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While downtime is among the biggest concerns for businesses, the process of scaling cloud computing may trigger several unfortunate events, including peak load failures, loss of control, and lack of visibility. These problems lead to poor performance, user dissatisfaction, and skyrocketing cloud costs, halting the business benefits of cloud modernization. Despite the challenges, achieving a scalable cloud architecture is possible and attainable. In this article, we’ll explore five principles of building scalable cloud computing, from strategic planning and financial accountability to automation and security. At the end, you’ll get access to a downloadable checklist to test your own cloud infrastructure and get a readiness score.
Scalability in cloud computing is the ability of a cloud environment to scale up and down depending on demand. Cloud providers offer numerous tools and features to help businesses meet the changing demand and optimize cloud costs. Cloud elasticity is a similar concept that is sometimes used interchangeably with cloud scalability. However, it refers to the cloud infrastructure’s ability to use fewer or more resources based on dynamic demands.
For example, if a website experiences a short spike in traffic due to a current sales promotion, cloud elasticity is key. Cloud scalability, on the other hand, isn’t about quick surges, but rather long-term steady growth.
In the very first step, the main goal is to leave the chaos behind and create a roadmap to controlled and scheduled scaling. Going through each step will help you identify potential issues, minimize or avoid them, and establish cloud cost efficiency.
To take the reins over your scalability, analyze historical traffic, campaigns, seasonality, and growth rates to forecast future demand. Proactive planning helps businesses avoid last-minute crises, focusing on scheduled and controlled expansion. You can predict potential capacity based on business input metrics like marketing plans and growth targets, as well as technical requirements such as database load.
Conduct an audit to uncover bottlenecks hampering effective scaling, including architecture weaknesses, load balancers, network issues, etc. This step is essential to calculating the future load that your systems will deal with once you scale. It’s important to note that scaling alone will not resolve all the technical issues, making audits the roadmap to discovering limits and finding a way to deal with them.
Performance monitoring and load testing are integral steps to optimizing cloud scalability. These tests are designed to measure the appropriate capacity to meet the real-world traffic. This includes the analysis of KPIs like memory and CPU to identify the minimum viable capacity, which can later be used in cloud TCO analysis. Not only does this principle prevent over-provisioning and waste, but it can also be used as a reference point for future scaling demands.
Load and performance testing is also a tool to make sure the system can handle unexpected traffic spikes. Simulate real-world behavior and establish concrete pass and fail criteria, such as error rates and network latency, to execute an effective test with meaningful results. Additionally, it’s recommended to rerun these tests after the changes to the architecture to validate the findings and avoid other potential bottlenecks hidden outside controlled cloud environments.
Another barrier on the way to cloud scalability is network and load balancer limits. Even if everything in the back end is scaled properly, faulty load balancing can create major complications during traffic spikes. You can avoid these issues by choosing the instance types with the required bandwidth and validating failover behavior.
At this point in your cloud scalability journey, your team should be able to predict increased demand and identify potential bottlenecks that pose limitations. This step allows organizations to move from uncertainty to building a controlled foundation for a sustainable cloud transformation strategy.
In the second step of the cloud scalability process, we’ll learn how to effectively react to spikes in traffic while delivering high availability, peak stability, and cost efficiency.
Different scaling types are used for various scenarios. For example, horizontal scaling is a go-to method of dealing with systems that are dependent on high traffic. Adding extra instances to distribute the increasing load aids companies in avoiding downtime. Vertical scaling helps teams get a short-term boost for predictable loads and works by adding resources to a single server. Diagonal scaling is a combination of vertical and horizontal scaling in a strategic way to work with both predictable and unpredictable loads.
The diagonal scaling approach is also known as hybrid and combines both aforementioned methods. The key is to use horizontal scaling as the foundation and add vertical scaling to performance-sensitive components. This technique helps organizations achieve high resilience and performance without overspending.
High availability (HA) is established when a system is functional and operational even during a failure. This can be achieved by relying on multi-zone redundancy and automatic failovers. Building a scalable cloud architecture is a critical process that typically introduces risks to the organization. High availability ensures that failures remain isolated events that don’t affect the system’s health.
Load balancing is another crucial part of dealing with increased demand. In addition to redistributing traffic to reduce the load, it can also isolate unhealthy back ends and prevent the cascading failure effect. In lieu of these guardrails, increased traffic can majorly affect dependencies and cause outages and downtime. Make sure your load balancers are configured correctly to deliver a smooth cloud scalability process.
Stateless services enable businesses to process requests independently from each other without storing session data. In addition to streamlining horizontal scalability, this approach also fosters fault tolerance and load balancing. In case session data is important for authentication or another application, it can still be managed in databases or in-memory caches.
