AI cost tracking is not complicated, but it is easy to do badly. Most teams either ignore it until a bill surprises them or over-engineer it into a project that never ships. These are the practices that give you real control with modest effort.
Structure API keys before anything else
Shared API keys make attribution impossible after the fact. Issue one key per team or major use case, named so the owner is obvious, and keep a registry mapping each key to its purpose and expected monthly cost. This one practice, an hour of work, is the foundation everything else stands on.
Track cost per outcome, not just total spend
A total tells you what you spent; it does not tell you whether it was worth it. For each significant workload, track cost against the business outcome it produces. Cost per outcome is the number that turns tracking into management, because it shows which spend is generating proportionate value.
Separate the two kinds of AI cost
Subscription and seat costs are predictable and headcount-driven. Consumption costs scale with usage and swing month to month. Tracking them together hides both. Separate them so each can be forecast and managed on its own terms.
Set soft alerts, not just hard caps
A hard cap that cuts off service is a blunt instrument that disrupts production. Set soft alerts well before the ceiling, at around eighty percent of expected spend, so you get an early warning with runway to respond. Reserve hard caps for experimental workloads where hitting the limit is inconvenient rather than disruptive.
Review weekly, not monthly
Consumption cost moves fast enough that monthly review finds problems after the money is gone. A thirty-minute weekly review comparing cost per call and call volume against the prior week catches issues while they are still cheap to fix.
Make every dollar have an owner
The deepest best practice is accountability. Every dollar of AI cost should map to a team or use case with an owner who can explain what it does and why it is worth it. Tracking that produces owned line items instead of one opaque total is what keeps cost from growing unwatched.
None of these require sophisticated tooling to start. Structure the keys, track cost per outcome, separate the cost types, alert early, review weekly, and give every dollar an owner. That is a working AI cost tracking practice you can stand up this week.