Cloud Cost Optimization Strategies: 6 Ways to Reduce Your Cloud Bill
Oct 12, 2023
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Сloud usage remains at the forefront of many companies’ digital transformation efforts. It maintains its prominence even amidst corporate spending reductions prompted by economic uncertainties. Yet, cloud costs can often become a budget black hole without careful oversight.
The recent State of the Cloud Report proves that companies waste around 28% of their spending on inefficient use of cloud services like overprovisioned and unused assets. These costs drain company funds that could otherwise fuel transformation, drive growth, and support critical business initiatives.
Good news: adopting cloud cost optimization strategies can help your company cut hefty bills and maximize the value of your cloud investment.
In this article, we’ll explore the most popular ways to reduce cloud expenses and share our company’s insights and tips. So, let’s dive in.
What Is Cloud Cost Optimization And Why Your Business Needs It
Cloud cost optimization is a strategic approach to managing and minimizing cloud expenses without compromising performance or productivity. It involves various techniques and practices to help your business achieve:
- Improved performance. Optimizing cloud costs ensures the appropriate allocation and effective usage of resources like computing power, storage, or other cloud services.
- Budget predictability. Effective cost control measures can help you confidently anticipate your cloud expenses, avoiding unexpected budget overruns. This, in turn, facilitates more robust strategic planning.
- Competitive advantage. Businesses that optimize cloud costs can invest more in innovation and strategic initiatives. As a result, they can respond to changing customer demands more effectively and attain a competitive edge regardless of the industry.
- Better sustainability. Efficient use of cloud resources can have a positive environmental impact by reducing energy consumption and carbon emissions associated with data centers. It helps to align with sustainability goals that many businesses are pursuing.
Yet, despite the many offerings efficient IT cost optimization brings, many companies struggle to manage their cloud spending. According to the Flexera 2023 State of the Cloud Report, a staggering 82% of organizations identify this task as a top challenge, followed by ensuring security and lack of expertise.
So, what exactly makes managing cloud spending such a daunting challenge?
The complexity and dynamic nature of cloud services and the sheer volume of data generated contribute to the difficulty of managing cloud spending. Additionally, the lack of visibility into resource usage and the continually evolving cloud pricing models can create hurdles in achieving optimal cost control.
As businesses increasingly rely on the cloud, the pressure to strike the right balance between cost containment and resource availability becomes even more pronounced. This challenge underscores the need for robust cloud cost optimization strategies and tools.
Let’s discover how they can help businesses harness the full potential of the cloud while maintaining financial prudence.
6 Main Cloud Cost Optimization Strategies
Contrary to popular belief, optimizing cloud costs means more than just identifying and eliminating waste, unused resources, and tools. It’s about helping your business achieve the right balance between performance, resources, and expenses.
Here are six main cloud cost reduction strategies to help you keep your expenses in check.
Performing cost analysis and monitoring
Effective cloud cost management begins with understanding where your money is going. This makes cost monitoring the foundation of any cloud optimization strategy. It involves tracking your cloud expenses to understand which services or resources drive the costs.
Efficient cost monitoring lets you:
- Identify cost spikes or anomalies early, preventing unexpected budget overruns
- Recognize seasonal variations or long-term cost patterns, enabling proactive cost management
- Spot underutilized resources and resize or terminate them
- Make more accurate forecasts for future cloud spending based on historical cost data and trends
Cloud providers like Azure, AWS, and Google Cloud Platform (GCP) offer native cost monitoring and management tools. They include real-time dashboards, cost breakdowns by service, and usage insights, allowing you to track expenses, set budget alerts, and manage costs within your cloud infrastructure.
You can also use third-party solutions like CloudHealth, Datadog, and Splunk, which provide advanced analytics and cost optimization recommendations. They excel when managing multi-cloud environments but also can come in handy with a single cloud usage.
