Why We Need a Proper Control Plane for LLM Usage - Budget Caps and Semantic Caching
Why We Need a Proper Control Plane for LLM Usage
Running AI agents at scale without cost controls is like giving every employee a corporate credit card with no spending limit. It works until it does not, and then you get a $10,000 API bill for a weekend of runaway agents.
The Problem
There is no standard infrastructure for managing LLM spending across multiple agents and workflows. Most teams track costs retroactively - they check the dashboard at the end of the month and react to surprises.
What is needed is a control plane - a layer that sits between your agents and the LLM APIs and enforces policies in real time.
Budget Caps Per Action
Not every agent action is worth the same amount. A code review might justify $0.50 in API costs. A simple file rename should cost less than $0.01. Without per-action budgets, a single agent can burn through your daily limit on one poorly-scoped task.
Per-action budgets look like:
- Define cost ceilings for each action type
- Route expensive actions to cheaper models when possible
- Kill requests that exceed their budget before they complete
- Alert when an agent's spending pattern deviates from baseline
Semantic Caching Cuts 40%
Many agent requests are semantically identical to previous requests. "What does this function do?" asked about the same function twice should return a cached response, not make a new API call.
Semantic caching using embeddings can reduce costs by 40% in typical agent workloads. The cache does not need exact string matching - it uses embedding similarity to identify when a new request is close enough to a cached one.
Build It Before You Need It
The best time to implement cost controls is before you have a cost problem. A basic control plane with per-action budgets and semantic caching takes a weekend to build and will save you thousands.
Fazm is an open source macOS AI agent. Open source on GitHub.