Your LLM Is Working Too Hard, and You’re Overpaying for It

Enterprise AI token costs may rise despite falling unit prices because agentic workflows use more tools, data sources, reasoning steps, and repeated context, with Gartner predicting inference costs per agentic workflow could increase more than fivefold by 2028. The passage argues that much of this expense comes from tool definitions, schemas, and large raw query results being loaded into model context, especially when agents must discover and combine data across multiple systems. It distinguishes these issues from the MCP protocol itself, attributing them to how applications expose tools and insert results into context. While dashboards, rate limits, model routing, caching, and prompt compression can control or reduce spending, the proposed approach is to remove unnecessary context before it reaches a model through a data-access layer that filters, joins, scopes, and caches enterprise data. CData promotes its Connect AI platform as such a managed MCP gateway, citing a benchmark in which a custom multi-source tool reduced usage from 183,541 tokens across 22 calls to 4,427 tokens in one call, a claimed 97.6% reduction, while noting that actual savings depend on workflow design, models, usage, and data volume.

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