The Hidden Cost of Cloud AI

Every API call has a footprint. Local-first AI changes the equation.

What Cloud AI Actually Costs

⚡

Energy

Data center compute + network + cooling for every query

🌡️

Carbon

Estimated 25-50g CO2 per complex AI query

💧

Water

Billions of gallons annually for data center cooling

🏢

Concentration

AI capability controlled by 3-4 companies globally

The Local-First Alternative

Cloud AI

  • ✗ Every query = data center round-trip
  • ✗ Cooling infrastructure required
  • ✗ Network transmission overhead
  • ✗ Redundant compute (same questions, repeated)
  • ✗ Vendor lock-in and dependency

Local-First AI

  • ✓ Efficient local chips (M4 = ~30W)
  • ✓ Passive cooling sufficient
  • ✓ Zero network for inference
  • ✓ RAG = compute once, retrieve forever
  • ✓ Full independence and portability

What We Actually Run

The Active Mirror Production Stack

Hardware
Mac Mini M4 (24GB unified memory)

Power Draw
~30W typical, ~65W peak

Daily Energy
~0.7 kWh (24/7 operation)

Models
Ollama (Qwen, Llama, Mistral)

Vector Store
ChromaDB (~9,500 chunks)

Location
Goa, India (your grid, your rules)

How We Help

🌱 Green AI Audit

Assess your current AI carbon footprint. Identify migration paths to lower-impact architecture.

🌍 Democratization Support

Low-cost, offline-capable AI deployment for organizations with limited resources or connectivity.

🧠 Cognitive Load Reduction

Context persistence and identity systems so humans stop repeating themselves to machines.

🏛️ Resilience Planning

Ensure your AI capability survives vendor outages, price changes, and policy shifts.

Get an Impact Assessment

Understand the true cost of your AI operations — and what local-first could save.

Request Assessment →