The latest TechTarget research reveals that using third-party cloud cost optimization tools helps businesses achieve substantial and instant returns, with an average monthly reduction in cloud expenses of 33%.
From our experience, real-life projects may encompass hundreds of cost points, some of which aren’t explicit. For instance, creating a virtual machine incurs charges not only for computing resources, licenses, and storage but also for static IP, network traffic, firewalls, and backups. Each of them drains your cloud costs, making regular monitoring a must to avoid unexpected expenses.
Besides, many providers offer free monthly or daily usage quotas for particular services. So, some free resources in lower environments (e.g., development, testing, staging) can cost a pretty penny in production. Predicting upfront all these expenses can be quite challenging, which makes regular cost monitoring the key to efficient resource utilization.
Overprovisioning is one of the most common sources of cloud waste. It happens because many organizations allocate more computing power, storage, or network bandwidth as a precautionary measure, often surpassing their actual needs.
Adopting a right-sizing strategy can help align computing resources with your capacity requirements at the lowest possible costs. AWS states that right-sizing lets you achieve monthly bill savings of up to 70%. No surprise since it’s a pivotal mechanism to maximize performance while gaining cloud cost reduction.
Right-sizing isn’t a one-time action but an ongoing process. It involves continuous monitoring and assessment of resource utilization to do both:
- Downsize overprovisioned resources
- Upsize resources operating near or at their full capacity
To do this, you need to measure metrics like vCPU, memory, network, and disk use and set predefined benchmarks for what represents their typical performance. Additionally, you can set up alert notifications when resource consumption reaches its maximum and implement auto-scaling policies. This will help you adjust resource capacity dynamically based on real-time demand.
Right-sizing is a great cost-saving strategy that Leobit employs in every cloud-hosted project. For instance, one of our projects requires running six Virtual Machines (VMs) in production, which cost $600 per month. But we don’t need such a capacity on low environments and can use just two VMs for $200 in testing and only one VM for $70 in development. This suggestion helped our client cut the VM cost by more than half.
Yet, right-sizing isn’t just about the size of computing resources; it also reflects on the license or service plan type. It means that a well-thought-out architectural design should consider current requirements and the potential cost increase risks associated with using features from premium or enterprise packages.
For instance, let’s see how the workload and supported features influence the choice of SQL Server Edition and associated costs. For projects with small load (e.g., MVP), you can use a free Web version of SQL Server, whereas medium load projects would require from 4 to 32 CPU cores with costs ranging from $300 to $2,300/month (Standard edition).
Implementing serverless architecture
Serverless computing doesn’t mean the absence of servers but automatic infrastructure management by a cloud provider. Function as a Service (FaaS) and Container as a Service (CaaS) are the most popular serverless offerings. Yet, depending on your cloud provider, serverless may also include NoSQL storage, queues, file storage, schedulers, and other services.
The 2022 CNCF Annual Survey reports a massive uptake in FaaS adoption, rising from 30% in 2020 to a whopping 53% of companies using serverless architecture.
Significant cost savings is the top reason why companies turn to FaaS/CaaS, since with serverless, you pay only for the compute resources your code consumes during execution. This means zero idle costs as serverless functions are event-driven and automatically scale up or down without manual intervention. But that’s only the icing on the cake.
Adopting serverless architecture lets you:
- Reduce operational overhead. FaaS simplifies server management and enables developers to build simple functions that independently carry out specific tasks (like making an API call).
- Optimize costs at a granular level. Serverless lets you pay only per execution and the resources used. The recent Deloitte research demonstrates that serverless applications can yield cost savings of up to 57% when compared to server-based solutions.
- Reduce time to market. Serverless services are blazing fast to deploy. They have built-in load balancing and health checking functionality, so you can easily customize your deployment strategy, optimizing infrastructure and operational expenses.
However, serverless computing isn’t a one-size-fits-all solution for web application development. Since serverless providers bill based on the duration of code execution, running applications with lengthy processes might incur higher costs in a serverless setup than a conventional one.
