' AND event_type IN ('checkout.session.completed', 'checkout.session.async_payment_succeeded') AND timestamp >= now() - INTERVAL 90 DAY GROUP BY week ORDER BY week DESC FORMAT JSON" | jq '.data'

Recent pack purchases

tb-prod.sh "SELECT timestamp, user_id, session_id, amount_cents / 100 AS amount, currency, payment_method FROM stripe_event WHERE payment_status = 'paid' AND event_type IN ('checkout.session.completed', 'checkout.session.async_payment_succeeded') ORDER BY timestamp DESC LIMIT 100 FORMAT JSON" | jq '.data'

Revenue by customer

tb-prod.sh "SELECT user_id, round(sum(amount_cents) / 100, 2) AS revenue_usd, count() AS purchases FROM stripe_event WHERE payment_status = 'paid' AND event_type IN ('checkout.session.completed', 'checkout.session.async_payment_succeeded') AND timestamp >= now() - INTERVAL 30 DAY GROUP BY user_id ORDER BY revenue_usd DESC LIMIT 50 FORMAT JSON" | jq '.data'

Weekly spend by balance bucket

tb-prod.sh "SELECT toStartOfWeek(start_time) AS week, splitByChar(':', selected_meter_slug)[-1] AS meter_source, sum(total_price) AS total_spend, count() AS requests FROM generation_event_v2 WHERE start_time >= now() - INTERVAL 60 DAY AND environment = 'production' GROUP BY week, meter_source ORDER BY week DESC FORMAT JSON" | jq '.data'

Where the money came from vs what was spent

generation_event_v2 spend (pack_spend, selected_meter_slug) says which bucket was consumed, not how it was funded. pack_balance is credited by Stripe purchases and auto-top-ups, but also by BYOP markup and community-model rewards (paid into the payer's bucket, shared/billing/track-helpers.ts) and some quest rewards. So pack spend ≠ cash. And Stripe checkout rows alone miss auto-top-ups, which never create a checkout.session.* event. To separate cash from earned funding, reconcile each source on its own and don't add figures with different monetary bases:

• Stripe checkout pollen_credited (stripe_event, payment_status = 'paid')
• Auto-top-up amount_usd (principal credited — see below)
• Historical Polar credits (Nov 2025–Jan 2026)
• Claimed community/BYOP rewards, by bucket

No cash purchase ≠ non-payment or abuse; pack spend ≠ proof of cash.

Auto-top-up: principal vs gross payment

Auto-top-up amount_usd in D1 is the Pollen principal credited; Stripe fees and taxes sit on top. Gross cash is in Tinybird stripe_event as payment_intent.succeeded rows with an empty user_id (auto top-ups are not attributed there); per user, fetch the invoice by stripe_invoice_id and use amount_paid. Keep principal, gross payment, tax, refunds and net revenue as separate numbers.

Historical pricing changes

Define a past price change from the code deployed at the time, not the PR title or announcement — an "X% cheaper" announcement can coexist with a same-day multiplier change that cancels it. Confirm billed unit prices in generation_event_v2:

tb-prod.sh "SELECT toDate(start_time) AS day, round(avg(total_price), 6) AS avg_unit_price, count() AS requests FROM generation_event_v2 WHERE model_used = '<model>' AND start_time BETWEEN '<before>' AND '<after>' GROUP BY day ORDER BY day FORMAT JSON" | jq '.data'

Provider cost, priceMultiplier markup and pack-credit promotions move independently; check all three. Keep cash paid separate from Pollen credited — a Pollen discount doesn't change cash buying power. Note who was exposed before the payment event you're explaining and any simultaneous changes.

Conversion cohort analysis

For "these users pay more" or "X caused conversion" claims, define the comparison first:

• Pick the cohort without looking at the outcome. Don't select on payment or on rewards that require a payment. Contribution rewards (PR/quest merges) are fine; payment-triggered rewards are not.
• Freeze a cutoff date for both membership and the payment check.
• Match observation windows. A last-7-days cohort has had 0–7 days to convert; compare it only with another cohort measured the same way, or use a fixed per-user window ("first 7 days after signup").
• Check ordering. For a reward-linked cohort, count who paid before the reward vs after. If most paid first, the reward didn't drive conversion.
• Count each user once.
• Report group sizes and caveats with the percentage.

Notes

• stripe_event is the source of truth for pack-purchase revenue analytics.
• generation_event_v2 records Pollen consumption, not cash revenue.
• Both datasets use user_id, so revenue and usage can be joined directly.
• The dashboard's daily_stripe_revenue pipe applies the same paid-event filter.
• For pre-migration revenue history, note that Polar was the pre-Stripe merchant of record (Nov 2025–Jan 2026) and is retired. Do not combine historical Polar and Stripe totals without checking the cutoff for overlap.
• Request-count shares (e.g. BYOP share) are dominated by free traffic and drift fast. Always state the date window, and prefer revenue share (% of total_price) for strategic claims.