Architect, deploy, and scale AI workloads on AWS, Azure, and GCP — with autoscaling, cost optimization, and enterprise security built in from day one. Your AI deserves infrastructure that's as smart as it is.
AI systems are only as reliable as the infrastructure underneath them. Yeskay's Cloud AI Infrastructure practice designs and manages cloud environments specifically optimized for AI and ML workloads — from GPU-accelerated training clusters to real-time inference APIs that handle millions of requests.
Our certified architects have designed production AI infrastructure for organizations across healthcare, finance, logistics, and retail — with a relentless focus on performance, security, and cost efficiency.
From initial architecture design to ongoing cost optimization and 24/7 operations support.
End-to-end architecture blueprints for AI workloads — compute, storage, networking, security, and data flow — designed for your specific performance and compliance requirements.
GPU and TPU cluster configuration, distributed training setup, spot instance management, and training job orchestration — cutting training costs without sacrificing speed.
Low-latency inference APIs with autoscaling, load balancing, A/B testing, and canary deployments — ensuring your models serve predictions reliably at any traffic volume.
Rightsizing, reserved instance planning, spot fleet management, and storage tiering that typically reduce cloud spend by 30–50% without performance impact.
Zero-trust network architecture, encryption at rest and in transit, IAM policy design, and compliance frameworks for HIPAA, SOC 2, and GDPR.
CI/CD pipelines for ML, automated model deployment, drift monitoring, and retraining triggers — bringing DevOps best practices to your entire ML lifecycle.
Real-world cloud AI deployments across regulated and high-scale environments.
End-to-end encrypted AI infrastructure for healthcare organizations — PHI data isolation, audit logging, and compliance controls baked into every layer.
Sub-100ms inference APIs serving millions of predictions per day — with autoscaling, multi-region redundancy, and zero-downtime deployments.
Federated data architecture spanning AWS and Azure — enabling teams to use best-of-breed services on each platform while maintaining unified data governance.
Cost-optimized GPU cluster using spot instances and preemptible VMs — cutting training costs by 60–70% while maintaining training throughput and reliability.
Event-driven, serverless ML batch pipelines for data processing and model retraining — zero idle cost, infinite scalability, and fully managed operations.
Infrastructure audits and optimization engagements that consistently deliver 30–50% cloud cost reductions for organizations that grew fast without governance in place.
From startup launches to enterprise cost optimization — cloud work that delivered measurable impact.
End-to-end AWS infrastructure for AI diagnostics platform — VPC, ECS Fargate, encrypted RDS, GuardDuty — fully HIPAA compliant, launched in 11 weeks.
8-week FinOps optimization: rightsizing 140+ EC2 instances, spot fleet implementation, Reserved Instance commitments, and tagging governance — from $2.8M to $1.6M annually.
Certified across all three major cloud providers and the leading MLOps and infrastructure platforms.
A methodical approach to cloud infrastructure that prioritizes security, reliability, and cost efficiency.
We assess your current cloud setup, identify gaps, security risks, and cost inefficiencies — then design the target architecture that fits your AI workloads.
Detailed cloud architecture blueprints including compute, storage, networking, security, and monitoring — with cost estimates and trade-off analysis.
All infrastructure provisioned via Terraform or Pulumi — repeatable, auditable, and environment-consistent from dev through production.
24/7 monitoring, incident response, and continuous FinOps optimization — keeping your infrastructure reliable, secure, and cost-efficient over time.
Whether you're starting from scratch or optimizing what you have, we'll design the right cloud architecture for your AI workloads.