Energy-Aware Routing (Green AI)
Routing decisions that consider the energy and carbon profile of each model, not only quality and cost.
What is Energy-Aware Routing (Green AI)?
Energy-aware routing prefers smaller, more efficient models for high-volume work where the quality threshold is met, reserving frontier models for steps that genuinely need them. See Orchestration & Energy research and the white paper How We Reduce Energy Consumption.
What is an example of Energy-Aware Routing (Green AI)?
A document pipeline sends routine language detection and metadata extraction to a compact local model, escalates ambiguous cases to a larger model, and batches non-urgent work when lower-carbon power is available.
How is Energy-Aware Routing (Green AI) different from related concepts?
Cost-aware routing minimizes financial spend. Energy-aware routing minimizes energy use or emissions. They can align, but electricity prices, hardware utilization, and carbon intensity can make them diverge.
What should enterprises evaluate for Energy-Aware Routing (Green AI)?
- Set task-level quality thresholds first so energy savings do not silently degrade outcomes.
- Measure end-to-end energy per completed task, including retries, retrieval, idle hardware, and failed responses.
- Keep policy and residency constraints as hard gates; optimize energy only among models permitted for the request.
Related terms
Putting Energy-Aware Routing (Green AI) to work?
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