SLO Metrics: A Best Practices Guide

Service reliability and great customer experience are table stakes for modern businesses. The challenge for many organizations is quantifying exactly what that means in practice. Service level objectives (SLOs) help solve this problem. 

Effective use of SLO metrics makes service reliability measurable, builds alignment with stakeholders, and supports continuous improvement. The right SLO metrics help teams focus on business needs and achieve the desired customer outcomes. However, getting SLO metrics right isn’t always straightforward, and teams need a clear strategy to avoid common SLO antipatterns. 

This article explores SLO metrics and related concepts such as SLI, SLA, burn rate, and error budget. It then dives deeper into the types of SLO metrics, common mistakes, and seven best practices for implementing SLOs for your organization. 

Summary of key service level objective metrics concepts

The table below summarizes the service level objective metrics and concepts this article will explore in more detail. 

Service level concepts 

SLI, SLA, and SLO metrics coexist to help measure customer satisfaction.

Clear performance and reliability expectations are essential for effective IT service management. SLIs, SLOs, and SLAs are the three primary service level concepts that come together to create a comprehensive framework for monitoring, managing, and communicating service reliability. Let’s explore these three essential service level concepts in depth. 

Service level indicator (SLI)

A service level indicator (SLI) is a quantitative measure of service reliability from a user-centric perspective. SLIs can take several forms: 

• Success ratios (good events / valid events) measured over a time window
• Latency percentiles like “ 95th percentile request latency”
• Throughput measurements like requests per second, 
• Availability measurements like the percentage of time a service is operational.

For instance, here is a basic transaction success rate SLI:

Transaction success rate = (Successful transactions / Valid transactions) *100

For this SLI, successful transactions are all transactions that meet defined success criteria, while valid transactions are all transactions excluding invalid requests and planned maintenance windows. Measurement occurs over a specified time window, for example, rolling 28 days. 

Organizations often adapt and enhance this basic formula, incorporating techniques like weighted measurements, satisfaction scores, or moving averages to better match their specific service characteristics. 

The specific form of an SLI should align with how users experience services. For instance, an e-commerce platform might care more about checkout success rates, while a content delivery service might focus on latency percentage.

Service level objective

A service level objective (SLO) is a target that defines acceptable service performance over a specific period. SLOs are essential for teams of all sizes because they help track if user expectations are met and maintain service quality. 

For example, teams may define SLOs such as: 

“97% of transactions must succeed over a rolling 30-day window”
or
“95% of page loads must complete within 3 seconds measured monthly”

Service level agreement (SLA)

A service level agreement (SLA) is a contractual agreement that defines performance commitments to customers. SLAs are typically expressed as target levels for specific SLIs over defined time periods. Because SLAs are contractual agreements, service providers may face financial and legal consequences for SLA breaches.
The lines between SLAs and SLOs often confuse those new to IT service management. The concepts are related, but different:

• SLAs are contractual obligations to a customers
• SLOs are internal service goals that are typically set more strictly than SLAs. 

While SLOs are not typically shared with external customers, they are crucial to meeting SLAs. 

Error budget & burn rate

An error budget is the maximum amount of time a service can fail or underperform while still meeting its SLO. The burn rate measures how quickly this error budget is being consumed relative to time elapsed. A high burn rate indicates the service is consuming the error budget faster than it is sustainable.

For example, consider your Wi-Fi plan. Your internet company guarantees an availability of 99.9% per month. This means the internet service can be down 0.1% of the time without breaching the agreement, meaning  0.1% x 43200 minutes = 43.2 minutes of allowed downtime without breaching its reliability target. This is your error budget.

The basic burn rate formula is:

Burn rate = (Error Budget Consumed / Time Elapsed) / (Total Error Budget / Time Period)

Teams may choose calculation methods depending on their specific needs, such as using direct ratios or time-based comparisons. Let’s explore a practical example to understand why. 

Consider a 10-minute outage in the first week (7 days). The error budget consumed would be 10 minutes, and the elapsed time would be 7 days or 25% of the month to keep things simple. The expected budget consumption would be 25% x 43.2 = 10.8 minutes. The burn rate would be (10 / 10.8) = 0.926x, slightly under budget.

If there is a second 20-minute outage in the second week, the total monthly consumption is now 30 minutes. The time elapsed is 14 days (50% of the month). The expected budget consumption would then be 50% x 43.2 = 21.6 minutes, and the burn rate would be (30 / 21.6) = 1.389x, which is above the sustainable rate.