From our experience, the benefits of serverless services become even more evident when you have multiple environments (e.g., PROD, UAT, TEST, DEV). Typically, lower environments have comparatively lower loads, making CaaS and FaaS offerings free of charge in those cases. And even though serverless may entail higher costs for production, the overall expenses can still be substantially lower compared to other hosting solutions.
A great example is our work for a Norwegian company, where we were in charge of redesigning an existing solution and making backend improvements. Previously, all services were hosted on Kubernetes Engine, incurring $18,000 annual costs in PROD and TEST environments. Switching to Azure Container Instances allowed our client to achieve a 90% reduction in hosting expenses.
Leveraging Spot and Reserved Instances
Most popular cloud providers offer options like Reserved Instances (Azure and AWS), Spot Instances (AWS), and Spot Virtual Machines (GCP) that can provide considerable cost savings compared to using on-demand resources.
Let’s take a closer look at what each of them gives you.
- Reversed Instances. Azure and AWS offer a significantly discounted hourly rate if you commit to a specific computing capacity for a predetermined period. When you purchase a Reserved Instance, you specify the instance type, region, and term (one or three years). You are then billed at the reduced hourly rate for the entire period, regardless of whether you use the instance.
- Spot Instances. This type of virtual machine is based on a bidding system. You specify the maximum price you are willing to pay per hour for a Spot Instance, and when the current Spot price falls below that threshold, the instance is launched. However, if the price exceeds your bid, your instance may be terminated with only a two-minute warning.
- Spot VMs (also known as Preemptible VMs). These VMs provide access to Google Cloud’s compute resources at a significantly reduced cost, but they can be terminated by GCP at any time with short notice (typically 30 seconds).
Here’s a comparison table to help you understand the key differences between these three cloud cost-saving options:
The good news is that you can combine different instance types that your cloud provider offers to optimize costs while maintaining performance and reliability. Automation can play a pivotal role here by monitoring the availability and pricing of Spot and Reserved Instances and automatically switching to them when they offer cost savings.
Based on Leobit’s experience, Spot Instances are perfectly suitable for processing tasks when using serverless is limited by long cold start times or specific software requirements.
As for Reserved Instances, they can become obsolete in fast-evolving projects for several reasons, including the transition to serverless architecture, adopting third-party solutions, or the service initially hosted on the VM becoming outdated. To avoid such situations, the usage of Reserved Instances should always align with a company’s long-term architectural vision.
And here is the good news: you can always repurpose a VM so it will still remain beneficial for the project.
Don’t know how to approach your cloud cost optimization right? Our team has certified cloud experts who can help you bring these strategies to life and optimize your software performance.
Don’t know how to approach your cloud cost optimization right? Our team has certified cloud experts who can help you bring these strategies to life and optimize your software performance.
Adopting automation and DevOps practices
Automation and DevOps are tightly interrelated, with automation being a core enabler of DevOps practices. Both go hand in hand in cloud spend optimization, empowering companies to reduce waste and manual errors, enhance agility, and ensure that resources align with actual workload requirements.
Here are the ways to achieve this:
- Infrastructure as Code (IaC). Tools like Terraform, Azure Resource Manager, and AWS CloudFormation can help automate the provisioning and management of cloud resources. Thanks to it, you can ensure efficient resource allocation and reduce the risk of overprovisioning and waste.
- Scheduled start/stop. Automation scripts or tools can be set up to start and stop non-production resources (e.g., development and testing environments) outside of working hours. This prevents idle resource costs.
- Orphaned resource identification. Automation tools can identify and flag orphaned resources that are no longer in use, prompting teams to remove them to avoid ongoing costs.
- Automated resource lifecycle management. Setting up scripts or using cloud-native automation tools can help you start, stop, or terminate resources that are no longer needed without any manual intervention. This helps you manage your cloud resources more efficiently and prevent ongoing charges for unused assets.
