Reliability vs availability: Understanding the differences

Key takeaways

• Reliability is the probability that a system or component will perform its intended function without failure, under specified conditions, and for a given period of time.

• Reliability calculations use mathematical models and statistical techniques to estimate the reliability of a system or component.

• Mean Time to Failure (MTTF) and Mean Time Between Failures (MTBF) are important metrics for reliability calculations.

• Availability is the probability that a system or component is operational and available for use at a given time.

• Reliability calculations can help to minimize downtime, reduce maintenance costs, and improve overall system performance.

Today's customers increasingly expect businesses to deliver always-on service. However, even the most well-resourced companies can experience failures and outages. Two distinct metrics, reliability and availability, can help measure success and make improvements.

Reliability, or system readiness, measures performance at specific intervals against defined performance standards. Availability, or system function, measures the percentage of time the system is operable. Together, they offer insights into the health of business systems and help identify areas for improvement.

This guide discusses service reliability vs. availability, how incident management metrics help measure them, and how to improve these key metrics.   

What is system reliability?

Reliability is the probability that a system or component consistently performs its intended function without failure over a specified period. Teams must understand how to measure and ensure reliability to make informed decisions about system performance and enhance customer satisfaction. 

For instance, payroll systems must reliably process direct deposits within a set timeframe each month, while cold storage systems must detect power outages and switch to backup generators without fail. Across industries, maintaining reliability in automated processes and tracking performance through incident management KPIs is crucial, as failures can lead to significant financial repercussions.

Reliability is critical to system design and maintenance because it directly affects performance, safety, and cost-effectiveness. Reliable systems operate correctly and consistently, helping organizations maintain customer confidence and operational efficiency.

How to measure and calculate failure rates for reliability

You can measure reliability with standard incident management metrics, such as mean time between failures and the failure rate:

• Mean time between failures: Calculate MTBF by dividing the total operation time by the number of failures. This metric is crucial for understanding the average time duration between failures.

• Failure rate: Calculate failure rates by dividing the number of failures by the total time in service. Handbooks like MIL-HNDBK-217 can lead to inaccuracies due to the assumption of a constant failure rate, which may result in misleading predictions about component reliability, especially as components age.

It’s important to consider additional factors, such as service-level agreements and customer expectations for the system. Reliability standards can vary based on what’s at risk if a system fails. For example, will failure cause a group of tax preparers to take the afternoon off? Or will it strand thousands of airline passengers far from their homes?

Reliability calculations

Reliability calculations use mathematical models and statistical techniques to estimate the reliability of a system or component. They typically use failure rates, mean time between failures (MTBF), and other reliability metrics to determine the probability of system or component failure. 

By analyzing these metrics, businesses can identify potential weaknesses and areas for improvement. Reliability calculations can be performed using various methods, including fault tree analysis, reliability block diagrams, and Markov modeling. These techniques help visualize and quantify the reliability of complex systems, enabling decision-makers to make informed choices about design, maintenance, and resource allocation.

Mean Time to Failure (MTTF) and Mean Time Between Failures (MTBF)

Mean Time to Failure (MTTF) is the average time a system or component takes to fail, while Mean Time Between Failures (MTBF) is the average time between failures. MTTF is typically used for non-repairable systems, while MTBF is used for repairable systems. Both metrics are important for reliability calculations, as they provide insight into the frequency and likelihood of system or component failures. 

By understanding these metrics, businesses can better predict maintenance needs, plan for replacements, and improve overall system reliability. Calculating MTTF and MTBF involves collecting data on failure events and using statistical methods to compute the average time to failure and the average time between failures, respectively.

Service-level indicators for reliability

Traditional metrics such as failure rate and MTBF help teams understand how often a system fails. However, they may not fully show whether the system is delivering a successful experience to users. Service-level indicators, or SLIs, measure specific aspects of service performance that determine whether a system is functioning as intended. They extend the concept of reliability monitoring to offerings such APIs, cloud services, and data systems.

Common reliability SLIs include:

• Correctness: The percentage of requests, calculations, or transactions that produce the expected result.

• Latency: The amount of time the system takes to respond or complete an operation.

• Freshness: Whether users receive current information within an acceptable timeframe.

• Completeness: Whether the system processes all expected requests, records, events, or transactions.

• Durability: Whether stored data remains intact and retrievable over time.

• Successful request rate: The percentage of requests completed without errors or failed responses.

Teams should select SLIs that reflect the system’s purpose and the experience users expect. For example, an online store may monitor successful checkout transactions and page-response times, while a data platform may focus more heavily on freshness, completeness, and accuracy. Teams can then compare these measurements with defined service-level objectives to determine whether the system is meeting its reliability targets.

How to improve reliability

Improving reliability requires a combination of preventive upkeep, resilient system design, rigorous testing, performance monitoring, and effective incident response.

• Create routine maintenance schedules to keep systems up to date and modern.

• Implement system redundancy to prevent component failures from halting processes.

• Complete quality control and testing when upgrading or making system changes so teams can correct issues before they reach production.

• To understand system reliability and performance, use comprehensive data collection and analysis methods at scale.

• Improve incident communication to decrease response and recovery time.

What is availability?

Availability is the percentage of time that a system or component is operational and can perform its function—its up-time.

Large online retailers, for example, must maintain site availability 24/7 to meet customer demand or risk losing market share to competitors. Availability takes into account a variety of conditions, such as user internet speeds and peak traffic times.

