Over the past two years, token costs have surged dramatically despite falling per-token prices. Enterprise AI bills have tripled as token consumption exploded—agentic AI uses 10–30× more tokens per task than a chatbot. This requires a careful token-sizing strategy and disciplined model selection. Critical principle: never use expensive frontier models as a Google Search alternative. Organizations must: • Right-size models: Deploy smaller, fine-tuned models for domain-specific tasks to minimize unnecessary token use • Implement LLM routing: Cut costs by sending simple tasks to cheaper models • Cache aggressively: Prompt and semantic caching can save 30–60% on repeated queries • Embed governance: Implement real-time monitoring, budget alerts, and chargebacks to business units as essential guardrails. #TMinsights