Infrastructure that treats GPUs like the scarce capital they are.
AI workloads break conventional cloud assumptions — bursty GPU demand, massive egress, latency-sensitive inference. We design landing zones, serving platforms, and hybrid stacks that keep both your latency and your CFO happy.
✓ depth: Azure primary · AWS · GCP · on-prem hybrid
✓ ip transfer: complete · lock-in: none
✓ delivery: hyderabad · timezone overlap: US/EU
# every claim on this page is contractually testable
Your cloud bill is an architecture decision you made implicitly.
GPU instances idling at 12% utilization, embeddings recomputed nightly that never changed, cross-region egress nobody mapped — AI infrastructure waste is structural, not behavioral. It's fixed by architecture: scheduling, caching, placement, and quotas designed in from the start.
Capabilities
AI landing zones
Identity, networking, policy guardrails, and cost controls designed for AI workloads on Azure, AWS, or GCP — IaC from day one.
GPU orchestration
Kubernetes-based GPU scheduling with time-slicing, MIG partitioning, spot orchestration, and queueing that pushes utilization past 70%.
Model serving platforms
vLLM, Triton, and managed-endpoint architectures with autoscaling, caching layers, and multi-provider failover.
Hybrid & on-prem AI
Air-gapped and data-resident stacks — open-weight models, vector stores, observability — for workloads that can't leave your network.
FinOps for AI
Per-team, per-model, per-request cost attribution with budget alerts and automatic anomaly detection. You can't govern what you can't see.
Migration & modernization
Lift, re-platform, or rebuild — sequenced to keep production serving while the platform underneath it improves.
The approach
A sequence, because the order is the point: each phase gates the next on evidence.
Assess
Workload inventory, cost baseline, and constraint mapping — data residency, latency budgets, compliance boundaries.
Design
Landing zone and serving architecture as IaC, with the cost model attached to every design decision.
Build
Platform deployment with progressive workload migration, validation at each step, production traffic protected throughout.
Operate & optimize
Utilization tuning, cost governance dashboards, and handover to your platform team with runbooks.
Deliverables
- Landing zone (Terraform/Bicep, fully owned)
- GPU orchestration layer with utilization targets
- Model serving platform with autoscaling
- Cost attribution and FinOps dashboards
- Hybrid/on-prem reference implementation
- Security and compliance baseline
- Migration runbook and rollback plans
- Platform team training