🌿 GreenOps Autopilot

Carbon-Aware Compute Scheduler for AI & Data Workloads

GreenOps Autopilot is an optimization engine that helps developers run compute workloads where and when electricity is cleanest, dramatically reducing the carbon footprint of AI and data processing.

By dynamically routing jobs to greener cloud regions and optimizing execution timing, GreenOps can reduce compute emissions by up to 99% — without increasing cost or sacrificing performance.

Inspiration

AI workloads are growing rapidly — and so is their environmental impact.

Most developers deploy workloads to the closest or cheapest cloud region (such as us-east-1). What is often invisible is the carbon intensity of the electricity powering those datacenters.

The difference can be dramatic: some regions produce up to 50× more CO₂ per kWh than cleaner grids like Montréal or Stockholm.

We built GreenOps to answer a simple question:

What if compute jobs automatically ran where the grid is cleanest?

What it does

GreenOps Autopilot analyzes compute workloads and recommends the lowest-carbon execution strategy.

Users provide:

Hardware requirements (CPU/GPU)
Estimated runtime
Budget constraints
Deadline

The system evaluates global cloud regions and returns:

🌍 Best region and execution window
🌱 Estimated CO₂ emissions
💰 Cost comparison
🤖 AI-generated sustainability insights

Key Features

Carbon-Aware Job Planner

Input your job requirements and receive a carbon-optimized execution plan based on environmental and financial impact.

Multi-Factor Optimization Engine

Our deterministic optimizer evaluates regions based on:

Live grid carbon intensity
Compute pricing
Datacenter availability
Green energy time windows

The system selects the lowest-carbon feasible solution that meets all constraints.

How we built it

GreenOps combines deterministic optimization with real-time carbon intelligence.

We integrated live carbon intensity data from Electricity Maps and modeled compute energy usage using hardware power profiles and datacenter efficiency metrics.

The system evaluates regions across major cloud platforms, including:

Amazon Web Services (AWS)
Google Cloud Platform (GCP)
Microsoft Azure

Each recommendation respects:

Budget constraints
Deadline requirements
Hardware availability

Carbon Calculation

We estimate compute emissions using a standard energy model: CO₂ (kg) = ((Wattage × Runtime × PUE) / 1000) × (Grid Intensity / 1000)

Where:

Wattage = Hardware power draw (TDP)
Runtime = Job duration in hours
PUE = Datacenter efficiency (~1.12 for modern facilities)
Grid Intensity = Carbon emissions per kWh

This allows GreenOps to compare the true environmental cost of running workloads across different regions.

AI Sustainability Insights

GreenOps uses Gemini 2.5 Flash to explain the reasoning behind each recommendation and translate carbon savings into real-world environmental impact.

Example insight:

“This optimization reduces emissions by 18kg CO₂ — equivalent to the carbon absorbed by 45 trees in a year.”

Execution Pipeline

GreenOps includes a persistent pipeline where users can:

Plan workloads
Track compute jobs
Monitor verified carbon savings

This creates a sustainability history for every compute task.

Real-Time Sustainability Dashboard

Our analytics dashboard dynamically tracks:

Total CO₂ emissions avoided
Jobs analyzed
Cost differences
Regional execution distribution

All metrics are generated from real execution data, not simulations.

Challenges

The biggest challenge was accurately estimating compute energy consumption across different hardware and cloud providers.

Since cloud providers rarely expose direct energy metrics, we modeled energy usage using:

Hardware thermal design power (TDP)
Runtime estimates
Datacenter efficiency factors
Live grid carbon intensity data

Balancing cost, performance, and sustainability simultaneously required careful optimization design.

Impact

GreenOps proves that sustainable computing is largely an optimization problem.

The exact same workload can produce dramatically different emissions depending on where and when it runs.

By making those decisions intelligent and automatic, GreenOps turns cloud infrastructure into a carbon-aware system by default.

What's next

Our vision is to evolve GreenOps into a fully automated carbon-aware orchestration platform.

Future plans include:

Connect the Execute button to real cloud provider APIs for automated job dispatch
Carbon-aware CI/CD pipeline integration so every build automatically runs green
Real-time cloud pricing API integration to replace static pricing estimates
Kubernetes scheduler plugin for always-on workload routing