Building a system takes more than just the development effort; maintaining and covering features takes much more. There is scalability, maintainability, availability, usability. The list goes on and on. With so many things to focus on, how do you keep the platform up and running? A better question might be - what do these even have in common? How do these add value to our systems? Each of these priorities bring value to the table. This includes service reliability alternatively referred to as SR. Yet, the question is how does it add value?
At its core, reliability is about delivering a usable and dependable product for the user. Why focus on the user? Because reliability is all about their experience with your service. If your metrics and measurements don’t account for the user’s perspective, you are missing the point of what reliability truly means.
But why is the customer experience so tied to reliability? Imagine your service has an impressive 99.999% uptime, but customers are dealing with inconsistent performance. In that case, your service is not reliable. On the flip side, if a significant portion of your back-end services are down, but customers don’t notice because of effective mitigations, your service is still reliable.
To dig deeper, we will break reliability into smaller, more manageable pieces, and we will draw on how reliability is defined in manufacturing and engineering, using quality metrics as a guide.
According to the American Society for Quality, reliability is the likelihood that a service will perform its intended function over a specific period. This includes several key components such as probability of success, durability, dependability, availability to perform a function, and quality over time. Let me give you more details about this.
Building Blocks of Service Reliability: What Makes It "Good"!
- Probability of success
This is about the ratio of successful requests to unsuccessful ones. For example, in a back-end REST service, a request is considered unsuccessful if it results in server errors, such as HTTP 500 status codes. But what about client errors like 400 (Bad Request) or 401 (Unauthorised)? In a healthy system, these errors should not typically count against the service’s success metrics since they result from incorrect client requests rather than issues with the back-end itself. However, this assumes that the client is functioning correctly and not introducing errors due to defects in its implementation—such as a faulty UI incorrectly invoking the API. Here, we focus on measuring the reliability of the back-end REST service as an isolated component of the system, separate from potential client-side issues.
2. Durability
This means how well your system can recover when things go wrong. If a database fails, can the user pick up where they left off, or is the data corrupted? Does it require a manual cleanup? If an instance of the system goes down, can traffic automatically reroute to another instance? Without a second doubt, fail instances happen in software and durability ensures that your service can handle them gracefully.
3. Dependability
A dependable product or service works the way customers expect it to. It delivers the value you’ve promised and meets your service-level agreements (SLAs). While this isn’t always easy to measure directly, customer feedback, reviews, and quality indexes can help. If customers are leaving your product over time, it might be a sign that your service isn’t dependable enough.
4. Availability
Availability, or uptime, is a key part of reliability—but it’s not the whole story. People often confuse the two, thinking they’re the same. However, a service can have high availability but still lack reliability if, for example, it has a low probability of success. Even if only a small percentage of customers are affected by downtime, their experience still matters.
If redundancies or mitigations are in place to minimise the impact of availability issues, reliability can remain strong. For instance, if a service is down in one region or operating at half capacity, but 99% of customer transactions still go through, the overall reliability might still meet expectations.
In software, availability is often measured in “nines.” You might hear terms like “three nines” (99.9%) or “five nines” (99.999%) over a month. Availability is calculated by dividing uptime by the total of uptime and downtime, usually measured over a consistent period like a month or quarter. Keep in mind that not every service needs the same availability target—it depends on its specific use case.
5. Quality Over Time
Finally, quality over time helps us understand how reliability and the customer experience evolve. Are things getting better or worse? Are there underlying issues causing these changes? How can we improve or maintain stability? This varies from service to service, but even slow changes in metrics over time can signal areas that need attention.
How Scaling Teams Are Solving the Reliability Puzzle
As teams grow, keeping everything running smoothly gets more complicated. To handle this, companies are moving away from just putting out fires when things go wrong and instead focusing on building reliability into their processes from the start. By using tools like Platform Engineering and Internal Developer Platforms (IDPs), teams can create standardised infrastructure, automate repetitive tasks, and cut down on operational headaches. This makes it easier to deploy updates consistently, even as the team scales.
Advanced observability tools and AI-powered monitoring are also changing the game. They help teams catch potential issues before they turn into major outages, giving them a chance to fix things proactively. Pair that with automated CI/CD pipelines, and you’ve got a system that reduces human error and ensures deployments are stable and repeatable. And let’s not forget Chaos Engineering—it’s like a stress test for your systems, helping you find weak spots and make your architecture more resilient before real users ever notice a problem.
To make sure reliability aligns with business goals, many teams are turning to Service Level Objectives (SLOs). These set clear performance targets so everyone knows what “good” looks like. Add in self-healing infrastructure, which can automatically fix issues and reduce downtime, and you’ve got a recipe for scaling efficiently without sacrificing reliability. By weaving these strategies together, teams can keep systems up and running, boost developer productivity, and deliver a better experience for users—no matter how big they grow.
What strategies have companies embraced to keep SR?
