AI & Data · AI Cost Control & Observability
Know what your AI systems actually cost — and make the spend predictable.
Token budgets, inference costs, and API bills add up fast once AI hits production. We audit your current spend and set up the controls to keep costs predictable as you scale.
What we deliver
- 01Spend audit: map all AI-related costs across your stack — API calls, compute, model hosting.
- 02Observability setup: token usage, latency, cost-per-request tracking per workflow.
- 03Budget controls: per-team, per-workflow, or per-model budgets with alerting.
- 04Reporting: dashboards your finance team can read alongside your engineering metrics.
Terms
EngagementFixed fee — scoped after a brief review of your stack
Delivery2–4 weeks
ToolingWorks with OpenAI, Anthropic, Azure AI, and open-source models
FAQ
Common questions.
We don't have a lot of AI spend yet. Is this worth it?
The best time to instrument is before costs grow. Retrofitting observability at scale is ten times harder.
Does this require changes to our application code?
Minimal — usually a wrapper or middleware layer. We design it to be non-intrusive to the existing application.