There are two approaches to planned maintenance. One approach is to count it as error budget burn and adjust targets. It’s also acceptable in some situations to exclude the error budget calculations during planned maintenance. For example, in situations where an SLO is defending an SLA that excludes planned maintenance windows, it is may be preferable to schedule the maintenance during defined maintenance windows that don’t count against the error budget. This preserves the error budget for handling unexpected issues.

SLOs vs. MTTR: Why proactive (SLO) beats reactive (MTTR) 

Traditionally, mean time to recovery (MTTR) has been used to measure incident resolution efficiency as a key performance indicator in IT operations. However, MTTR alone is insufficient to measure service reliability and meet user expectations, so SLOs are becoming more widely adopted by organizations. Let’s compare these two service management concepts to learn why a proactive SLO-based measurement strategy beats a reactive MTTR-based approach. 

MTTR values can be misleading since they do not account for outage frequency. So, a system with a fast recovery time but several short outages might seem “more reliable” than one with fewer but longer outages, even though the total downtime and user impact could be worse.

Consider two scenarios of the previous Wi-Fi plan example:

• Scenario A: The internet provider has no defined SLO but uses MTTR to track service performance and aims to resolve issues in 20 minutes on average. 
• Scenario B: The internet provider's monthly SLO is 99.9% availability, or 43.2 minutes of error budget. 

The company uses a reactive approach in Scenario A, focusing on resolution over impact. While MTTR tracks how fast issues are resolved, it doesn’t account for the number of incidents. This could mean that the provider recovers the service in under 20 minutes, but the service is disrupted 10 times that month, with a total downtime of 200 minutes, far exceeding the 43.2-minute error budget. 

On the other hand, in scenario B, the company takes a proactive approach. By defining availability targets, the internet provider prioritizes the customer experience and ensures that downtime stays within 43.2 minutes per month, regardless of the number of disruptions. SLOs also incentivize providers to minimize the frequency and duration of disruptions instead of simply optimizing repair times.

Five types of SLO metrics

Depending on the kind of service you are measuring reliability for and the expected outcome, several different types of SLOs might be relevant. The sections below explore five common types of SLO metrics and their use cases.  

Availability-driven SLOs

These SLOs measure the percentage of time a service is accessible and operational to meet the users' expectations for the service´s uptime. Some example scenarios where ability-driven SLO metrics are helpful include: 

Latency-driven SLOs

These SLOs measure the time it takes for a service to respond to a request or complete a transaction to ensure fast performance for users. Some example scenarios where latency-driven SLO metrics are helpful include: 

Throughput-driven SLOs

These SLOs measure the rate at which a system processes requests or handles transactions. They ensure the system can handle the expected load without performance degradation. Some example scenarios where throughput-driven SLO metrics are helpful include: 

Error rate-driven SLOs

These SLOs measure the percentage of failed or incorrect responses from a service. They ensure the minimization of disruptions and errors directly impacting user experience.  Some example scenarios where error rate-driven SLO metrics are helpful include: 

Composite SLOs

In complex systems, having isolated SLOs for individual services may not provide a complete view of end-to-end system reliability. Composite SLOs combine multiple component-level SLOs to measure the overall health from a user perspective.

Consider the human body as an example, where different organs (components) must work together to maintain overall health (the composite goal). The overall health (system reliability) depends on all components (heart, lungs, liver, etc.) working together. The composite SLO will suffer if one system fails to meet its SLO. Similarly, having siloed SLOs for each service is insufficient to get a big-picture view of a service ecosystem.

For example, consider an e-commerce checkout flow:

• Frontend service: 99.9% availability
• Authentication service: 99.9% availability
• Payment processing: 99.9% availability
• Order management: 99.9% availability

While each service meets a strong individual SLO, the actual user experience depends on all services working together. The composite SLO would typically be lower than any individual SLO, as it represents the probability of all required components working successfully together.

Composite SLOs ensure teams measure and optimize for the complete user journey rather than individual service performance. They help teams understand true end-to-end reliability and set realistic targets that reflect the user experience. 

To learn more about composite SLO metrics, refer to this official documentation. 

Composite structure as an example in Nobl9. (Source)

Defining and setting up SLO metrics

To effectively identify your SLIs and set up corresponding SLOs, you must first understand what matters to your customers. This process is called customer journey mapping.

Identify the most critical user journeys to help capture the experience of the customers of the product. A critical user journey is a series of steps or tasks that is a core part of a user's experience. For highly complex systems, you should decompose services to understand their criticality, role, dependencies, and data sources. For example, the critical user journey in an online shopping experience would be: 

Once the journey has been identified, determine which SLIs best reflect the user experience. These metrics can be availability, latency, throughput, error rate driven, or even custom metrics depending on the use case at hand:

The next step is to define the target SLO against which you would measure reliability. This target should be realistic based on historical data, user feedback, and upcoming business priorities.

