A practical guide to standing up AI FinOps from inside engineering: who owns what, what meetings matter, and what tooling exists today.
Cloud FinOps gave us tagging, reserved instances, and rightsizing. None of it maps cleanly to AI spend. Here's what breaks and what to do instead.
Token costs are not what you think. A deep look at tokenizer differences, output vs. input pricing, context window economics, and why cost-per-token misleads.
Shared API keys are convenient and financially opaque. No attribution, no accountability, no optimization signal. Here's what that actually costs you.
Seat price is the wrong comparison. A real TCO analysis of Copilot vs. Cursor: API overages, productivity lift by use case, context differences, and split-team costs.
Your AI spend is growing and you can't explain it. That's not a cost problem — it's an attribution problem. Here's the distinction that changes everything.
Four stages of AI cost management maturity. Most enterprises are stuck at Stage 1. Here's what each stage looks like, where transitions stall, and how to move forward.
Anthropic API attribution stops at your organization level. This post explains the gap in native tooling and the three approaches teams use to get real team-level cost visibility.
An enterprise AI tool audit reveals shadow AI, shared API keys, and ungoverned spend. Here's a practical step-by-step guide to finding what your organization is actually running.
GitHub Copilot ROI goes beyond suggestions accepted. Here's how to build a real model using time saved, PR cycle time, and bug rate to justify your $19/seat investment.
How to justify AI spend to your CFO requires the right framework — not clean ROI numbers. Here's how engineering leaders can build a credible, defensible AI spend conversation.
Agentic AI cost monitoring requires workflow-level attribution. Token count per call is the wrong unit when one user action triggers a chain of model calls, retries, and reflection loops.