Caching, when applied selectively and strategically, can help you achieve peak cloud stability by offloading back-end services from unnecessary requests. You can apply caching on the client side, server side, and CDN, and eliminate redundant requests on their way to overload the system. Begin by caching static data that isn’t subject to many changes, such as catalogs and images, to extract the most benefits. Refrain from caching dynamic data like cart contents to avoid inconsistencies.
At this checkpoint of the process, you should be able to establish high availability and stable latency. In other words, to build scalable cloud applications over time, spikes shouldn’t be unbearable emergencies but a normal part of business that your system is capable of dealing with.
The third principle is devoted to automating scaling and deployment to achieve seamless production deployments in one to five minutes. In this part, we’ll discuss auto-scaling, infrastructure as code (IaC), continuous integration and continuous delivery (CI/CD), and containerization.
The critical building blocks of a scalable cloud architecture are microservices and serverless computing. Microservices enable the individual scaling of components, instead of scaling the entire system, which wastes resources and poses potential threats to the system’s health, stability, and availability. Paired with microservice architecture, serverless computing provides cloud elasticity, allowing businesses to only pay for the resources used. These building blocks are key to running applications that smoothly scale up or down.
Auto-scaling is a technique that allows organizations to automate scaling by monitoring relevant metrics like CPU, request rate, memory usage, and others. While extremely useful, auto-scaling cannot be successful without clear limits like cooldown periods and budget thresholds in line with FinOps for cloud scalability. For more predictable traffic spikes, such as season change or major discounts, it’s recommended to rely on scheduled scaling. Combined, auto-scaling and scheduled scaling can ensure your system is responsive and stable regardless of changing workload demands.
IaC is designed to use code to enable automation, version control, and consistency, instead of traditional manual processes. Scalable cloud infrastructure can be attained with IaC practices, transforming it into testable assets that can be governed by code, which is especially valuable during cloud migration services. Using tools like Terraform and Pulumi, teams can ensure consistent performance during development and production, and the cloud migration process.
The success of building scalable cloud architecture is dependent on rapid and frequent infrastructure changes. To afford this, companies need a modern CI/CD pipeline to virtually eliminate human involvement. Small and quick changes can be performed in under five minutes, minimizing risks associated with deployments. Using fully automated CI/CD like Jenkins and CircleCI, businesses can establish safe and routine deployments.
Another crucial part of automatic scaling is containerization, a practice of standardizing how applications are run. Containerization and orchestration platforms like Kubernetes automate scaling, deployment, and management of applications, ensuring consistency and high performance through standardized cloud automation. However, it’s important to note that tools like Kubernetes are intended for complex systems, while smaller workload demands can be met with serverless platforms like AWS Lambda and Azure Functions.
At the end of the third step, the bulk of your scaling and deployments should be automated. Manual labor should be allocated to defining policies and thresholds and managing the systems.
The next step is to ensure zero-downtime deployment and fast and effective rollbacks. You can achieve this with blue/green and canary deployments, a well-crafted rollback plan, and resilience validation.
To achieve cloud scalability with zero-downtime deployments, teams can utilize three main strategies: blue/green deployment, canary deployment, and rolling updates. Blue/green deployment consists of two identical cloud environments that you can toggle between to quickly and safely release changes and rollbacks. Canary deployment offers access to a small group of users for validation and feedback, much like canary birds in the mines. Finally, rolling updates work by gradually replacing the old system with the new version in small batches.
Another way to minimize downtime is by creating, fostering, and refining a robust rollback strategy. A solid rollback playbook must include version control, automated backups, and well-tested restore procedures. In addition to a well-developed document, businesses should run drills to make sure everyone knows what their duties are during a crisis.
No matter how far you go in the security measures, there is always a possibility of something going wrong. Resilience validation is a technique that helps you evaluate how strong your system is and how many issues it can handle. Stress testing identifies the limits and degradation points and chaos engineering deliberately introduces failures to the system to see how it will deal with them. Using these methods, organizations can discover hidden dependencies and weaknesses in strategies and address them ahead of time.
Crisis mitigation is not only about technical preparedness, but also transparent communication and collaboration. Despite automation in testing, deployment, rollback, and scaling, human involvement remains integral to ensure optimal performance. Businesses that establish clear and open communication channels and foster cross-functional collaboration see more success during their cloud scalability journey.
The fourth step is complete when all the systems have been extensively tested and validated. It’s also recommended to define your own sufficiently safe deployments to standardize releases. While you can rerun tests endlessly, eventually you’ll have to deploy, and identifying performance thresholds for safe deployment is critical.
While in the previous steps we largely concentrated on technical tasks and planning, the last principle is about operational optimization, financial accountability, and data visibility. Through practices like financial operations (FinOps), centralized data monitoring platforms, and robust security measures, you can achieve cloud scalability.
