llm-token-pricing

Token-based billing is the primary cost lever for teams building with LLMs in production. Coverage spans how providers price input, output, and cached tokens, how agentic and multi-turn workflows compound costs through growing context windows, and what design patterns keep spend under control. Specific topics include cross-model pricing comparisons, prompt cache mechanics including write fees and TTLs, and the billing edge cases that produce unexpected invoices at scale.

Glowing cube tokens flowing from a laptop into a meter, showing LLM token billing in blue and amber streams

llm-token-pricing, agentic-ai

The Hidden Cost of Agentic AI: Understanding LLM Token Billing

Agentic flows can make LLM bills snowball: every tool call adds context that gets sent again on the next turn. Prompt caching can keep those costs under control, but cache minimums, write surcharges, and TTLs mean you need to design your agent around the cache—not just hope it saves money.