CostMon

What problem does it solve?

Spend is scattered across a dozen cloud, AI, and SaaS billing consoles, each with its own format — so nobody has one trustworthy total.

Who is it for?

Engineering and finance teams tired of reconciling a dozen separate billing consoles.

Why are we building it?

We're a studio that runs nine products plus every client's stack, which means our own AI, cloud, and SaaS spend already lands across a dozen billing consoles, each with a different export format and a different definition of "usage." Reconciling that by hand every month is exactly the kind of thing we'd tell a client to stop doing.

CostMon is the fix we're building for ourselves first: one normalised view of spend across providers, so engineering and finance are looking at the same number instead of two spreadsheets that never quite agree.

It's still in build, not because the idea is unproven — we already do this reconciliation by hand — but because normalising a dozen billing formats into one honest total is the part worth taking the time to get right before anyone else depends on it.

How does it work?

1. Provider connectors, not manual exports

   Each cloud, AI, or SaaS provider is a connector that will pull billing data on its own schedule, so the aggregation doesn't depend on someone remembering to download a CSV.

2. A normalised spend schema

   Every provider's invoice gets mapped into one common shape — line item, service, cost, period — so a dollar of AI inference spend and a dollar of cloud compute spend become comparable.

3. One trusted total, not nine partial ones

   The normalised records roll up into a single view engineering and finance can both look at, instead of each side maintaining its own reconciliation.

What's the hard part?

Every provider bills differently — different line-item granularity, different currencies and proration rules, different definitions of what counts as a "unit." The hard part isn't pulling the data, it's designing a normalisation schema honest enough to make a cloud invoice and an AI usage invoice comparable without quietly losing the detail that actually explains a cost spike.

What does it do?

• Aggregates billing from every cloud, AI provider, and SaaS invoice
• Normalises spend into a single comparable view
• One trusted total across all providers
• Shared source of truth for engineering and finance

What does this prove we can do for you?

This is the same billing-normalisation and cost-observability work we do for clients trying to answer "what does our AI spend actually look like" — CostMon is us solving it for ourselves and every other studio product first.

Where this shows up in client work

• What AI actually costs
• AI cost benchmark by workload