We decreased our LLM costs with Opus
By implementing a tiered LLM architecture using Haiku as a triage agent to filter duplicate issues before escalating to the more expensive Opus model, the organization reduced overall LLM costs while improving investigation quality—with 80% of failures resolved without reaching the frontier model. This cost-effective approach demonstrates that strategic model layering, combined with agent-driven data access patterns and hierarchical task decomposition, can deliver superior performance at lower expense than relying on a single capable model. IT leaders should reconsider their generative AI cost structures, as intelligent routing and selective model deployment can dramatically improve ROI on LLM investments.
Hacker News3 min read
By implementing a tiered LLM architecture using Haiku as a triage agent to filter duplicate issues before escalating to the more expensive Opus model, the organization reduced overall LLM costs while improving investigation quality—with 80% of failures resolved without reaching the frontier model. This cost-effective approach demonstrates that strategic model layering, combined with agent-driven data access patterns and hierarchical task decomposition, can deliver superior performance at lower expense than relying on a single capable model. IT leaders should reconsider their generative AI cost structures, as intelligent routing and selective model deployment can dramatically improve ROI on LLM investments.