Measured results from Fortune 500 production deployments of Fractal digital twins in utilities, telecommunications and financial services, over five years.
10 million customers. One billing run. Same data, different architecture.
Same workloads, same data: what you pay to run them.
The same AI workload, before and after: what actually lives in your building.
What it takes to go from decision to production.
High-end consultants to deploy and manage a traditional enterprise AI stack, plus ongoing vendor support contracts.
One programmer. Fractal's onboarding team guides the twin setup, AI integration and deployment.
Typical for enterprise AI on legacy database architectures, with multi-vendor dependencies and custom integration work.
A parallel deployment alongside existing systems. Nothing changes for current operations; real production numbers within 90 days.
Measured results from Fortune 500 production deployments over five years.
| Dimension | Before Fractal | After Fractal |
|---|---|---|
| System downtime | Hours per month, with scheduled outages | Under 30 seconds per year |
| Software licensing | Database, virtualization and cloud-specific services, renewed annually | Eliminated: runs on commodity hardware you own |
| Vendor lock-in | Deeply embedded in database, virtualization and hyperscaler ecosystems | None: hardware-agnostic, with no external cloud dependency |
| New feature delivery | 1–6 months per feature cycle, through multi-vendor approval chains | Daily: the full application stack in every Fractal instance |
| AI performance ceiling | Constrained by database I/O and middleware latency | ≥ 10× faster, up to 1,000,000× measured, via Locality Optimization |
The question every enterprise AI architect must answer before going live.
What happens when 1,000 enterprises replace legacy infrastructure with Fractal digital twins.
From ~2 MW per enterprise down to ~1 kW, a 99.95% reduction in power draw per site, across 1,000 deployments: equivalent to powering 1.6 million U.S. homes for a year.
Based on a 2 MW average enterprise data center vs. a 1 kW Fractal footprint × 8,760 h/yr.
At the U.S. average grid intensity of 0.386 kg CO₂ per kWh, removing 17.5 TWh of data center load avoids 6.8 million metric tons of CO₂ a year: equivalent to taking 1.5 million cars off the road.
U.S. EPA grid emissions factor, 2024. Excludes cooling water and embodied carbon.
Each enterprise saves $3M–$5M a year in data center capex and opex, database and virtualization licensing, and cloud fees: over $4B a year across 1,000 deployments, plus 15+ large data centers that never need to be built.
Conservative estimate. Actual savings vary by enterprise scale and licensing baseline.
Every enterprise deploying AI on structured data faces the same fork in the road.
Same AI. Different architecture. Different world.
Figures are Fractal-reported results from Fortune 500 production deployments in utilities, telecommunications and financial services.
A 90-day deployment at production scale, alongside your existing systems. No disruption. Your data. Real numbers.