What is Cloud Cost Management?

As many companies have had to undergo drastic cost-cutting measures this year, the importance of reviewing, monitoring, and managing variable expenses has never been more critical.

In the digital age, one such variable expense that nearly every company incurs is its cloud costs. Similar to other business metrics, cloud costs are notoriously difficult to monitor using traditional tools due to their inherent complexity.

This guide explores what cloud cost management is, tools to help you with cloud cost management, how machine learning can drive this initiative, as well as how Anodot is using its own ML-based business monitoring to cut a projected $360K in annual cloud costs.

What is Cloud Cost Management?

Also known as cloud cost optimization, cloud cost management helps your company understand what actually goes into your cloud costs – which then helps you optimize your spend and ensure that you are operating at optimal efficiency.

Cloud cost management is useful no matter what stage in the cloud process you’re in, be that if you’re mid-migration, settled on a hybrid environment, or dedicated to a fully cloud stack.

Some common costs revealed by cloud cost management tools include:

-Memory
-Storage
-Network traffic
-Web services
-Training and support
-VM instances-Software licenses

Cloud Cost Management Challenges

As you may know, managing and optimizing cloud costs have become increasingly complex due to the growing number of services, regions, and instance types.

What makes managing cloud costs even more challenging is that traditional tools offered by cloud providers typically delay reporting on costs. If your cloud environment glitches, the resulting surprise expenses can significantly impact the bottom line.

In addition to this data lag, cloud costs are challenging to monitor and manage for three main reasons:

Context: Each metric that makes up cloud expenses derives significance from its own context, which means it cannot be evaluated in absolute terms.

Topology: The business topology of cloud costs is also unknown, which makes the relationships and correlations between metrics highly complex and dynamic.

Volatility: Finally, cloud-based metrics typically have irregular sampling, which means there may be delays of several minutes or hours between metrics being used at their normal capacity.

To deal with the challenges of cloud cost management, machine learning offers the ability to deal with these complex metrics and their dynamic behavior. Before we get into how AI and cloud cost management should be your best friends, let’s quickly look at how you can add cloud cost management software to your toolstack. 

Check out these tips for maximizing cloud ROI

What is a Cloud Cost Management Tool?

Wondering how to get started with cloud cost management? First, you’ll need a cloud cost management tool.

Cloud cost management tools like Anodot are the best way to keep your cloud data in one place. Ideal for improving your organization’s understanding of the cloud, these tools are designed to optimize cloud spend and improve visibility with easy to understand dashboards and low learning curves.

How to Choose a Cloud Cost Management Tool

For all of the benefits of cloud optimization tools, it can be a challenge to find the best fit for your company. Don’t worry though – you’re in good hands. We’re something of cloud cost management tool experts!

Here’s the top five things you should keep in mind when auditing the best software for your tech stack:

–Scalability. Your tool should grow with you as you scale your cloud toolset and complete a full migration, or, inversely, your chosen tool should be able to match you if you pull back on cloud offerings.
-Automation. Part of the appeal of a cloud cost management tool is that you don’t have to worry about menial cloud tasks anymore – all thanks to automation. For example, you should get alerts if spending in certain areas spikes or dips, or budget limits are exceeded.
-Cloud monitoring. Your chosen tool should give you real-time monitoring data and alerts on spend and cloud data.
-Integration flexibility. A good tool should be able to integrate with your cloud provider of choice and any additional tools you might be using, ensuring that you have full visibility into cloud spend across all platforms.
Compliance and security. Your chosen tool should keep you up to date on industry-specific changes to compliance, like HIPAA. It should also ensure your cloud data is kept safe and secure.

Cloud Cost Management Strategies

Avoid overspending in the cloud by implementing a strong cloud cost management strategy. Get an idea of common strategies that you can use below:

Strategies to improve organization

Need to get your reporting skills in order? There’s no better tool to use than cloud management software. You can build organizational strategies to improve:
-Regular reporting and reviews of cloud consumption.
-Regular budget reviews to determine how much budget is used and where.
-Regular reviews to ensure compliance is being properly upheld.

