Strategies, benchmarks, and best practices for controlling LLM costs, enforcing AI governance, and protecting sensitive data across enterprise AI coding tools.
Practical guidance for engineering leaders and CISOs managing AI coding tools at scale.
How token-based, agent-driven spend breaks cloud FinOps assumptions, who owns the AI budget, what the FinOps Foundation is standardizing, and what a complete practice covers.
One in five enterprises can't stop a runaway agent's spending in real time. Why provider-native controls can't intervene mid-run — per their own docs — and what real in-path enforcement requires.
Single-request caps, session budgets, and per-developer limits — the controls Cursor doesn't give you natively.
Every line of code your developers write with AI tools passes through a third-party API. Here's how to inspect, protect, and control that traffic.
27.4% of data sent to AI tools contains sensitive information. Three-layer detection catches what file-path exclusions miss.
Your team uses all three. Each has different governance capabilities — and different blind spots. Here's the full breakdown.
A developer kicks off a Cursor agent and goes to a meeting. 40 API calls later, $180 in tokens burned. Here's how to prevent it.
Your organization spends $50K/month on LLM APIs. Can you tell your CFO which team is responsible for what portion?
CFOs are asking what AI costs and what the return is. Here's the practical framework for measuring AI ROI — from visibility to attribution to continuous optimization.
The price gap between the cheapest and most expensive LLM is 300x. Intelligent routing, caching, and token optimization cut costs 7-37% per tool — measured, not modeled — with quality retention enforced by threshold.