Strategic multi-cloud implementation across AWS, Azure, and Google Cloud with unified orchestration, achieving vendor independence and 40% cost optimization through intelligent workload distribution.
A global financial services company was locked into a single cloud provider, facing vendor dependency risks, limited geographic coverage, and suboptimal pricing for different workload types. They needed a strategic approach to leverage multiple cloud providers while maintaining operational efficiency.
The challenge was to design and implement a multi-cloud architecture that would provide vendor independence, cost optimization, and improved global performance while ensuring seamless orchestration and management.
Implemented Kubernetes-based orchestration with Terraform for infrastructure as code, enabling seamless workload management across AWS, Azure, and GCP.
Developed AI-driven workload placement algorithms considering cost, performance, compliance, and geographic requirements for optimal resource allocation.
Established unified monitoring and observability platform using Prometheus, Grafana, and custom dashboards for comprehensive multi-cloud visibility.
Implemented consistent security policies and compliance frameworks across all cloud providers with automated governance and audit trails.
Achieved 40% cost reduction through intelligent workload placement and leveraging best pricing from each cloud provider for specific services.
Reduced global latency by 50% through strategic geographic distribution and edge computing capabilities across multiple cloud regions.
Eliminated vendor lock-in risks with portable architecture and standardized deployment processes across all cloud platforms.
Achieved 99.99% availability SLA through multi-cloud redundancy and automated failover capabilities across providers.