Today’s companies need to determine whether their software rollouts are successful to ensure good user experience, software stability, reliability, and effectiveness during rollouts. To assess their rollouts, they monitor metrics like memory consumption, CPU usage, error rates, and more.

SaaS providers prefer to host their solutions centrally on the cloud as a single deployment unit and share them across all their customers (tenants) in order to achieve strategic business objectives. To support this, SaaS generally demands cloud-managed services to help implement necessary automation, security, and observability specifically for their multi-tenanted compute layers.

What does it take to not only reduce cloud costs but build an efficient approach to both cloud operations and FinOps? NetApp spoke to analyst firm IDC to gain valuable research insights and find the answers to pressing FinOps questions. Watch the series of roundtable interviews below, featuring host Tom Shields, Senior Marketing Director at NetApp; Jon Bock, VP of Marketing at Spot by NetApp; and Jevin Jensen, Research VP of Infrastructure and Operations at IDC.

Wherever you are on your cloud journey, you’re likely under pressure to cut or at least control your cloud spend. You may be undertaking “quick fix” cost cutting tactics such as turning off resources or moving workloads to cheaper services. But, as you may be finding out, tactical, reactive cost management activities generally only work for the short term and do not solve systemic inefficiencies that only increase over time if not addressed.

At Spot by NetApp, we see an increasing number of customers embracing Azure either as their primary cloud or as part of a hybrid or multi-cloud deployment strategy. While these customers are reaping significant business benefits from Azure, optimization of application infrastructure, controlling costs and mitigating operational burden continue to challenge Azure cloud operations (CloudOps) teams.

Let’s get right to the point: most Kubernetes workloads are underutilizing CPU and memory, with 49% of containers using less CPU than their defined requests. Although Kubernetes provides users with the ability to define resource guidelines for containers based on CPU and memory needs, it can be difficult to define and maintain these resource requirements for dynamic applications, especially in fast scaling scenarios.