Defined term
Multi-LLM architecture
Routing different tasks to different models based on cost, quality, latency, and capability tradeoffs.
Multi-LLM architecture uses more than one foundation model in the same product. A classification task may go to a small fast model, a summarization to a mid-tier model, and a high-stakes reasoning step to a frontier model. The router can be rule-based (by task type) or learned. Multi-LLM lets teams optimize cost per call without sacrificing quality on the steps that matter.
When it matters
When cost per call matters and your workflow has steps of varying complexity. Routing simple classification to a fast small model and reasoning to a frontier model can cut total inference cost by 40-70% with no quality drop.
Real example
A support workflow that routes intent classification to Haiku ($0.003/case), retrieval and summarization to Sonnet ($0.02/case), and final answer generation to Opus only for cases with low retrieval confidence ($0.15/case). Average blended cost: $0.04/case vs $0.15 single-model.
KPIs to watch
Average cost per case (target: -40% vs single-model baseline), routing accuracy (right model for the step, >95%), latency P95 per route (kept under SLA).
Related terms
Frontier model
The leading-edge foundation models with the highest reasoning, coding, and multimodal capabilities.
Agentic AI
AI systems that can plan, take multi-step actions, and use tools to complete tasks autonomously.
Autonomous agent
An AI agent that completes a defined task without per-step human input.
RAG (Retrieval-Augmented Generation)
Generation grounded in retrieved source documents rather than the model's parametric memory alone.
See it in action
We use this every week
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