# AI Cost Management Managing the financial costs of using AI systems is a critical engineering concern that scales with adoption. ## Pricing models API providers typically charge per-token (input and output priced separately), per-request, or via subscription tiers. The cost difference between models can be 100x or more for the same task, making model choice a financial decision as much as a technical one. That decision is not stable either: [[AI API prices are rising]] across the closed-weight frontier, so it has to be revisited with each release rather than set once. ## Cost optimization strategies - **[[Model routing]]**: route simple tasks to cheap models, complex tasks to expensive ones - **Caching**: store and reuse responses for repeated or similar queries - **Batching**: group requests to reduce overhead and take advantage of bulk pricing - **Local fallback**: run [[Running AI Models Locally|local models]] via [[AI Open Weight Models]] or [[Small Language Models (SLMs)]] for tasks that don't need frontier capabilities - **[[AI Quantization]]**: reduce model size and inference cost at acceptable quality tradeoffs ## Hidden costs The obvious per-token price is only part of the picture. Context window waste (sending irrelevant tokens), retry loops from poor prompts, and over-provisioning (using GPT-4 class models for classification tasks) silently inflate costs. [[Context Budget]] and [[Token Budget]] discipline matters. [[Context Compression]] can reduce input costs significantly. ## ROI calculation Measure AI spend against the value produced: time saved, quality improvement, tasks that were previously impossible. A $500/month API bill that replaces 20 hours of manual work is cheap. A $50/month bill generating garbage is expensive. ## References ## Related - [[AI API prices are rising]] - [[Model routing]] - [[Running AI Models Locally]] - [[AI Open Weight Models]] - [[Small Language Models (SLMs)]] - [[AI Quantization]] - [[Context Budget]] - [[Token Budget]] - [[Context Compression]]