AI Data Center Power Optimization SaaS

Schedule AI workloads when electricity is cheapest and grids can handle it

Data centers running LLM training and inference workloads waste 30-40% on peak electricity rates and risk grid curtailment during demand spikes. Our platform integrates live CAISO/ERCOT grid APIs and ML scheduling to shift non-urgent GPU jobs to off-peak windows, cutting energy costs by $200K-$800K monthly per facility while preventing brownout-triggered shutdowns that cost $2M+ in lost compute time.

Key Benefits:

- Reduce electricity spend 25-35% by auto-scheduling training jobs during renewable energy surplus windows and sub-$30/MWh spot pricing periods

- Avoid $2M+ grid curtailment penalties through predictive load-shedding that pauses non-critical inference 15 minutes before capacity alerts

- Increase GPU utilization 18-22% by filling low-demand overnight slots with queued fine-tuning and research workloads that tolerate 6-12 hour delays

MVP Scope: Phase 1: Build real-time grid price aggregator + basic workload scheduler for 1-2 data centers. Integrate with CAISO/spot pricing. MVP targets 15-20% energy cost reduction. No ML forecasting yet—rule-based scheduling only. Dashboard shows cost savings and grid strain metrics.

Tech Stack: Python (FastAPI, PyTorch), PostgreSQL + TimescaleDB, Redis (job queue), Kubernetes, React + D3.js, WebSocket (real-time updates), Grid operator APIs (CAISO, ERCOT, IEX)

Components:

- {'name': 'Real-time Grid Capacity Monitor', 'description': 'Live API integration with grid operators (CAISO, ERCOT, etc.) and electricity pricing feeds. Ingests demand forecasts, renewable availability, and spot prices.', 'tech': ['WebSocket', 'Time-series DB', 'Grid APIs']}

- {'name': 'Workload Scheduler Engine', 'description': 'ML-based scheduler that queues GPU jobs (training, inference) based on grid capacity windows and price thresholds. Prioritizes non-urgent compute during low-cost, high-capacity periods.', 'tech': ['Python ML', 'Job Queue (Celery/RQ)', 'Constraint solver']}

- {'name': 'Cost & Carbon Dashboard', 'description': 'Real-time visualization of energy spend, CO2 emissions, and grid strain. Shows cost savings vs. baseline and carbon offset metrics for compliance reporting.', 'tech': ['React', 'D3.js', 'PostgreSQL']}

- {'name': 'Workload API & Integration Layer', 'description': 'REST/gRPC endpoints for data centers to submit GPU jobs with flexibility windows. Integrates with Kubernetes, Slurm, or custom orchestration.', 'tech': ['FastAPI', 'gRPC', 'Kubernetes operators']}

- {'name': 'Predictive Demand Forecaster', 'description': "Learns data center's historical compute patterns and predicts optimal scheduling windows 24-72 hours ahead. Reduces latency for time-sensitive workloads.", 'tech': ['PyTorch LSTM', 'Prophet', 'Feature engineering']}

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