Mean Time Between Failure (MTBF) is a way of indicating how long, on average, a system continues to function before failure occurs. It represents the average time a system functions before it fails. MTBF is mainly used with mechanical or electronic systems that can be repaired after failure, such as servers, machines or vehicles.
A higher MTBF value means that the system operates longer on average without failure, indicating higher reliability. It is an important concept in industries where downtime is costly, such as manufacturing, IT, aviation and transportation.
Note: MTBF is often confused with Mean Time To Failure (MTTF), but there is an important difference. MTBF applies to systems that you can repair, while MTTF is used for systems that you replace as soon as they break down (such as light bulbs or batteries).
MTBF plays a major role in predicting maintenance needs, improving product designs and estimating the total cost of ownership of a system.
By analyzing MTBF, you can, among other things:
Measure reliability: MTBF shows how often to expect failures.
Plan maintenance: A low MTBF indicates frequent failures. This allows you to schedule preventive maintenance to reduce downtime.
Reduce costs: Fewer outages mean fewer emergency repairs, less production loss and lower operating costs.
Underpin warranties and SLAs: Manufacturers often use MTBF as an argument for the quality of their product.
In addition, it is a useful KPI in quality management and risk management. MTBF helps inform decisions about investments, replacement times and improvement projects.
Mean Time Between Failure (MTBF) is used in software applications to measure how reliable a system is before it crashes, crashes or suffers another serious failure. Think of servers, network systems, or mission-critical software applications.
The formula
The basic formula remains the same:
MTBF = Total operational time / Number of failures
Example:
A Web server runs 2,000 hours in a quarter and crashes 4 times during that period. Then the MTBF is:
MTBF = 2,000 / 4 = 500 hours
On average, the server operates for 500 hours before a failure occurs.
What do you need for a good calculation?
For software, it is important that you:
Properly define each outage: Only include incidents that result in downtime or severe loss of function.
Use accurate logs and monitoring: Such as uptime monitoring, logging tools and observability platforms.
Chooses realistic time periods: For example, a quarter or six months during which the system was in normal use.
In modern systems such as microservices or container platforms, MTBF can be measured on a per-component basis. In a serial dependency (where one crash brings down the entire system), the weakest link has the greatest impact on overall MTBF. In parallel systems with failover mechanisms (such as load balancers or redundancy), the MTBF can be much higher because the system continues to function despite individual crashes.
Suppose you manage a cloud application that runs 24/7. In the past month, the system has been running continuously, which amounts to about 720 hours of operational time (30 days x 24 hours). During that period, three critical failures were recorded where the application was temporarily unavailable.
The calculation is then simple:
MTBF = 720 hours / 3 failures = 240 hours
This means that, on average, the application experiences an outage every 240 hours. With this information, you can:
Detect trends in failures
Plan better maintenance or refactoring
Estimate whether the system meets SLA agreements
Now suppose you have several components - for example, a frontend, backend and database - and only the database crashes twice, while the rest remains stable. You can then calculate MTBF for each component as well:
Database: 720 hours / 2 = 360 hours MTBF
Backend: 720 hours / 0 = theoretically infinite, or "no failure"
Frontend: 720 hours / 1 = 720 hours MTBF
This itemized approach gives you much better insight into where bottlenecks are and where you need to intervene.
Although the formula of MTBF seems simple, in practice it is often difficult to arrive at a reliable value. Especially in software environments, the following challenges can come into play:
What counts as a failure? Is a short delay of one second measurable? Or only complete downtime? With software, it is important to define clear criteria in advance. Without this delineation, the MTBF becomes unreliable.
Many companies rely on logging or monitoring tools. But if logging is not fully set up or incidents are not captured, the calculation can be skewed. For example, consider a backend that crashes but automatically restarts without alerting. Then it looks like nothing ever happened.
In modern software architectures such as microservices, serverless or container platforms, there are often dozens to hundreds of individual components. A failure in one small component can lead to a chain reaction, but is not always correctly linked to the main system MTBF.
With continuous development (e.g., CI/CD), software changes constantly. As a result, historical MTBF data are sometimes no longer representative of the current version.
Some teams record failures accurately, but do not measure total operational time precisely. Or vice versa. Without accurately monitoring both elements, the MTBF calculation has little value.
The MTBF value provides insight into the average time between failures, but it is important to have a good understanding of what this value does and does not tell you.
What you can infer
Reliability trend: A higher MTBF indicates fewer failures and thus a more stable system.
-Maintenance needs: A low MTBF means you need to resolve incidents more often. This may prompt more monitoring or preventive measures.
