Cloud Infrastructure
AI-optimized cloud architecture on AWS, GCP, and Azure
Overview
AI workloads have unique infrastructure demands — GPU availability, cold-start latency, vector storage, and cost spikes. We architect, deploy, and optimize cloud infrastructure on AWS, GCP, and Azure specifically for AI. Your models run fast, scale automatically, and don't blow up your cloud bill.
Capabilities
GPU Provisioning & Management
Right-size GPU instances (A100, H100, L4) for training and inference with spot and reserved capacity strategies.
Model Hosting & Serving
Deploy models with SageMaker, Vertex AI, Azure ML, or custom Kubernetes with auto-scaling and failover.
Cost Optimization
Reserved instances, spot fleets, model quantization, and caching strategies that cut AI infra costs 40–70%.
Observability & Monitoring
Latency tracking, cost attribution, error rates, and model drift detection across your AI stack.
Use Cases
- Production LLM deployment
- Fine-tuning and training pipelines
- Vector database hosting at scale
- Real-time inference APIs
- Multi-region AI applications
- Enterprise RAG infrastructure
Ideal For
- Companies running AI in production
- Teams hitting cloud cost ceilings
- Startups scaling AI products
- Enterprises migrating AI workloads
Frequently Asked Questions
Which cloud should we use?
It depends on your existing stack, data residency, and model availability. We're multi-cloud and recommend the right fit for your situation.
Can you help cut our AI cloud bill?
Yes. We typically find 40–70% savings through right-sizing, spot instances, caching, and model optimization.
Ready to Deploy Cloud Infrastructure?
Book a free AI Deep Dive and we'll map Cloud Infrastructure to your business needs, team capabilities, and budget.
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