To ensure SLOs are truly useful, follow the VALET principle:

• Valuable—The SLO should focus on metrics that matter to the business and user experience and not only to internal teams.
• Actionable—Direct actions should be set up when the SLO is breached (team notifications, resolution runbooks, etc.).
• Legitimate—The SLO should be based on relevant, measurable data from trusted sources, with consistent methods. 
• Explicit—The SLO should be unambiguous, clearly defined metrics and measurement windows.
• Testable—The SLO should be verifiable and validated using existing monitoring tools.

Once you have identified your SLOs, you can use them to track service reliability. However,​​ remember that SLOs are not set in stone. You must revisit, refine, and iterate your SLOs regularly, for example, by adjusting them based on past performance data and changing business needs.

Four common SLO mistakes to avoid

When teams use SLO metrics well, they can quantify system performance and drive continuous improvement. However, there are several SLO antipatterns and mistakes that can cause SLO implementations to fall short of their potential. 

Defining too many SLOs

Defining too many SLOs for a service could overwhelm teams and dilute focus from what truly matters to users. It is difficult for engineers to prioritize which SLOs are "must act on" vs "nice to act on," and is an inefficient use of their time. Instead, focus on the few critical SLOs (availability latency, throughput, etc.) that directly impact user experience. 

To avoid this SLO mistake, follow Google’s recommendation of 3-5 SLOs per user journey.

Not performing service decomposition

Highly complex systems often struggle to identify the SLOs that directly correlate to customer experience. In this case, it's crucial to brainstorm as a team and break down the technical architecture into simpler components before identifying the respective SLOs. Failure to do so can lead to unrealistic objectives being set.

Imagine you have an E-commerce platform with a checkout process, and you set up an SLO as "99.9% of the checkout attempts must be successful". When the SLO is breached, the engineering team might struggle to identify the root cause. 

• Is there an issue with the front end? 
• The request to Payment Gateway failed? 
• Or could the inventory database not be reached?

Decomposing the service is beneficial for correctly identifying the problem. This allows for more granular SLOs, such as "99.5% of checkout page loads must complete within 3 seconds" or "95% of payment processing API calls must respond successfully within 100ms."

Ignoring error budgets

Defining SLOs and error budgets go hand in hand. Treating error budgets as an afterthought leads to reliability misalignment. Once your SLOs are set up, the next step is to decide on an error budget policy—in agreement with your organization's developers, product managers, and SREs. The policy outlines what to do when your service runs out of budget. Standard policies include stopping feature launches until the service is within SLOs or devoting some or all engineering time to working on reliability-related bugs.

Setting unrealistic reliability targets

Targeting an extremely high SLO, such as 99.9999% or 100%, is unrealistic. A high SLO would mean you only have time to be reactive, as there is a negligible error budget for feature enhancements, deploying new hardware, scaling up to meet customer demand, etc. Sooner or later, the service stagnates.

Each additional nine added to an SLO target (e.g., going from 99.9% to 99.99%) corresponds to exponential growth in effort and costs to meet the SLO, making them impractical to implement in really complex landscapes. Hence, teams should set up realistic SLOs based on customer journey mappings, past data, and alignment with business priorities.

Seven practical best practices for SLO metrics

There is no one-size-fits-all SLO implementation that will work for every time. Getting SLOs right requires a combination of expertise, context, and tooling. That said, there are proven best practices that can make the process simpler and help organizations get the most out of their SLOs. In the sections below, we’ll explain seven proven SLO best practices to help teams avoid common SLO mistakes and improve their operations. 

Use the KISS principle

Keep it Short and Simple, or KISS, applies perfectly to defining SLOs. Pick an aspect of your system that is relevant and easy to measure. Then, choose a few critical metrics (e.g., availability, latency, error rate) that are the most relevant to user experience, and with the VALET principle in mind, define your SLOs based on those metrics. 

Emphasizing simplicity in this process can make a significant difference in quality and ease of use. For example, consider these two SLOs:

• SLO 1: 99.5% of users of an e-commerce platform complete the checkout process within 5 seconds unless they encounter timeout or network errors
• SLO 2: 99.5% of checkout requests on an e-commerce platform should be completed within 5 seconds over a rolling 30-day window.

SLO 1 is complex because it includes exceptions and is challenging to measure because it tracks user traffic instead of website performance. On the other hand, SLO 2 is a simple latency SLO that considers the KISS principle—it is simple to measure, user-centric, testable, and actionable by the teams if breached.  

