AI Cost Intelligencefor Enterprise Teams
Analysis for CTOs, platform engineers, and FinOps leaders operating multi-provider AI systems.
AI inference cost governance across multi-model stacks
When your platform runs GPT-4.1, Claude Opus 4, and Gemini 2.5 Pro simultaneously, provider-level dashboards stop being useful. Here is how to govern inference cost across a heterogeneous model estate.
Read featured insightUnit economics for LLM features: cost-per-workflow and margin guardrails
Token cost is an infrastructure metric. Cost-per-workflow is a business metric. Here is how to build the bridge — and how to set margin guardrails before a feature ships.
Read articleReal-time AI spend anomaly detection in production
LLM spend can increase by 50x in minutes — a prompt injection, a runaway retry loop, or a misconfigured context window. Here is how to detect it before the invoice arrives.
Read articleFinOps architecture for RAG systems in 2026
Retrieval-Augmented Generation has four distinct cost layers, each with different optimization leverage. Most teams measure only one of them.
Read articleEnterprise chargeback and showback for AI platform teams
AI spend is now large enough to require the same internal financial controls as cloud infrastructure. Here is how to implement chargeback and showback without building a separate cost allocation system from scratch.
Read articleUnderstanding LLM pricing: input, output, and cached tokens
How providers charge for tokens and what it means for your bill.
Read articleAI FinOps best practices for 2025
Practical ways to control AI spend without slowing down product.
Read articleReducing token waste in production
Optimize context length, caching, and model choice to cut costs.
Read articleCost attribution for platform and product teams
Break down AI spend by team, project, and environment.
Read articleWhy anomaly detection matters for AI spend
Catch spikes and misconfigurations before they hit the budget.
Read articleUpdated weekly with 2026 market context