Autonomous advisor augmented by AI driven insights that saves over 40% of your cloud spend, with assured performance

Enterprises are accelerating their digital transformation initiatives like never before to address the disruption caused by COVID-19. Multi cloud and modern applications have become vital enablers to power this journey. This dramatic shift has raised many pertinent questions - How do we size the right capacity from day 1 as we move to the cloud? How do we ensure that resources are optimally sized on an ongoing basis to reduce costs? How do I schedule workloads only when needed and decommission unused workloads? How do I decide the right purchasing strategy – reserved instance versus on demand? Can we do all of this without impacting application performance?

Wipro’s Surfonomics offering for multi-cloud and container optimization addresses these business challenges and delivers business outcomes by lowering TCO by 40% while assuring performance across multi cloud and container environments.

The unique solution capabilities include -

• Cloud strategy planning: Data driven cloud migration strategy, TCO analysis and optimal environment on cloud day 1
• Optimal resource management: AI powered analytics to resize, retire and smart parking of workloads – Cloud resource optimization to save costs with assured application performance
• Right purchasing strategy: Visibility on reserved instances coverage, utilization and decision on reserved instances versus on demand instances to save costs
• Integration capabilities: ITSM integration for implementing recommendations and CI/CD integration for continuous optimization

The offering comprises of advisory services and ongoing execution leveraging Wipro Cloud Studios. Customers get a standard service from Wipro that surfs, senses and recommends optimization avenues. The Surfonomics autonomous engine possesses recommendation and automated executions with integrations into enterprise service management processes. All of this happens securely without impacting application performance.