Modern AI agents make dozens of LLM calls to complete a single task. Plan a step, pick a tool, parse a result, summarize the context, decide what to do next, draft a reply. Most teams route every one of those calls to GPT-4 or Claude Opus because it's the safe default.
The truth is, most steps in a typical agent flow don't need a frontier model. Routing decisions, tool argument extraction, intermediate summaries, classification — smaller models handle them just as well, at a fraction of the cost.
But nobody downgrades a step, because nobody wants to be the person who broke an agent in production to save a few thousand dollars. So the spend keeps growing, multiplied across every step, every run, every user.
Step-by-step analysis.
We don't just track total spend, we break it down by step in your agent flow, so you can see exactly which decisions, tool calls, and reasoning steps are eating your budget.
Tested on your traffic.
Not on someone else's benchmark. Recommendations come from your real production runs, not a generic leaderboard.
Quality first.
Every recommendation includes a measured equivalence rate with confidence intervals. We don't suggest a switch unless the data is conclusive.
Works with your stack.
LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or custom-built agents. OpenAI, Anthropic, Google, and open models.
Read-only by default.
We measure and recommend. You stay in control of what actually ships to your agent.
Join the waitlist and we'll reach out when early access opens.
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