2026-07-11 · 6 min read
Unit economics for AI SaaS without a warehouse
Finance teams ask a simple question: which customers make money after LLM costs? Engineering has Grafana traces; billing has invoices — but neither produces per-customer margin with recommendations. Warehouses add months of pipeline work.
FluxMeter Intelligence computes unit economics from the same Redis rollups that power budget enforcement — period/month/customer/model slices already exist from ingest.
Revenue overlay — the missing half
Cost alone is insufficient. POST /intelligence/revenue/{customer_id} or OpenMeter overlay ingest supplies revenue per customer. GET /intelligence/unit-economics?period=2026-07 returns margin, loss status, and rule-based recommendations (upgrade, cap, repricing).
Customers without revenue return status: unknown_revenue — honest partial answers beat a blank dashboard.
From unit economics to board reports
Pair unit economics with GET /intelligence/profitability for cross-customer trends, GET /intelligence/root-cause for spend delta drivers, and GET /intelligence/report?format=markdown for a Finance-ready export.
POST /intelligence/simulate models model-switch or token-grant promos before you commit — closing the loop from diagnosis to decision.
Try it in five minutes
Run make demo, ingest a few events with distinct customerIds, POST revenue for each customer, then curl /intelligence/unit-economics. Full walkthrough in docs/intelligence-api.md on GitHub.