AI token cost is the number almost every team gets wrong on the first attempt, and it is the number that decides whether an AI feature ships or quietly gets shelved. A prototype that costs a few pence per request feels free. The same prototype, wired into a product with twenty thousand users a month, […]
FinOps for AI has moved from a niche discipline to the single biggest cost-control question facing technology leaders, and most organisations are discovering it the hard way — through an invoice nobody forecast. A large language model that costs pennies per request in a pilot becomes a six-figure annual line item once it is embedded […]
LLM API pricing in August 2026 spans a 640-fold gap between the cheapest and dearest flagship output token. This guide compares OpenAI, Anthropic, Google, Mistral, DeepSeek, Qwen and xAI rates side by side. It then shows how caching, batch and off-peak discounts cut real bills by 50–98%.
An AI budget behaves nothing like a software budget: it moves the moment somebody writes a longer prompt, enables a more capable model or ships an agent that retries five times instead of once. This guide sets out the controls that keep inference spend predictable, covering token budgets at request, session and tenant level, a capability ladder and routing strategy that sends each task to the cheapest model that can do it, hard and soft usage limits that stop runaway agent loops, prompt caching and context discipline, tagging and unit-cost dashboards, the monthly operating cadence, and a fully worked support-copilot example.
A business decision guide to RAG, fine-tuning and long context windows: what each one actually changes, how the costs compare at real query volumes, a six-question scoring matrix, the hybrid patterns mature teams use, and the governance obligations each option creates.
Inference Economics guide for IT and finance leaders covering AI token costs, model routing, prompt caching, batch inference, agent budgets, and cloud spend control.
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