According to Gartner, global spending on public cloud services will increase by a significant 20.4% by 2024, with much of the cost directly related to increased usage. This will come as no surprise to anyone managing an IT budget who finds themselves spending more money on keeping cloud services online.
However, they are not without solutions. The major players are all introducing cloud pricing calculators, while FinOps is gaining traction among those managing runaway costs. But here’s the problem: while cloud calculators and FinOps can be useful in addressing the infrastructure, they don’t go far enough in addressing the applications. As a result, IT teams using these tools are still unable to focus on the main driver of cloud costs: inefficient code.
A simple way to look at it is that the cloud is an extension of your code, and because inefficient code in the cloud causes you to spend large amounts of money right away, turning the tide should be a priority.
Senior Vice President Product Management and Enablement at Hitachi Vantara.
Cloud changes the planning cycle
For on-premise infrastructure configurations, managing usage usually means looking at the code that resides directly on the server. The more data and features you add, the faster you’ll reach around 70-80% utilization – the point at which many IT managers start thinking about adding more server capacity.
However, purchasing, setting up and connecting a server in a local data center can take up to six months. It’s not an overnight job. So in the meantime, teams can try to make adjustments to existing servers to reduce usage, improve performance, and give them some breathing room until the new hardware is online.
When deploying code to the cloud, things are very different. That’s because the cloud’s ability to auto-scale means you’ll never reach the 70 or 80% capacity level. And because the meticulous level of expansion planning isn’t required in the same way as in an on-premises situation, the impetus to tweak and improve your code to gain more leeway isn’t as great.
While you may have worked hard to regain 20-30% of capacity on-premise, that procedure never happens in the cloud. As such, your bill for cloud services continues to increase.
The case for cost SLOs
All of this requires us to rethink the way we approach the cloud, code and costs. The common misconception is that the cloud is infrastructure, when in fact it is code. And not enough developers think about how much their code costs in production. Typically, they prioritize the parameters that exist in service level objectives (SLOs), such as CPU and memory consumption, latency, and response times. Typically, costs are not considered an SLO. I would say that needs to change.
Establishing costs as an outcome from the very beginning sets the tone for optimizations that make cloud costs more affordable – while also unlocking other benefits. Every implementation involves costs, but what if these costs could be minimized? For example, let’s say an online transaction typically takes two seconds to complete. If you could introduce a line of code that could reduce those two seconds to 500 milliseconds, you could save 75% while improving customer satisfaction.
It’s a phenomenon called cost-conscious coding that couldn’t come to the fore at a better time. As cloud costs rise, AI becomes increasingly capable, allowing developers to create code faster than before. According to McKinsey, AI can halve the time it takes to write new code. Therefore, it will be possible to become more efficient and cost-effective with code exposed in the cloud, while the hours people spend creating the code will be significantly reduced.
Make cost-conscious coding meaningful
To manage your costs as an SLO, the best place to start is by defining, measuring, calibrating, and recalibrating them. What do you want the parameters to be and how will you monitor progress? Cost-conscious coding is an iterative process, so you’ll continually discover new ways to improve performance.
That said, it’s also worth noting that your returns will eventually stabilize. There will come a point when the law of diminishing returns kicks in, and results will also vary from company to company. Cost-conscious coding must therefore be aware of the effort put into it and the expected results. The results must always justify the initial effort, otherwise it will be a wasted exercise, no matter how much AI helped write the code.
If you’ve noticed that your cloud costs have skyrocketed lately, cost-conscious coding is definitely worth investigating. It fits well within the broader philosophy of setting costs as an SLO, and if there is such an expectation, developers are much more likely to adopt it. Even if your costs don’t increase now, chances are they will at some point as you plan to grow. Cost-conscious coding is an activity that prepares for the future and may use emerging technologies.
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