Start small and iterate

Often, it can be overwhelming in highly complex organizations to understand where to start defining SLOs. Here, it helps to start simple:

• First, choose one application for which you want to define SLOs. Later, if necessary, you can add other sub-apps or dependencies.
• Decide in simple terms who your application users are.
• Consider the common ways these users interact with the application- the user journeys.
• Draw up the high-level architecture diagram of the system, request flows, and dependencies. This diagram helps define SLIs and their corresponding SLOs
• Gradually iterate and expand the scope of SLO coverage, eventually moving to composite SLOs.

Don’t reinvent the wheel, use industry standards 

Standards and frameworks such as RED, USE, and Golden Signals, can help streamline your SLO journey and avoid reinventing the wheel for problems the industry has already solved. 

RED metrics

These are most suitable for user-facing applications and web services. 

USE metrics 

These are most suitable for system-based resource tracking, such as servers, databases, and network devices.

Golden signals 

This standard monitoring practice typically used by SRE (Site Reliability Engineering) applies to any system. If you cannot monitor any other metrics, you should focus on these four: latency, traffic, errors, and saturation. 

Regularly align with business objectives

SLOs must continuously evolve with business needs, user expectations, and system performance trends. Establish a regular cadence (e.g., monthly, quarterly, or semi-annually) to review SLOs and readjust where needed. Crucial questions to ask yourself during these reviews:  Does the SLO still align with current user needs? Are the SLOs realistic based on historical data? Have there been any shifts in performance, user traffic, or error patterns that require updates?

For example, suppose you have a latency SLO defined for your SaaS platform as: "API latency for all regions is 500 ms for 95% of the requests, over a rolling 30-day window". Next month, there is an expansion into the Asia market, likely resulting in a higher latency due to the distance from the data center. You can readjust the SLO by setting region-specific SLOs:

• Asia: API call latency is below 700 ms for 95% of requests over a rolling 30-day window
• Other regions: API call latency is below 500 ms for 95% of requests over a rolling 30-day window 

Build on metrics history

Metrics history is a data-driven process for setting up SLOs that determines how the system has performed over time. Analyzing historical metrics allows you to identify trends like seasonal traffic spikes, increasing user load, or performance degradation. It also avoids overcommitting to targets that strain resources or frustrate teams. SLO

targets should always be set within SLA commitments to customers so that teams have a sufficient buffer to address issues before they impact contractual obligations. 

Nobl9 offers a comprehensive tool called SLI Analyzer for setting up SLOs based on historical data. It helps you create your first SLOs and constantly readjust them based on past data trends.

SLI Values from the last 30 days can be analyzed for “Good” vs “Total”. (Source)

Alert on the right SLOs

Configure alerts on SLOs to detect issues early, prioritize critical problems, and minimize noise.

• Focus on user impact to avoid false positive alerts: Trigger alerts only when there is a noticeable degradation in user experience. Use aggregated alerts to avoid overwhelming teams with too many notifications.
• Tie alerts to Runbooks: Alerts should include clear remediation steps (scripts, automated runbooks) to streamline incident response.

Gaining complete transparency on SLOs is challenging if you monitor your system using multiple platforms and data sources. Nobl9 provides a single Service Health

Dashboard to view “Red, Amber, Green” (RAG) status, inspect healthy SLOs, identify alert-triggering ones, and explore them further.

A single pane of glass view of all existing SLOs and their corresponding alerts (source)

Build SLO observability

Observability and SLOs are intertwined concepts that coexist to help achieve the transparency that system reliability requires.

• Collect data using MELT (Metrics, Events, Logs, Traces) to best determine which user-centric SLOs can be set up.
• Implement proactive anomaly detection to detect issues before they become SLO breaches, reducing downtime and improving user experience.
• Set up dashboards and reports to track performance against SLOs. These can be custom-setup based on the audience, scope, and requirements.

Nobl9 provides extensive capabilities for setting up reports and dashboards to gain data-driven insights into your SLOs. You get out-of-the-box reports on SLO history, error budget status, resource usage, system health, etc., alongside custom reports.A system health report across different regions and projects. (Source)

Last thoughts

Implementing effective SLO metrics is a journey that requires continuous refinement rather than a one-time setup. By following the best practices discussed in this article, organizations can transform service reliability from an abstract goal to a measurable outcome. 

Remember that the ultimate purpose of SLOs is to quantify user experience and drive business value, not to create additional operational overhead. As your SLO practice matures, you’ll find that these metrics become a common language that bridges the gap between technical teams and business stakeholders, creating alignment around what matters: delivering reliable services that meet user expectations while supporting business growth.