Compute Flood
Save compute. Save the climate.
Introduction
Companies need both an AI strategy and a sustainability strategy to be competitive, but AI models consume outsized resources. We provide a common interface for LLM API calls to make sure companies get the lowest cost, lowest and resource intensity model without sacrificing efficacy.
Problem
2% of electricity goes to powering data centers, and the world is on pace for 10% by 2030 due to the rise of AI compute workloads. This "AI flood" of unoptimized computing has a severe environmental impact. Small and medium enterprises are particularly vulnerable to the costs and lack resources for mitigation. ComputeFlood solves this by optimally allocating AI workloads to reduce energy/carbon output. Our solution serves AI-driven enterprises across industries seeking lower costs, latency, and emissions.
Opportunity
ComputeFlood's core innovation is an intelligent routing layer that classifies incoming queries and dynamically assigns them to the least resource-intensive AI model that can adequately handle the task. Under the hood, we run extensive evaluations mapping query complexity to model capability/efficiency to optimize this routing policy. This allows product teams to lower costs by avoiding overpowered models for simpler queries. The solution integrates seamlessly with existing AI workflows via an API, and supports custom/proprietary models. By stratifying AI processing, ComputeFlood eliminates energy-hogging one-model-fits-all approaches.