Per-tenant AI cost attribution: why aggregate dashboards are not enough
Aggregate AI spend hides who is driving cost. Per-tenant attribution shows who to charge, who is profitable, and where margins leak.
The token budget problem: why your production agents run out of room
Context windows are finite. Production workloads are not. Here is what actually breaks when an agent exhausts its budget, and three patterns that prevent it.
The case for structured outputs in production AI
Most production AI systems parse prose from LLMs instead of requesting structured JSON, and the cost and reliability gap this creates is larger than expected.
Reading an LLM bill: line items that actually matter
Most LLM bills get scanned for total cost. Seven line items carry the real signal. A 5-minute monthly review that turns the bill into a diagnostic.
Caching strategies for LLM applications
LLM responses are expensive, slow, and often repeated. Here is how to cache them without building a system that silently returns stale answers.
Why Your AI Gets More Expensive Over Time (And How to Reverse It)
Three months after launch, one company's AI bill tripled. Distillation, prompt compression, and model routing can cut inference costs 50-80%.
AI Implementation Costs in 2026: What Companies Actually Spend
A weekend AI prototype cost $47. The production version cost $180,000. Here is what companies actually spend, and where they overspend.