Cloud spend optimization strategies

Get your cloud spend in order by trying one (or all, if you have the bandwidth) of the following:
-Right sizing your cloud services to fit the immediate need of your organization.
-Power schedule cloud usage since resources aren’t typically needed all hours of the day, all days of the week.
-Auto-scale resources up or down (or even turn resources off) as needed.

Cloud Cost Monitoring & AI

As described in this article, there are three layers to AI-based cloud cost monitoring:

Cost monitoring: Algorithms are used to track cloud costs at each individual service, region, team, and instance type. This means that when anomalies do occur, you can dive into the dimensions associated with the root cause.

Usage monitoring: Next, cloud usage is monitored on an hourly basis. As mentioned, traditional cloud cost monitoring and management tools often have a data lag of 8 to 48 hours, which means you can be more proactive in preventing runaway usage and costs.

Cost forecasting: Finally, since machine learning can monitor each individual metric and learn new normal behavior as it evolves, this allows the solution to provide more accurate cost forecasts and ultimately improved budget planning and resource allocation.

Now that we’ve discussed the three layers of monitoring, let’s review how Anodot’s R&D team uses their cloud cost management solution to cut a projected $360K from the company’s monthly bill.

Case Study: Using Autonomous Business Monitoring to Optimize Cloud Costs

As the global economy witnessed drastic changes for nearly every business, companies have had to go into what investor Elad Gil has coined as “Startup Offense and Defense in the Recession”. In other words, we’ve had to do everything we can to improve on the sales side and also cut costs wherever possible.

As discussed, one of the largest variable expenses that companies have today is their cloud costs. With this in mind, we chose to make optimizing and reducing cloud costs one of the pillars of our defensive strategy. In particular, we had the following three objectives in mind:

The process of optimizing cloud costs needed to take less than one month with three full-time engineers working on it.
The new system had to fit within our existing code base without any major changes required.
The change needed to persist over the long run, so that our expenses would continue to be reduced in the following months.

With these objectives clearly laid out, three of our engineers set out to solve this challenge by making changes in four key verticals:

Tag AWS Resources: The first step was to pinpoint exactly what costs every AWS service was incurring by tagging each resource with the associated instance type, components, and processes. This allowed us to go through each resource and determine where we can store, compress, and process data more efficiently.

Minimize Workloads in R&D: After pinpointing costs with each resource, we realized that we could optimize our workload size in the development cycle, using Feature Branches so that each branch has its own environment. This allowed us to minimize the operation size and remove non-essential services from development clusters, which ultimately led to a roughly 50 percent reduction in cost savings in this particular cycle.

Efficient Cloud Usage Planning: Another benefit of tagging AWS resources is that we were able to predict usage more efficiently. This not only increased our financial planning capabilities but also allowed us to reserve and switch between instance types more efficiently.

Cloud Cost Management in Real Time: Finally, since we knew we wanted these cloud cost savings to persist in the coming months we implemented a cost management solution independently monitored by machine learning. As mentioned, this gave us hourly updates on cloud usage instead of the previous 8- to 48-hour delay we had previously. Additionally, real-time alerts on anomalous cloud usage contained related instances and influencing events. Providing this correlation analysis, enabled our engineers to zero in on the root cause and remediate incidents much faster.

By tackling cloud costs in these four verticals we were able to resolve any incidents before runaway costs impacted our bottom line.

What’s the Future of Cloud Cost Management

When looking to better manage operational costs, cloud usage is an area ripe for optimization. To initiate this effort, teams should consider implementing machine learning for real-time cost management, usage monitoring, and cost forecasting. What’s more, ML-based solutions offer the granularity, speed, and accuracy to detect and resolve incidents before any serious financial damage has been done.

In order to adopt a more proactive cost monitoring and management strategy this year, Anodot’s R&D team was able to use our own technology to cut $360k from our annual expenses. In particular, Anodot accomplished this by tagging cloud resources, reducing R&D workload sizes, planning for usage more efficiently, and manage our cloud costs in real time.