Full visibility demands continuous monitoring and logging via tools like Prometheus, ELK Stack, and Datadog. These platforms offer a centralized location for metrics, logs, and traces as well as data analytics features to extract meaningful insights. You can also set up alerts to immediately react to anything that might go wrong, avoiding further complications.
Aside from continuous monitoring, businesses can also benefit from cloud performance optimization. Specifically during peaks, take a note of KPIs like latency percentiles, saturation, queue depth, and error rates to see how far the system can go. Measure metrics during and after surges to test your system’s ability to scale cloud resources.
FinOps is a method of aligning and unifying finance, technology, and operations to optimize cloud spending. By tracking and analyzing performance metrics and resource allocation patterns, teams can uncover underutilized resources that drain budget. In conjunction with auto-scaling, FinOps practices deliver financial accountability and minimize waste, enabling teams to reduce cloud spend without losing performance.
When business grows, so does the list of potential vulnerabilities and threats, forcing companies to invest more resources in security. This includes data encryption both at rest and in transit, identity and access management (IAM) focusing on the principle of least privilege, and network security like WAF and DDoS protection. Data security isn’t something that can be neglected or postponed, as the cost of a breach—including reputational damage—is always higher in the long run than the cost of implementation.
At the fifth and last checkpoint, organizations are expected to have full visibility into performance, finances, and security in real time. You should be able to look into any part of the business and technology and see what’s happening, understand the reasons behind it, and make the necessary performance improvements.
Can’t tell if you have a scalable architecture? In this section, we offer a short list of common red flags that might lead to system issues in the future. Additionally, you can access a bigger questionnaire that’ll help you calculate your readiness score and determine how scalable your cloud infrastructure is.
Building scalable cloud infrastructure is a nuanced process that could be undermined by unresolved weaknesses. Here’s a short self-check list of potential red flags to look out for that can cause serious roadblocks down the line:
If the self-assessment has opened your eyes to the gaps in your cloud scalability journey, consider downloading the Cloud Deployment Checklist for E-commerce. This checklist briefly covers the phases and helps you analyze your current infrastructure and readiness to scale cloud computing. It offers a swift yet in-depth look into your cloud architecture, deployment, testing, security, and business. At the end of the questionnaire, you’ll receive a score reflecting your level of preparedness to achieve cloud scalability.
A Well-Architected Review (WAR) is a mostly free evaluation delivered by cloud providers or certified partners like NIX, and is often the first step in cloud architecture consulting. The assessment offers a deep examination of the company’s cloud resources and workloads, measuring how secure, reliable, cost-effective, and sustainable the cloud infrastructure is. If you have experienced recurring incidents during traffic surges, uncontrolled scaling, subpar visibility, and ever-increasing cloud computing costs, you might be the perfect candidate for such an assessment.
After you request the WAR, the NIX team will produce an actionable roadmap within one working week. This will include a list of your potential risks and threats, tips to regain stability as quickly as possible, and a high-availability architecture plan. The review also addresses your security practices and improvement suggestions, a zero-downtime deployment plan, and an observability and visibility strategy.
This tailored document comes with scalable cloud architecture best practices that will:
Get in touch with NIX to receive a free Well-Architected Review in just one week, full of actionable insights crafted by industry experts.
Building a scalable cloud architecture is not just about technology—it’s about creating a foundation that can grow with your business, adapt to changing demands, and deliver reliable performance at every stage. From optimizing infrastructure and improving resilience to ensuring cost efficiency and security, every decision matters when designing for scale. With decades of experience and a large pool of cloud experts, NIX can manage your entire cloud environment, helping you achieve seamless scalability, operational efficiency, and peace of mind. Contact us to discuss your cloud capacities and how to enhance them.
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A cloud infrastructure is scalable when it can seamlessly handle an increase or decrease in computing resources, like networking or processing power, without downtime, major cost surges, and other technical and operational complications.
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Cloud computing scalability can be horizontal, vertical, diagonal, or hybrid. Horizontal scaling refers to adding more virtual machines to handle increased demand, while vertical scaling is about upgrading a single machine. Hybrid scaling is a combination of both, in which horizontal scaling deals with complex loads and vertical scaling is for predictable ones.
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Simply put, scalability refers to one’s ability to handle an increased volume or number of tasks without experiencing major issues. In cloud computing, a scalable cloud solution is one that can grow and shrink on demand without dropping performance and speed and without ballooning cloud costs.
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Companies can achieve a scalable cloud environment by planning ahead, investing in high availability and caching, enhancing automation in CI/CD and deployment, eliminating downtime, and unlocking full visibility into data and processes.
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