Availability alone does not confirm that every feature is working correctly. A system may be online yet return inaccurate data, respond too slowly, or experience failures in individual components. For this reason, teams should evaluate availability alongside reliability rather than treating uptime as a complete measure of system health.

For example, a system can be available while still experiencing reliability problems, including:

• Partial failures: The system is online, but a specific feature or a dependent service is not working.

• Latency failures: The system responds, but too slowly to meet user expectations or performance standards.

• Correctness failures: The system is accessible but returns inaccurate, outdated, or otherwise incorrect information.

• Completeness failures: A process runs but fails to handle all expected requests, records, or transactions.

Availability is often calculated using the formula: 

Availability = (MTBF / (MTBF + MTTR)), where MTTR is the mean time to repair. 

This formula clearly explains how often a system is expected to be operational and ready for use. High availability is crucial for systems that require continuous operation, such as online services and critical infrastructure. By focusing on MTBF and MTTR, businesses can improve their systems’ availability and meet user expectations.

How to measure availability

Availability is expressed as a single percentage. When all downtime is included, teams calculate it by subtracting total planned and unplanned downtime from the total elapsed time, then dividing the result by the total elapsed time:

availability percentage = (total elapsed time – downtime) / total elapsed time

For example, if an online retail site is down for three hours a day due to traffic overload, its availability score is 87.5%. The standard may be closer to 99.5% for large international retailers, giving the online retailer much to improve.

ITSM software, such as Jira Service Management, helps teams track incidents and collect data to measure availability.

When calculating availability, teams must define what counts as downtime. Some calculations include both planned downtime, such as scheduled maintenance, and unplanned downtime caused by failures or outages. Others exclude approved maintenance windows. Organizations should document the method they use so availability percentages can be interpreted and compared accurately.

How to improve availability

Improving availability means reducing both the frequency and duration of downtime through proactive maintenance, resilient system design, and rapid recovery processes.

• Implement proactive, standard maintenance schedules to ensure high availability.

• Add system redundancy with failover mechanisms.

• Create rapid repair processes as part of incident management.

Proactive maintenance, in particular, can help businesses gain greater availability and service reliability. Conducting a reliability, availability, and maintainability (RAM) study can provide important insights into where to focus maintenance efforts.  

The difference between reliability and availability

Reliability and availability are often mistaken for the same thing. However, they not only differ but also don't always align.

Even the standards by which companies measure them can differ, depending on the system and its function. To gain an accurate view of any business system, you should analyze reliability vs. availability metrics separately.

• Reliability measures whether the system has delivered the correct output at a specific, defined time—e.g., transferring payroll funds to the correct accounts on the right day. 

• Availability measures the system’s uptime—for example, by providing uninterrupted oxygen monitoring to premature babies during their required incubation period.

Jira Service Management includes automation templates that collect data, elevate incident communication, and improve overall customer service.

 Reliability and availability measure different aspects of system performance, but they work together to provide a more complete picture of system health.

Organizations should assess both metrics rather than treating either one as a complete measure of performance. A highly available system that frequently produces errors may still fail to meet user needs, while a reliable system provides little value when users cannot access it.

Factors affecting reliability and availability

Reliability and availability are shaped by the conditions a system operates in, the quality of its components, and the processes used to monitor, maintain, and support it.

• Environmental: This can include IoT components, such as pressure gauges with exposure to inclement weather, or cyclical user patterns, such as high retail site traffic on specific days. Mean and standard deviation are applied to various parameters to assess the probability of failure and enhance safety factor methodologies.

• Component quality: Examples include third-party integrations or hardware. The importance of standard deviation in understanding the variability of calculation outcomes and the probability of failure in structural analyses cannot be overstated.

• Operational: This may include the frequency of inspections and maintenance or investment in modernized software.

Businesses can improve overall service reliability and availability by standardizing environmental thresholds and adding redundancy, requiring ISO compliance for component quality, or implementing procedures to inspect, test, and maintain every aspect of the system.

Balance reliability and availability with Jira Service Management

With the right tools and approach, companies can balance system reliability and availability, especially in our always-on world. Jira Service Management enables teams to restore service rapidly.

Jira and Jira Service Management empower customers to report issues and help service teams centralize alerts for rapid categorization and prioritization. Rules and communication channels ensure that no one ever misses a critical issue.

Reliability vs. availability: Frequently asked questions

What is an example of reliability vs. availability?

Consider new technology like driverless cars. Service reliability standards are near or at 100% because a single failure can result in injury or death. 

Conversely, the availability of driverless cars affects the user experience. The higher the availability or operational time, the better the experience. Low availability may cause the business to lose market share, but it is unlikely to result in injury or death.

Why are reliability and availability important?

Both reliability and availability impact a business’s bottom line by affecting customer satisfaction. In addition, systems that are unavailable or unreliable cost companies money through lost revenue, spoilage, unplanned maintenance, and lost productivity.

Focusing efforts on improving service reliability and availability can lead to a greater competitive advantage, increased market share, higher revenue, and a better maintenance cost budget.

What are the trade-offs between reliability and availability?

Businesses sometimes have to prioritize reliability over availability or vice versa. Real trade-offs may be necessary when timelines are short or investment funds are limited.

In the case of driverless cars, businesses are likely to invest more time and effort in increased reliability, even if it negatively impacts availability. However, in less critical situations, such as online retail, a business may focus on increasing availability because being “always open” is a key differentiator between e-commerce and brick-and-mortar competitors.