How has the recent turmoil within the OpenAI offices changed your plans to use GPT in a business process or product in 2024?
Increased uncertainty means we are more likely to evaluate alternative AI chatbots and LLMs.
No change in plans, though we will keep an eye on the situation.
With Sam Altman back in charge, we are more likely to go all-in with GPT and LLMs.
What recent turmoil?
Cloud Services / DevOps

How to Leverage AI for Efficient Cloud Management

If you’re asking, "why AI?" you’re in the right place. In fact, AI is practically knocking down your door to help handle cloud issues.
Oct 19th, 2021 10:09am by
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I bet it hasn’t been long since you heard someone grumbling about spiraling cloud costs. Next time they do, perhaps it would be worth your while to find out what they’re doing about it. According to a recent report by the FinOps Foundation, 49% of FinOps practitioners have zero automation in place to handle cloud spend. That’s nada, nothing, zip.

If you’re asking yourself what can artificial intelligence really do to help, you’re in the right place. In fact, AI is practically knocking down your door to help handle cloud inefficiencies.

What Are Cloud Inefficiencies?

Maxim Melamedov
Maxim is the co-founder and CEO of Zesty. With over 13 years of experience in the tech industry, including a prior career in homeland security, Maxim thrives on solving complex problems and disrupting previously established norms.

Cloud efficiency is the ability to make the best use of your cloud environment with the lowest cost and the least amount of waste. Unfortunately, it’s not as easy as it sounds, and cloud inefficiencies are common, largely caused by the unpredictability of a cloud environment.

When you start out on the cloud, your initial project is likely to be easy to control, but as the cloud begins to show benefits, and companies decide to spread the wealth across departments, projects, functions and teams, cloud inefficiencies start to crop up all over the place. These are especially noticeable when it comes to capacity management such as reserved instances and savings plans, visibility and monitoring of cloud consumption, and wasted resources like idle compute or unused data.

Behind all of this is a lack of clarity into what you need as a business. It’s a tale as old as time. DevOps can’t accurately identify exactly what they will need for their workload ahead of the fact and usually have huge demands in terms of deadlines and release cycles. As a result, they tend to over-provision resources to make sure they never become forced to pay heavy on-demand prices. They also just don’t have the time or the human resources available to keep one eye on consumption and trends around their usage while also completing their essential day job.

Enter: Artificial Intelligence for Cloud Efficiencies

Even if you put a full team of people in charge of managing cloud efficiencies, that wouldn’t be money well spent as AI can do a much better job. Today’s AI works tirelessly behind the scenes to accurately analyze cloud usage and come up with ways to reduce or even eliminate inefficiencies on the cloud. Here are just some of the advantages of AI:

24/7 cloud monitoring: Your engineers have better things to do than monitor your cloud infrastructure. Even if you put them on the task 24/7, there’s a limit to how many data points they can analyze without losing accuracy. AI offers the perfect solution. AI algorithms can run 24/7 to accurately and efficiently measure your usage in real time. Using this technology, your cloud can effectively monitor itself and ensure it continues to function at its optimum capacity.

Instantly identifying right-sizing opportunities: Your cloud is constantly fluctuating. Demand increases and decreases at the drop of a hat, and these changes are not always 100% predictable. Why should you pay for what you’re not using? For humans, trying to react to these fluctuations fast enough is nearly impossible. For AI, it’s child’s play. As your compute or disk usage scales up or down, AI can come to the rescue by monitoring, analyzing and instantly reacting in real time. Resources are adjusted according to real-time application needs, right-sizing instance types to workload needs and adding the automated ability to eliminate or downsize instances automatically behind the scenes. No more manual changes and configurations – that’s why they call it right sizing.

Managing EBS: Elastic Block Storage is a complex beast. You may have EBS volumes that are entirely orphaned or idle, and you may have necessary EBS volumes where you’re paying for higher performance than you need. AI can find unused EBS, predict usage trends and even automatically merge and detach volumes as necessary to ensure optimal disk utilization.

Predictive intelligence: Smart machine learning algorithms can easily learn your cloud’s historical behavior and use that information to maximize cloud efficiency. Generally, your cloud needs will ebb and flow during specific times of the day or a particular time of year. AI can analyze behavior and provide intelligence ahead of time, so you know what demand you’ll have in a specific season, or be in line with a projected spike in traffic, or forecast what infrastructure will be necessary for gradual growth without idle or wasted resources.

Let AI Be Your Not-So-Secret Weapon

According to Forbes, 63% of executives believe AI increases revenues, and 44% see how AI has reduced costs. So why not put it to work where you need it the most, tacking cloud inefficiencies?

Savvy businesses won’t put up with cloud inefficiencies any longer. That’s why innovation is happening like wildfire across cloud cost-optimization use cases, from consumption and capacity utilization, to elasticity, visibility and the reduction of wasted or idle resources. This innovation is being supercharged by artificial intelligence, saving companies from manual guesswork and over-provisioning that leads to sprawling cloud costs.

The answer might be artificial, but the savings sure won’t be. Those will just be intelligent.

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TNS owner Insight Partners is an investor in: Pragma.
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