Service Monitoring: A Best Practices Guide

SLODLC Discovery Phase: Observe system behavior

The first phase of the SLODLC framework is about understanding your system before making monitoring decisions.

Understand system and user flows

Effective monitoring starts with clearly understanding how your system works and its user interaction. This foundational step helps ensure that monitoring efforts are aligned across user actions and how those requests travel through the system. The SLODLC Discovery Worksheet is an excellent tool for achieving this and offers a structured way to document critical services, user flows, failure modes, outage analysis, and telemetry mapping. Teams use it to map out the most essential parts of the application, like login flows, dashboards, and billing systems, as these are typically the highest-impact services for end users. 

As the SLODLC handbook advises, "Start with something meaningful and visible to users, but simple to define". This means engaging with product and engineering teams to surface the most valuable user journeys, like onboarding, payment submission, or file upload, and identifying the systems and dependencies that power them. 

These early conversations also help uncover known failure points or recurring incidents. They also help review past incidents to understand where telemetry was lacking, which signals were most useful in diagnosis, and where new monitoring coverage may be needed.

For example, a SaaS platform's user experience often hinges on login, dashboard, and billing services. These flows are apparent, and their performance directly affects satisfaction and trust.

Choose what to monitor

Effective observability requires a move from speculative monitoring (choosing metrics based on assumptions or hunches) to evidence-driven decisions rooted in real system behavior. This means learning from the past to shape what needs to be tracked going forward. Instead of guessing which metrics might be necessary, base your monitoring on actual incidents, user pain points, and observed gaps in telemetry.

Review past outages and incidents, ideally from the last 6 to 12 months. What degraded? What failed? What were the early warning signs, and where were they missing? These retrospectives help pinpoint which metrics or events were useful or considerably absent when troubleshooting. The outage analysis section of the SLODLC Discovery Worksheet offers a practical format to capture this analysis.

This exercise often reveals patterns such as telemetry that existed but didn’t trigger alerts, alert fatigue from noisy thresholds, or missing instrumentation altogether. For example, a team might discover that latency metrics were available but not granular enough to detect regional slowdowns, or that logs existed but weren't structured for alerting. These lessons directly inform better monitoring design.

By basing your monitoring on observed system behavior, you can enhance signal quality and minimize noise, particularly in alerting. This phase also helps identify early candidates for SLIs. When chosen wisely, these SLIs become the foundation of your SLOs, enabling structured reliability goals and more innovative alerting practices. So, take the time to analyze your system’s behavior.

Select data sources

Here, you determine the sources of your monitoring data. The quality of your SLIs and the overall effectiveness of your monitoring depend on choosing reliable, relevant telemetry. Without trustworthy data, observability efforts risk becoming speculative. Data can come from one or many sources and typically fall into one of three types: metrics, logs, or traces. Choosing and integrating these telemetry sources during the observation phase ensures that future SLIs, SLOs, and error budgets are based on data reflecting system behavior. The SLODLC Discovery Worksheet provides a structured way to perform this analysis.

Each has strengths and limitations. Together they provide a collective picture of system behavior and lay the foundation for service monitoring initiatives.

Metrics

Metrics are quantitative, structured, timed measurements typically collected from monitoring systems. They are the foundation of most SLIs because they are efficient, lightweight, and easy to aggregate. Metrics help quantify aspects of system performance such as availability, latency, throughput, and error rates.

For example, a metric like http_request_duration_seconds_bucket from Prometheus enables teams to monitor the duration of requests broken down into latency buckets. This is a powerful way to track performance thresholds and align them with user expectations. Moreover, metrics can be processed in real time, supporting proactive alerting and fine-tuned error budgeting. Their structured nature makes them ideal for building high-resolution dashboards and automated health checks.

Modern observability platforms such as Nobl9 allow teams to integrate with these data sources and apply "good" vs "total" query logic, also called ratio metrics, to define SLIs, which then support the reliability targets set by SLOs. For example, here is how such an SLI may be defined:

• Tool: Prometheus
• SLI calculation: SLI = good_events / total_events
• Example metric: http_request_duration_seconds_bucket tracks request latency distribution across pre-defined time buckets
• Example SLI: Percentage of successful login requests under 500ms

These metrics often serve as the starting point for tracking system user-perceived reliability and are directly supported by the SLODLC Implementation Worksheet in the collecting SLI section.

