Many people’s experience with cloud costs is limited to the monthly $10 or so bill they get from Apple or Google. But for technology companies, which have to manage and process vast amounts of user data, it can be the second-biggest expense after payroll. Indeed, when Snap went public in 2017, filings revealed the company had more than $3 billion in cloud services contracts with Amazon Web Services and Google.
And if you thought your cell phone bill was hard to understand, try making sense of cloud charges. Companies like AWS, Azure and Google offer thousands of options, with variations that can result in some eye-popping overruns, whether it’s a startup accidentally racking up a $72,000 bill during a few hours of testing or Pinterest having to spend an extra $20 million to accommodate a bump in user demand.
In fact, it’s estimated that at least 30% — or $180 billion of the nearly $600 billion on cloud spend globally — is entirely unnecessary. The culprits can be as mundane as multiple copies of identical files or failing to clean up outdated or unused assets. Often, cloud costs are a black box altogether. In our 2020 Saas Cloud Spend survey, about one-third of the decision-makers who responded didn’t even know their company’s cloud spend as a percentage of annual recurring revenue.
Making sense of shifting cloud use across teams and contracts can seem like a game of whack-a-mole. But by focusing on three principles — visibility, accountability and automation — companies are finding ways to fight cloud spend, often saving millions and avoiding layoffs in the process.
Visibility: You can’t fix what you can’t see
The first step is to understand where cloud spend is happening. This isn’t quite as easy as it might sound. The very characteristics that make the cloud so convenient also make it difficult to track and control how much teams and individuals spend on cloud resources. Even the costs can be variable, depending on the type of service used, the resources consumed and the time of day or week.
According to the FinOps Foundation, a group focused on advancing best practices in cloud financial management, most companies still struggle to keep budgets aligned. The good news is that a new generation of dedicated tools can provide transparency. Resource tagging can automatically track which teams use cloud resources, making it possible to measure costs and identify excess capacity accurately. Meanwhile, with cloud cost anomaly detection, users can receive alerts when the meter starts ticking wildly. But visibility is only the first step to bringing costs under control.
Accountability: Put someone at the helm
Companies wouldn’t dare deploy a payroll budget without an administrator — or an entire HR department — to optimize spend carefully. Yet, when it comes to cloud costs, there’s often no one at the helm.
That’s why the second step is establishing accountability and ownership for cloud costs. Enter the emerging disciplines of FinOps or cloud operations. Increasingly, organizations are standing up these dedicated teams, whose purview can embrace everything from setting cloud budgets and negotiating favorable contracts to putting engineering discipline in place to control costs. Importantly, this isn’t an annual exercise but an ongoing commitment.
To work, these teams must be given authority to create guardrails enforced across the company. One of the reasons cloud spend spirals out of control so quickly is that teams have been insulated from the cost effects of their cloud use.
Say a developer is testing a new program or feature and has created a machine in the cloud for this purpose. It might seem easier just to keep the machine running than to power it down and restart it. But budgets suffer when developers take up that bandwidth during periods of latency. Multiplied by hundreds or thousands of users across the company, the wasteful spending quickly adds up.
Automation: The missing ingredient — AI
But even with a dedicated team monitoring cloud use and need, automation is the only way to keep up with complex and quickly evolving scenarios.
The sad truth is that much of today’s cloud cost management remains bespoke and manual, even at some of the most tech-forward companies. In many cases, a monthly report or round-up of cloud waste is among the only maintenance done — and highly paid engineers are expected to manually remove abandoned projects and initiatives to free up space. It’s the equivalent of asking someone to delete extra photos from their iPhone each month to free up extra storage.
That’s why AI and automation are critical to identify cloud waste and eliminate it.
Amazingly, the most recent FinOps Foundation survey reveals that fewer than 40% of organizations have automated reporting for cloud usage or anomalies, notifications for cost overruns, rightsizing containers or other statistics. But this is just the first step of automation. The next step is to intelligently and automatically remove the waste. I’ve seen Fortune 1000 companies reduce cloud spend by up to 40-50% by automating best practices.
For instance, tools like “intelligent auto-stopping” allow users to stop their cloud instances when not in use, much like motion sensors can turn off a light switch at the end of the workday.
Companies that rely on “spot instances” to access surplus capacity can run automation that helps them access the best rate, much like Expedia lets travelers access better deals on hotels and rental cars.
Meanwhile, even more tools are being developed to help companies model the most cost-effective service contracts or sell excess capacity on the secondary market
As cloud management evolves, companies are discovering ways to save millions, if not hundreds of millions. With next-level AI now handling the heavy lifting of identifying and eliminating cloud waste, the very backbone of the tech economy — data storage and processing — is getting a much-needed overhaul.