How CLōD Scaled AI Workload Routing with Real-Time Energy Intelligence

Challenge

CLōD needed to prove that AI inference compute could flex dynamically with grid conditions — routing workloads across multiple data centers based on live energy signals — without adding latency or complexity for developers.

The Problem

• Routing AI inference requests across multiple data centers in real time
• Avoiding cost spikes during grid stress events without manual intervention
• Maintaining low latency for end users while optimizing for energy cost
• Integrating live grid signals (price, curtailment, demand response) into routing logic
• Validating the thesis that flexible compute is practical at production scale

Solution

CLōD built its AI inference routing layer directly on top of LōD's energy intelligence infrastructure. Every connected data center feeds live grid data into the platform — pricing, curtailment status, demand response signals, and available compute capacity. CLōD's router scores each facility in real time and sends each request to the optimal location.

Results

Implementation

CLōD deployed on LōD infrastructure using the same data layer that feeds signals to data center operators. The routing layer was built to read live grid signals and score facilities continuously. Developers interact through a single API endpoint — the energy-aware routing happens entirely under the hood.

Impact

CLōD proved that energy-aware AI workload routing is not a concept — it is a production system handling real inference requests across multiple facilities. The same infrastructure is available to any operator in the LōD network. Data center operators building their own flexible compute stack can use the LōD Data MCP, Control Layer, and Automation MCP to replicate the same foundation.