Reliability vs Availability: A complete guide to system performance metrics

In an always-digital world where users expect reliable services, businesses must measure two critical metrics: reliability and availability. However, reliability and availability are terms often used interchangeably but understanding the difference is crucial when building systems that users can trust and depend on. Both metrics are vital, but depending on your use case, you might prioritize one over the other.

Take the 2017 AWS S3 outage. Services stayed online, but a single mistyped command disrupted thousands of companies. The system was technically available, but critical functions broke down. This is a stark reminder that availability without reliability is a hollow win.

In this guide, we’ll break down these key metrics, find out how to calculate them, and discuss best strategies to improve both metrics.

Understanding reliability and availability in system design

To create systems that are resilient and are trusted by users, it is important to first understand the difference between reliability and availability. Let’s try to understand through a quick-to-digest definition followed by a short example:

What is reliability? 

Reliability refers to a system’s ability to function correctly over time without unexpected failures, ensuring consistent performance. It is measured using a key metric called Mean Time Between Failures (MTBF), which calculates the average time a system operates before a failure occurs. A higher MTBF indicates fewer disruptions.

• Example: A database server designed for reliability should handle requests seamlessly for weeks, if not months, without requiring intervention.

What is availability?

Availability refers to how often a system is operational and accessible, focusing on the uptime and accessibility of your services. It is typically measured using the uptime percentage. For example, 99.9% availability translates to approximately 8.76 hours of annual downtime.

• Example: A streaming service may stay online 99.99% of the time, but buffering issues during prime time degrade user experience.

The difference between both metrics

Both metrics are critical, but the priority depends on the system's purpose. For example:

• High availability priority: E-commerce platforms, banking services.
• High reliability priority: Medical equipment, industrial control systems.

Understanding these definitions and their unique roles is the first step in building systems that excel in both areas. In the next section, we’ll explore real-world examples to help clarify how reliability and availability play out in different scenarios.

Real-world examples of reliability and availability

To understand how reliability and availability work in practice, let’s examine how different industries prioritize these metrics.

Example 1: High availability in e-commerce

Imagine you run an e-commerce business that handles millions of transactions daily, where users expect every action to perform without fail. The platform must be accessible, especially during peak events like Black Friday.

Availability is the priority here for the system is to maintain high uptime, often targeting the “five-nines” of availability. The challenge lies in downtime, as even a few minutes of downtime can result in lost revenue, a pile of abandoned carts, and negative reviews showing up on social media.

The solution involves implementing redundant systems, failover mechanisms, and servers that are well-distributed geographically to ensure users can always access the platform. 

In this case, availability trumps reliability. Even if small glitches occur (e.g., a product image doesn’t load immediately), users can still complete their purchases.

Example 2: High reliability in aviation systems

Now, consider the software managing an aircraft’s navigation systems. Here, reliability is non-negotiable. A failure during operation could have catastrophic consequences.

Reliability is the priority here as the system must operate without any fail and the challenge is that even a single failure could compromise safety. A mission-critical system like this required rigorous testing, high quality hardware, and redundancy to ensure systems function as intended every single time.

Unlike e-commerce, reliability is paramount here. Availability without reliability (e.g., the system is accessible but frequently crashes) could lead to disaster.

Example 3: Balancing reliability and availability in cloud services

Cloud platforms like AWS or Azure often need to balance both reliability and availability. For example, their compute and storage services must be both highly available and reliable to meet user demands.

• Dual Priority: Systems must minimize both downtime and the likelihood of failure during operation.
• Challenge: Ensuring uptime during maintenance or unexpected failures while maintaining service quality.

These platforms use multi-zone deployments, robust monitoring, and self-healing infrastructure to balance reliability and availability effectively.

Metrics and formulas for measuring reliability and availability

Now that we have explored multiple real-life examples of understanding the impact of these key metrics, let’s learn how reliability and availability are measured:

Measuring reliability

To define reliability through numbers, we take two metrics:

1. Mean time between failures (MTBF)

MTBF is a critical metric for understanding how long a system operates before experiencing a failure. It’s commonly used for systems where reliability is paramount.

Formula: MTBF = Total Operational Time / Number of Failures

Example: A server running 10,000 hours with 5 failures has an MTBF of 2,000 hours.

A high MTBF indicates a reliable system with fewer interruptions.

2. Mean time to repair (MTTR)

MTTR measures the average time it takes to repair a system after a failure. It’s particularly important for minimizing downtime and improving availability.

Formula: Formula: Availability = (Uptime / Total Time) × 100

Example: Example: 10 hours of downtime across 5 failures = MTTR of 2 hours.

Lower MTTR ensures faster recovery from incidents.

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Measuring availability

To measure availability, we consider only one metric:

Uptime percentage

Formula: Availability = (Uptime / Total Time) × 100

Example: 8,756 hours uptime / 8,760 total hours = 99.95% system availability.

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Best practices to improve system reliability and availability

Building systems that users can depend on requires strategies that anticipate failure and prioritize seamless operation. Here’s how teams across industries tackle reliability and availability challenges, with lessons learned from real-world engineering.

Use redundant systems to prevent failures

By deploying backup servers, mirrored databases, and parallel networks, teams create fallbacks that kick in automatically during outages. Load balancers act as traffic cops, directing users away from overwhelmed nodes. Storing copies of data across multiple locations, like cloud providers do with multi-region architectures, ensures that even if one data center goes dark, services stay online.

Run regular tests to find weaknesses

Load testing pushes systems to their limits, mimicking Black Friday traffic to see how they hold up. Chaos engineering takes this further by pulling the plug on a server mid-operation to see if backups take over smoothly. Teams like Netflix run these experiments daily (they even named their tool Chaos Monkey) to harden systems against real-world surprises. Regular failover drills ensure backups are battle-tested.

Monitor performance and set up alerts

You can’t fix what you can’t see. Real-time monitoring tracks metrics like uptime and error rates, acting as an early warning system. Tools like Prometheus and Grafana turn raw data into dashboards, highlighting trends like rising latency or mysterious error spikes. Automated alerts ping engineers the moment something’s off, often before users notice. Centralized logs, aggregated with platforms like the ELK Stack turn debugging into a targeted investigation.

Automate recovery for faster incident response

When every second counts, automation is the difference between a hiccup and a crisis. Kubernetes, for instance, automatically restarts failed containers, while cloud services like AWS scale resources during traffic surges without human input. Pre-built runbooks act as playbooks for engineers, guiding them through fixes step-by-step. The result? Systems that heal themselves while teams focus on bigger fires.

Learn from incidents and improve continuously

Every outage holds a lesson. Postmortems dissect what went wrong, turning root causes into action items like patching flawed code or tightening configs. Teams at companies like Google document these findings religiously, using them to prevent repeat failures. 

Over time, this cycle of failure → analysis → improvement builds systems that grow more resilient with every incident.

Keep system architecture simple and scalable

Fancy architectures often backfire. Microservices might be trendy, but they introduce dependencies that can cascade into outages. A well-designed monolithic system, while less glamorous, can outperform a fragile web of services. The trick is to streamline: cut unnecessary dependencies, design modules that can be updated independently, and prioritize scalability. Complexity is a liability.

Tired of playing whack-a-mole with outages?

Nobody likes spending staring at dashboards or deciphering alert fatigue. We know it and that’s why Zenduty works as a co-pilot for your engineers, guiding them through major incidents towards clarity. 

Stop settling for high MTTAs and MTTRs and start your journey with Zenduty today!

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