Stage 01 · Planning
AI Infrastructure Planning
Before you touch a purchase order, you need a plan. We review your workloads, define your hardware requirements, and give you a deployment roadmap you can actually execute.
What's Included
Everything you need before you buy.
Workload Analysis
We map your inference or training workloads to hardware specs — batch size, latency targets, throughput requirements.
GPU & Hardware Selection
H100, A100, L40S, or something else? We recommend the right configuration for your workloads and budget.
Power & Cooling Review
GPU racks draw 10–40kW. We calculate your power requirements and confirm your target facility can support them.
Networking Architecture
InfiniBand vs Ethernet, spine-leaf topology, storage connectivity — we design the network before you rack anything.
TCO Modeling
Side-by-side comparison of on-prem, colocation, and cloud for your specific workload at your specific scale.
Deployment Roadmap
A written plan covering procurement timeline, colo selection, deployment milestones, and go-live checklist.
Common Questions
What buyers ask us first.
Do I need colocation, or should I build on-prem?
It depends on your power situation, team size, and scale. We'll walk you through the real tradeoffs — power costs, cooling requirements, physical security, and what happens when hardware fails at 2am.
How much does a GPU server actually cost?
An 8x H100 SXM5 server runs $250–350K new. A100 configs start around $80K. But the hardware cost is only part of the picture — power, networking, and rack fees add up. We show you the full TCO.
How long does the planning phase take?
Typically 1–2 weeks from initial conversation to a written infrastructure plan. Faster if your requirements are well-defined.
Next Stage
Hardware Procurement
Once the plan is locked, we source the hardware.
Let's plan your AI infrastructure.
A 30-minute call is all it takes to understand your requirements and map a path forward.