The Covid-19 pandemic encouraged many companies to increase their cloud plans when facing higher consumer demand for digital products and services. Given that cloud expenses aren’t charged upfront, many organizations are probably going to exceed their estimations. A recent study showed that 23% of cloud budgets overrun under regular circumstances (Flexera).
Startups and enterprises alike struggle to take control over cloud costs. How can they not when a typical cloud bill is 80-pages long and charges different resources by different metrics? By design, cloud bills show how much the team has spent – not explain why it ended up spending so much.
Is there a way out? In this article, I go over common strategies companies use to address cloud cost management and forecasting to show why AI-driven cloud optimization holds such a big promise for every team using cloud resources today.
Cost management solutions offer static recommendation
The question of cloud costs is an urgent one for both scaling startups and established enterprises looking to increase their profit margins.
Consider this: In Q1 2021, Zoom reported that their gross margin widened to 73.9% from 69.4% in the previous quarter due to the optimization of public cloud resources.
Companies use a variety of tools that allow understanding cost allocation, identifying peak usage scenarios, and discovering which teams or services contribute to the cloud bill (that keeps on increasing).
These solutions are valuable sources of knowledge. But the larger the company, the more complex and challenging it becomes for humans to reason about cloud resources and make smart cost optimization decisions.
That’s primarily because cost management, visibility, and optimization tools offer static recommendations. It’s up to team leaders or DevOps engineers to implement these insights.
And even if they do, the crucial data is still missing:
- Are all of these virtual machines really needed?
- Do they offer the best cost vs. performance ratio?
- Does the team require all of these services?
- Are there any orphaned resources in the infrastructure?
Answering all these questions is difficult. But automation can help teams do that and finally gain control over their cloud infrastructure.
Automated cost optimization in the cloud
Automation solutions that use Artificial Intelligence and machine learning can rightsize cloud resources seamlessly. They add, delete, and move virtual machines automatically in line with the changing application requirements and policies set by users.
AI-driven cloud optimization tools bring value because they can recognize patterns and act on them automatically.
Imagine an e-commerce application that experiences a lot of traffic over the course of 12 hours because of an event like Black Friday. The AI engine will recognize this pattern and automatically add machines during the busiest parts of the day, removing them when the traffic subsides.
If you use AWS, you can be sure that your cloud optimization solution equipped with AI is always on the lookout for an optimal mix of EC2 instance types. It can calculate whether it’s more cost-effective to run 100 8-core machines or 50 16-core machines or have machines equipped with ARM or Intel processors.
AI opens the doors to spot instances
Another perk of using an AI-driven solution is the opportunity to use spot instances – virtual machines that offer a 60-80% discount off the on-demand pricing. However, they come with a tradeoff since the cloud provider might reclaim them at any time, giving only a short warning.
Many organizations avoid using spot instances because moving a workload quickly enough to maintain business continuity is difficult for human DevOps. But an AI can instantly spin up another machine and even keep on looking for other available spot instances to optimize costs.
AI paves the way to dramatic cost reductions
AI-based cloud optimization tools help to reduce the complexity in managing the cloud infrastructure. As the example of Zoom shows, optimization can bring incredible cost savings.
The industry is brimming with new projects that aim to tackle the difficult questions of managing, forecasting, and optimizing cloud costs. AI technologies might be exactly what tech startups and enterprises need to make the most of the public cloud and use its scalability to drive innovation.