Site Reliability Engineering (SRE) is an increasingly critical discipline in the modern DevOps environment. Originated at Google in 2003, this methodology proposes treating operations as if they were software problems. According to a recent article published by IBM, there are seven fundamental principles that guide teams toward operational success.
It is important to highlight that, according to the authors of Google’s SRE guide, between 40% and 90% of the total costs of a system are incurred after its creation. Therefore, SRE focuses on maximizing the utility and stability of the product throughout its lifespan.
Below, we break down these key principles based on the original source from IBM Think Insights.
1. Embracing Risk
Counterintuitively, the goal of SRE is not to achieve 100% reliability, as this is usually costly and stifles innovation. The principle is based on managing risk as a continuum, using “error budgets” to balance stability with the speed of delivering new features.
To manage this, Error Budgets are used. This concept recognizes that trying to go from 99.99% to 100% availability is exponentially expensive and often imperceptible to the user. The error budget defines how much downtime is acceptable; as long as it is not exhausted, the team has the freedom to innovate and launch updates quickly.
2. Service Level Objectives (SLO)
Establishing quantifiable goals is vital. An SLO defines the expected level of quality (such as latency or availability). These objectives are measured through Service Level Indicators (SLI) and help prioritize engineering work over reactive tasks.
To implement this correctly, it is crucial to differentiate three concepts:
- SLI (Indicator): The actual metric (e.g., current latency of 200ms).
- SLO (Objective): The internal goal (e.g., “latency < 300ms 99% of the time”).
- SLA (Agreement): The contract with the user and penalties if failed. A good SRE focuses on meeting SLOs to keep users satisfied without burdening the team with unnecessary perfectionism.
3. Eliminating Toil
“Toil” is defined as those manual, repetitive tasks that do not provide long-term value but grow linearly with the service. SRE seeks to automate these tasks to free up cognitive time for engineers to focus on higher-value projects.
A golden rule for identifying Toil is: “If your service remains in the same state after you have finished a task, the task was likely useless.” Tasks like manual restarts or downloading metrics also generate cognitive load, forcing engineers to constantly relearn basic processes instead of focusing on architecture.
4. Monitoring
You cannot improve what you do not measure. Effective supervision focuses on the “Four Golden Signals”: latency, traffic, errors, and saturation. The goal is to analyze system performance in real-time and alert on issues before they severely affect users.
The detailed Four Golden Signals are:
- Latency: The time it takes to serve a request (differentiating between successful and failed ones).
- Traffic: The demand placed on the system (e.g., HTTP requests per second).
- Errors: The rate of requests that fail (explicitly like 500s, or implicitly like slow responses).
- Saturation: How “full” the service is (CPU/Memory usage) before it starts to degrade.
5. Automation
Automation is the engine that enables scalability. By automating processes such as account creation or error detection, consistency is guaranteed, human error is reduced, and exponential workloads can be managed without increasing headcount at the same rate.
The real value of automation lies in consistency and scalability. A human might make mistakes when manually creating 100 user accounts, whereas a script will always do it the same way. This decouples business growth from staff growth: managing 10,000 users costs the same engineering effort as managing 10.
6. Release Engineering
This principle integrates the release process from the beginning. It prioritizes rapid and frequent releases, hermetic builds, and deployment automation to ensure changes are safe and reversible if necessary.
The philosophy of “rapid and frequent releases” is pursued. By launching small changes every hour instead of large monthly updates, it is much easier to isolate errors and revert changes. Furthermore, hermetic builds are promoted: the process must be identical and independent of the machine where it is compiled, ensuring predictable results.
7. Simplicity
Complexity is the enemy of reliability. SRE advocates for keeping systems simple: removing unnecessary features, avoiding over-engineering in APIs, and making small, incremental changes to facilitate debugging.
As IBM points out, software is “inherently dynamic and unstable.” Therefore, removing dead code or features that do not add value is just as important as creating new ones. Simple and modular APIs require less documentation, are faster to integrate, and drastically reduce points of failure in the system.
SRE in Practice with TeraLevel
In day-to-day operations, these principles serve as a way to systematize reliability without obsessing over 100% uptime, balancing reactive work with practices that reduce manual effort and improve resilience.
At TeraLevel, we approach these topics from real-world experience: it is not just about defining best practices, but implementing them with automation and observability that work in production. This is where TeraSuite adds value:
- TeraMonitor facilitates continuous monitoring and early detection of deviations.
- TeraSec reinforces reliability against threats and misconfigurations.
- TeraBackup ensures recovery and continuity in the face of unforeseen failures.
This approach—measuring, automating, and simplifying—allows architectures to be not only correct on paper but robust in real operation.
Source: To dive deeper into these concepts, you can check the original article at IBM Think Insights.