Cloud costs don’t kill companies. Unknown cloud costs do.
When you can’t connect cost → usage → revenue, you’re flying blind:
- You don’t know which customers are profitable
- You can’t price confidently
- You can’t spot waste early
The unit economics recipe (simple version)
To start, you only need four ingredients:
- Costs (AWS Cost and Usage Reports)
- Usage (your app events in PostgreSQL)
- Revenue (Stripe subscriptions/invoices)
- A warehouse (BigQuery) to compute and slice it reliably
Three dashboards that pay for themselves
- Margin by customer/segment: revenue − cost-to-serve
- Cost drivers: what’s driving spend (compute, storage, data transfer) and where
- Noisy neighbors: customers or features consuming disproportionate resources
Why teams get stuck (and how to avoid it)
The hard part isn’t collecting data—it’s mapping it:
- Billing line items don’t naturally map to tenants
- Tagging improves over time (you need backfills)
- Cost models change as infrastructure evolves
DIY pipelines can do this, but it’s easy to end up with brittle jobs and “mystery math.” A managed data platform keeps ingestion reliable so you can iterate on attribution without rebuilding everything.
A quick win: “profitability alerts”
Once cost + usage + revenue are unified, you can add alerts like:
- “Customer margin dropped 30% week-over-week”
- “Data transfer cost spike tied to feature X”
- “Top 5 tenants driving 40% of compute”
That’s how you catch problems early—before they become a quarterly surprise.