Multi-Cloud AI Strategy
GPU availability, pricing and model access differ across AWS, Azure, GCP and specialist GPU clouds. We design a portable AI platform that lets you place each workload where it runs best.
What's getting in the way
Single-cloud AI means accepting one provider's GPU shortages, pricing and model catalog. Multi-cloud done badly means double the complexity. The goal is portability where it pays off and simplicity everywhere else.
GPU availability and pricing change month to month, and each cloud offers different models and hardware. A portable platform lets you place each workload where it runs best today and move it tomorrow, without rewriting applications. We design that platform and the rules for when to use it.
- Placement strategy: which workloads go where, based on cost, capacity and data gravity
- Portable stack: Kubernetes and open serving layers that run on any cloud
- Unified gateway: one API, routing to models across providers
- Capacity planning: reserved, on-demand and spot across vendors
The path we follow
Assess
Current cloud footprint, contracts, data locations and GPU needs.
Design
Portable platform on Kubernetes with a unified model gateway.
Place
Workload placement policy based on cost, capacity and compliance.
Operate
Capacity planning and cost reporting across every provider.
How we deliver it
Cloud-to-AI Migration
Move legacy workloads and data platforms onto infrastructure that is ready for AI.
Explore service →SVC-02GPU Infrastructure Optimization
Cut inference and training spend with better scheduling, quantization and right-sized clusters.
Explore service →SVC-01Private & On-Prem LLM Deployment
Run open-weight models on your own servers or private cloud, with no data leaving your network.
Explore service →Common questions
Isn't multi-cloud more complex?
It can be. We keep portability to the layers where it pays off, mainly serving and the gateway, and keep everything else simple.
Do you include specialist GPU clouds?
Yes. Specialist providers are often the most cost-effective for training and batch inference.
Find out what private AI would cost you.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.