In addition, in some projects, it might be relevant to use Platform as a Service (PaaS) to build, deploy, and manage applications without worrying about underlying infrastructure details. PaaS offerings often include fully or partially managed services like SQL Server managed instances, which take over most database management functions such as scaling, patching, backups, and monitoring. This allows developers to focus on building and deploying applications rather than managing infrastructure.
It’s essential to align these practices with your organization’s specific goals and continuously monitor and optimize them as your cloud infrastructure evolves.
A great example of how automation can help cut cloud expenses is our work on a CRM system for one of our clients. We used SQL Server Reporting Services (SSRS) to generate weekly reports every Monday at midnight. A set of three Windows VMs took two minutes to start, performed the job in three hours, and remained idle until the next Monday.
So, our experts created a script that launched the machines half an hour before midnight and shut them down once the work was completed. This approach allowed our client to achieve considerable cost reductions, including an annual savings of $3,000 on computing resources.
A similar mechanism can also work well when DevOps infrastructure requires a dedicated build machine with specific firewall rules. When deployments to production occur only once a month, keeping it constantly running can be costly.
Using cloud-native architecture
Cloud native entails building optimized applications for the capabilities of a public, a private, or a hybrid cloud. This approach is quickly revving up since it allows to considerably reduce the following expenses:
- Infrastructure costs by leveraging cloud-native services such as serverless computing
- Operational costs by automating tasks such as scaling, monitoring, and logging
- Development costs by enabling faster development cycles through the use of microservices and containerization
Cloud-native architecture is often more cost effective than traditional services on virtual machines or even Kubernetes. And there is a good reason for that: cloud native reduces the need for dedicated infrastructure and resource usage optimization.
Thanks to the key components of cloud architecture, organizations can swiftly and efficiently:
- Adapt to changing demands
- Achieve a higher level of scalability and resilience
- Optimize resources based on usage patterns to reduce costs
The 2022 Cloud Native Computing Foundation (CNCF) annual survey reveals that many companies are on the rise to adopting cloud-native technologies. Yet, most are still in the early stages, with only 30% of organizations having already adopted a cloud-native approach across nearly all development and deployment activities.
It’s also worth noting that cloud platforms are rapidly evolving, with new updates emerging almost daily (check out Azure, AWS, and GCP changelogs). Cloud-native services regularly receive new features that could previously require substantial investments in custom development. So, it is crucial to collaborate with cloud experts who are well-versed in the evolving portfolios of cloud providers.
Modern cloud platforms offer ready-made, feature-rich native solutions with a high level of customization. Such native solutions are often much more efficient and usually cost less than potential bundles of servers and licensed software. For instance, in Leobit’s projects, adopting cloud native led to monthly cloud cost reductions ranging from $300 for simple systems like schedulers to over $2,000 for compute-intensive tasks like data processing.
While such migrations can be resource-intensive (due to development activities), in many cases, they lead to long-term cost reductions, with the payback period ranging from several months to a few years. Such migrations and the associated risks are inevitable for legacy systems. The good thing is that you can avoid them in new projects by correctly setting up the architecture from the start.
Connecting the Dots
Cloud cost optimization is a strategic initiative that can drive significant savings while maintaining or even enhancing your software performance. Adopting the strategies we outlined in this article can help your company gain better visibility into your cloud expenses and align resources with your business needs.
Every project is unique, which makes no one-size-fits-all approach to tackle cost optimization. While cost monitoring and automation can benefit almost any project, strategies like serverless architecture, cloud-native computing, and others might not work in your particular settings and only drain costs down.
Partnering with an experienced software development company like Leobit can take this burden from your shoulders. Our company is honored to be a Microsoft Solution Partner for Digital & App Innovation and has certified cloud experts with profound skills in AWS, GCP, and Azure cloud development.
Contact us, and our team will help you choose the right strategies to get started on your cloud cost optimization journey.