System comparison: You can use MTBF to compare the reliability of different systems or versions.
What you cannot derive
Duration or impact of failures: MTBF says nothing about how severe or prolonged a failure is. One 10-second crash and one 10-hour crash count equally.
Cause of failures: MTBF shows frequency, but not why the system fails. This requires additional analysis, such as root cause analysis (RCA).
Future reliability: MTBF is based on the past. A system that has recently been updated may behave completely differently.
Especially in software, MTBF is a guideline and not a guarantee. It helps prioritize and evaluate system quality, but should always be combined with other information such as error logs, user experience and recovery statistics (such as MTTR).
A higher Mean Time Between Failure (MTBF) means fewer failures and thus a more reliable system. Especially with software, it is possible to take targeted measures to increase the MTBF.
Ensure proper monitoring of your application, server, or infrastructure. Tools such as Prometheus, Grafana, New Relic or Datadog provide insight into performance and warn you when deviations occur. This allows you to intervene before a failure occurs.
Perform a root cause analysis (RCA) for every failure. Don't just look at the symptoms, but look for the underlying problem. Adjust your code, infrastructure or processes accordingly to prevent recurrence.
Automated testing and CI/CD pipelines reduce the chance of errors in production. This prevents a new release from causing unexpected crashes.
Implement failover mechanisms, redundancy, and proper error handling. For example, if one microservice fails, the system should respond to it in a controlled manner instead of crashing completely.
Schedule regular maintenance for servers, databases or underlying systems. As with hardware, you can also prevent “wear and tear” in software by implementing updates, optimizations, and patches on time.
Outdated libraries, dependencies, or systems can cause unexpected instability. Regular updating helps improve MTBF.
By working structurally on quality, monitoring and maintenance, you increase reliability and therefore the MTBF.
Mean Time Between Failure (MTBF) is used in many industries to measure the reliability of systems. Within software and IT, it is primarily a practical KPI for service quality, uptime, and risk management.
SaaS platforms use MTBF to monitor uptime and substantiate SLAs to customers. The higher the MTBF, the more reliable the service.
DevOps teams use MTBF alongside metrics such as MTTR to understand the balance between stability and speed of change.
Network administrators measure the MTBF of servers, switches or virtual machines to better align capacity planning and maintenance.
Site Reliability Engineers (SREs) use MTBF as part of broader reliability metrics, such as SLOs and error budgets.
MTBF is applied to device firmware, IoT solutions and industrial software integrated into machines. Here, reliability is often crucial due to continuous use.
Managers and product owners use MTBF to assess risks and set priorities. Low MTBF can lead to choices such as additional investment in refactoring, system replacement or infrastructure overhaul.
MTBF is not an end in itself, but a useful tool to better inform technical and strategic decisions.
Mean Time Between Failure (MTBF) is not an isolated term. There are several terms that are often used in the same context, especially within systems management, DevOps, and reliability engineering.
The failure rate indicates how often a failure occurs within a given period of time. It is basically the inverse of MTBF:
Failure rate = 1 / MTBF
For example: if an application has an MTBF of 500 hours, the failure rate is 0.002 failures per hour. This metric is often used in risk analysis or component reliability estimates.
MTTR represents the average time required to resolve a failure. Whereas MTBF focuses on preventing failures, MTTR focuses on how quickly you are back up and running after a failure.
A low MTTR combined with a high MTBF indicates a robust yet efficiently managed system.
MTTF is similar to MTBF, but is used for systems that are not repaired, but replaced. Think of disposable components such as fuses or batteries.
With software, you rarely see MTTF because most applications are restartable and recoverable.
RCA is a method of finding the root cause after a failure. It is often deployed in response to incidents where the MTBF drops so that structural improvements can be made.
By structurally performing RCAs, you not only improve your MTBF, but also your process quality.
Mean Time Between Failure (MTBF) helps you understand the reliability of software and IT systems. It gives an average number of hours between failures, and is a useful measure for both technical and business decisions.
However, the MTBF alone does not tell the whole story. Always combine it with other data such as MTTR and root cause analysis. That way, you'll provide a balanced picture of system stability and where improvements are needed.
So don't use MTBF as an end in itself, but as a practical tool to make reliability measurable and discussable.
It depends on the type of system. In software, the higher the MTBF, the more reliable the system. For mission-critical applications, you aim for the highest possible MTBF.
You divide the total operational time of a system by the number of failures in that period. For example: 1,000 hours of operation with 5 failures = MTBF of 200 hours.
MTBF shows how often on average you can expect a failure. It gives insight into the reliability of a system, but says nothing about the severity or duration of those failures.