- Going Agile
Agile methodologies like Scrum, Kanban, and Lean are essential for maintaining service reliability while scaling teams. By breaking projects into smaller, iterative steps, teams can deliver services incrementally, test outcomes, and adapt quickly to changing demands. This approach ensures that as teams grow, they remain flexible and responsive to customer needs.
For example, cross-functional Agile teams can scale effectively by distributing responsibilities and maintaining clear communication channels. Regular sprint reviews and retrospectives help identify bottlenecks early, ensuring that service reliability doesn’t suffer as the team expands. Agile also fosters a culture of continuous improvement, empowering team members to take ownership and innovate, which is critical for scaling without compromising quality. Agile can also help deliver a product in a way that helps it mature.
- Focusing on Service Design
Service design ensures that customer-centricity remains at the core of service delivery, even as teams grow. By mapping customer journeys and identifying key touchpoints, teams can design scalable processes that maintain reliability. Tools like customer personas, journey maps, and service blueprints help teams align their efforts, even when new members join or responsibilities shift.
As teams scale, service design provides a clear framework for onboarding new members and ensuring consistency in service delivery. By understanding customer pain points and expectations, teams can prioritise improvements that enhance reliability, regardless of their size. This structured approach prevents service quality from degrading as the organisation grows.
- Using Automation and Analytics
Automation and analytics are game-changers for maintaining service reliability during team scaling. Automation handles repetitive tasks, reduces human error, and frees up team members to focus on higher-value work. This is especially important as teams grow, ensuring that service delivery remains consistent and efficient.
Analytics, on the other hand, provides actionable insights into performance trends, helping teams predict and address issues before they escalate. For example, predictive analytics and AI can analyse historical data to forecast potential service disruptions, enabling teams to proactively mitigate risks. As teams scale, these tools provide the data-driven foundation needed to maintain reliability without overburdening team members.
Quality assurance is another aspect of service reliability, and thus, it can be used to mitigate the risk of reliability concerns. Tools such as Chaos Mesh can be used to test the fault tolerance and recoverability of infrastructure. Further, the availability of components such as API can be measured and approximated using performance testing tools.
- Supporting and Empowering Teams
Scaling teams successfully requires empowering employees to take ownership of service reliability. This means involving team members in decision-making, providing them with the right tools and training, and encouraging a culture of collaboration and innovation.
When teams feel supported and valued, they are more likely to adapt to scaling challenges and maintain high service standards. For instance, cross-training team members ensure that knowledge is shared, reducing dependencies on specific individuals and making the team more resilient as it grows. Empowered teams are better equipped to handle increased workloads and complexity without compromising reliability.
- Keeping Stakeholders in the Loop
Stakeholder engagement is critical for maintaining service reliability during team scaling. Keeping customers, employees, suppliers, and partners informed and involved ensures alignment and builds trust. Transparent communication about changes, risks, and benefits helps stakeholders understand how scaling efforts will impact service delivery.
For example, involving stakeholders in feedback loops ensures that their needs are met as teams grow. This collaborative approach minimises misunderstandings and ensures that scaling efforts are aligned with stakeholder expectations. By fostering strong relationships, businesses can scale their teams while maintaining the reliability and quality of their services.
Service Reliability and SkyU
A reliable internal developer platform (IDP) is a game-changer for productivity and trust. Without it, even the most advanced applications can end up causing more headaches than they solve. Service reliability (SR) and IDPs are like two sides of the same coin—they work together to create smooth workflows, encourage team adoption, and speed up innovation.
At SkyU, our in-house IDP is built with reliability as its foundation. We rely on Kubernetes to power our deployment strategy, and its self-healing capabilities are a lifesaver when it comes to minimising downtime. Our deployment pipeline is carefully structured, moving code from development to production with rigorous testing and automated deployments. This not only cuts down on errors but also keeps things stable and predictable.
To keep things secure and running smoothly, we’ve baked in automated checks like vulnerability scanning and static code analysis. SkyU agents keep an eye on critical cluster metrics, sending alerts the moment something seems off so we can tackle issues before they escalate. Health probes are constantly checking to make sure services are up and running, while firewalls and API gateways act as our first line of defence against malicious traffic.
By combining proactive monitoring, automated recovery, and tight security controls, SkyU has built an infrastructure that’s not just robust but also highly reliable. It’s all about giving our teams the confidence to innovate without worrying about the platform letting them down.
To Wrap Up
Making sure services stay reliable as you scale isn’t just about throwing technology at the problem—it’s about finding the right balance between automation, security, and keeping a close eye on things. When teams start using internal developer platforms, it’s crucial to focus on standardisation, resilience, and observability. These are the building blocks for trust and efficiency.
By investing in things like self-healing infrastructure, well-structured deployment processes, and real-time monitoring, you’re not just making your systems more reliable—you’re also giving developers the freedom to innovate without constantly worrying about things breaking. At the end of the day, reliability isn’t just about keeping the lights on; it’s about creating an environment where teams can consistently deliver value, sustainably and without unnecessary stress.
