The key to cutting cloud costs without impacting performance. Can an application be both fast and efficient?

Many cloud businesses believe there is a strict tradeoff between speed and savings. Being the fastest to market is a key business advantage, so cost considerations are not prioritized. Instead, focus is shifted to the speed of delivery. Although companies don’t want to be wasteful, speed always comes before running efficiently and minimizing costs. Below we unlock the key to cutting cloud costs without impacting performance.

There is an unspoken belief that cloud cost optimization will always hamper performance. But this is a myth.

Effective cloud optimization can give enterprises the best of both worlds, by empowering them to achieve the speed necessary to excel in competitive markets, whilst preventing them from overspending unnecessarily. 

What is the key to cutting cloud costs without impacting performance? The process begins with identifying the most efficient configurations for operating an application, through learning from data and verifying available settings. These settings can range from the lowest level of detail, such as what CPU architecture an application is running on, up to application-specific information such as what feature flags are enabled.

However, not all forms of cloud optimization are created equal. The most popular methods typically look at a very narrow group of settings, or look only at the application code through the lens of a profiler. This creates a shortsighted view of the possibilities, and totally ignores the interactions between components and settings that may be impacting your application’s cost and performance in unexpected ways. 

Opsani approaches cloud optimization and the resulting cloud cost optimization differently. By leveraging machine learning and AI technologies, the Opsani engine is able to systematically explore those configurations that are far too complex for humans to handle on their own. 

This use of advanced AI is essential for true cloud optimization, because larger applications quickly outpace the capacities of human cognition. The larger the application is, the more components and settings there are within the cloud optimization profile. Some changes – such as resizing an instance, or changing the buffer allocations of a persistent store – can take several minutes to perform. Furthermore, producing precise, statistically verifiable measurements of the cost/performance impacts of a configuration can take minutes or even hours of data gathering. These individual steps of measuring and adjusting the application will take an undetermined amount of work time. And by the time you’re done identifying the optimal settings, the world has moved on, and your developers have committed more code. 

This is why it is essential to automate and integrate cloud optimization into the workflow. The best place to integrate the process into your pipeline depends on the maturity model of your DevOps tooling, processes, and software delivery. Opsani customers who have gone all-in on Continuous Delivery are embracing cloud optimization by allowing the Opsani engine to optimize a canary deployment automatically each time code is pushed to staging/production. Then, they automatically promote the settings throughout clusters, once a new optimal has been identified.

By contrast, customers that still rely on train-based delivery schedules or ad-hoc releases may want to perform cloud optimization after the CI suite passes, and produce an optimization profile to be applied at the same time with the next release. Ideally, this is done by creating infrastructure-as-code assets.

Ultimately, real cloud optimization is synonymous with real cloud cost optimization. When the tech works right, it busts the myth that companies have to choose efficiency or performance. Teams can streamline their cloud bills, and have their apps run better.

Learn more about cloud optimization here.