Companies spend millions of dollars on their cloud performance. Much of this is overspend, but large enterprises cannot risk downtime, and so they hugely overprovision in order to buy peace of mind.
In this article we’ll reveal how AI-based Continuous Optimization can put a stop to this wastage, and employ automated machine learning to help companies save up to 70% on their spend.
One of FinOps’ objectives has always been to enable shifts in companies and organizations – shifts that empower teams to participate in the process of increasing efficiency, optimizing utilization and reducing spend – through a combination of systems, best practices and culture.
An effective FinOps stance cannot be adopted if a company’s applications are habitually running hot, because in such a scenario, a company is not managing its cloud finances well.
- 80% of finance and IT leaders report that poor cloud financial management has had a negative impact on their business.
- 69% admit to regularly overspending on their cloud budget by 25%.
- 57% of them worry about the costs of cloud management on a daily basis.
Gartner predicts that overall cloud spend will exceed $330 billion by 2022. A huge chunk of this expenditure is needless and wasted.
Picture this: a company that currently spends about $50 million on the cloud and grows 20% year upon year will have spent more than $372 million in 5 years. What if, say, 20% of that outlay is overspend? That’s $60 million of needless spend.
But why do companies overspend in the first place? For peace of mind.
As modern enterprises rush to move to a DevOps paradigm and operate a robust CI/CD toolchain, they neglect the post-release portion of the delivery pipeline. To prevent this oversight from wreaking havoc to the entire system, companies over-provision their resources. Performance tuning is only done as a panic response to an unachieved SLA.
Because of this, cloud and mobile apps are not performing to their full potential while accumulating high costs in terms of resource utilization. This is a bonafide FinOps failure.
Leveraging AI for Continuous Optimization is the Key
Continually optimizing cloud-based applications can quell the problem of overspending, but such optimization is far from easy. Even a simple 5-container application can have 255-trillion resource and basic parameter permutations. This is why most application performance management (APM) tools barely scratch the surface when they suggest cutbacks on utilization.
What’s needed is a Continuous Optimization (CO) tool that leverages the capabilities of artificial intelligence (AI) to continuously optimize and tune the performance of applications. Opsani delivers on that.
By integrating into the CI/CD pipeline, AI can tweak and perfect resources and parameters often deemed too complex, in order to touch to find the optimum performance, lowest cost, and maximum efficiency. This ensures that the infrastructure is tuned precisely to the workload and goals of the application.
As a FinOps model strives to improve your teams’ cohesion and efficiency, so does autonomous AI continuously optimize your applications.
Continuous Optimization: A Valuable Addition
Several companies have already implemented CO for their applications and systems. One of them is Ancestry.com. They instituted Continuous Optimization to drive their costs down without hampering performance.
The results were phenomenal: Ancestry.com saw savings of up to 61%, with no degradation in performance.
Aside from Ancestry.com, a major fintech leader that provides SaaS financial management solutions also implemented CO to optimize its cloud operations. They were juggling more than 1,300 virtual machines across AWS, Java, Wavefront, and Spinnaker.
Within just the first quarter of use, the fintech leader saw a 67%-cost reduction, saving millions of dollars.
And it wasn’t only these companies that got staggering numbers when they implemented CO:
- A category-leading business directory service and a well-known video conference company both experienced 30% cost savings.
- A leading PaaS company saved 49% from its total spend.
- A major non-profit yielded a 147% improvement on their systems.
Within FinOps, the goal is to make better decisions while moving faster, creating frictionless relationships between teams in order to increase velocity and spend efficiency. In a mission like this, an AI optimization tool is priceless. Automation takes the worry of optimization and constant performance tuning off of everyone’s plates. This leaves more time for teams to deal with more important concerns, like setting the best practices, and creative pursuits that generate more value for their organization.