Logs

Logs provide unstructured, event-level details that capture what happened at specific moments within the system. These logs are crucial in auditing events, especially failures, by surfacing the exact conditions under which an issue occurred. A typical log entry looks like this:

{
  "event": "payment_failed",
  "code": "timeout"
}
      

SLODLC Implementation Phase: Implementing monitoring

The next phase of the SLODLC is the implementation phase. In this phase, teams implement the SLIs, SLOs, and monitoring policies that enable effective service monitoring.  

Define and collect measurable SLIs

Once the right data sources are identified, the next step is to define SLIs. These core metrics reflect how well your service is performing from a user's perspective. This foundational concept in modern SLO-driven reliability practices aligns with widely adopted observability methodologies.

SLIs are not arbitrary. They’re precise, meaningful metrics based on user-impacting events. They help answer how reliable this service is for users. And how do you measure that reliability consistently? A good SLI uses structured telemetry to measure the success of a user journey. Nobl9 recommends calculating SLIs using the formula: 

SLI = total_good_events / total_events

This ratio tells you how often the system behaved in a way that users would consider successful over time. For example, your service processed 10,000 HTTP requests in a 5-minute window, and 9,980 returned a 200 OK. Your SLI would be:

SLI = 9980 / 10000 = 99.8%

SLO Management tools make this process easier by integrating with existing telemetry. Your teams can define SLIs using queries directly from their existing metrics.

A simple how-to approach for defining SLIs is:

• Start with a real user journey – Choose a high-impact and measurable experience, like checkout success, dashboard load time, or file uploads.
•  Choose a data source – Use telemetry systems already in place.
•  Define good and total events – Use an SLI builder to input queries for "good" and "total" conditions.
• Preview and validate – Review historical data and ensure the SLI aligns with actual service behavior.
• Focus on what users care about – Prioritize signals that reflect real-world experience, not just internal system metrics.

This approach ensures that SLIs are technically feasible and use measurable, relevant data. The SLODLC Implementation Worksheet—Section 2: Collect SLIs provides a space to document how each SLI is calculated and which data source supports it. 

Set meaningful SLOs

After clearly defining and validating SLIs, the next step is to establish SLOs. These performance goals indicate the level of reliability a service must maintain over time. SLOs help teams shift from observing system behavior to setting user-centric reliability targets. Reliability targets based on SLOs are a key pillar of modern SRE practices, enabling more predictable service performance, informed trade-offs, and reduced operational noise. Defining a meaningful SLO requires close collaboration between engineering and product stakeholders. Together, teams must determine what “good enough” looks like for the user.

An overview of the three components of an SLO. (Source)

A good practice is defining baseline targets (conservative, realistic thresholds based on historical data) and aspirational goals (targets that reflect continuous improvement). This dual-target model helps teams remain grounded while aiming higher over time. 

For more complex services, composite SLOs can combine multiple SLIs, such as availability and latency, into a single objective. These composite objectives offer a holistic view of service health across critical user journeys, making them especially valuable in distributed systems where reliability depends on several interconnected components. More information can be found on our Composite Guide.

The SLODLC Implementation Worksheet prompts teams to document these goals in a structured format, ensuring each SLO is clear, measurable, and technically feasible to track. 

Track and enforce error budgets

It is crucial to clearly communicate the potential unreliability of a service before action is required. Error budgets quantify the permissible level of service failure within a defined period. An error budget is calculated as the inverse of an SLO.

Error Budget (%) = 100% - SLO Target (%)

For instance, if an SLO defines 99.9% availability over 30 days, the corresponding error budget is 0.1%, translating to roughly 43 minutes of allowable downtime.

100% - 99.9% = 0.1%

This budget provides a clear boundary as long as your team stays within it. As the error budget is gradually consumed, teams typically slow their release cadence, introduce more stringent testing, or prioritize fixes over features. Importantly, not all releases are equal. Suppose a feature is business-critical or resolves an ongoing user issue. It may proceed even when the error budget is nearly exhausted, provided an acceptable mitigation plan or rollback strategy is in place. This flexible approach acknowledges that reliability is not about avoiding failure at all costs, but about making informed trade-offs.

Reliability platforms such as Nobl9 support SLO-driven practices and help continuously track how much of the error budget has been consumed. These tools offer real-time visualizations, show burn rate trends, and correlate service events to budget consumption, providing actionable insight into reliability risk. Alerting systems can also be configured to trigger responses as burn rates accelerate, making the concept of error budgets not only measurable but enforceable. Error budget policies ensure team consistency and align reliability enforcement with user priorities.

An example of an error budget policy table is:

Conclusion

Modern service monitoring isn’t just about keeping systems up; it’s about aligning your systems with user expectations and making reliability a measurable and data-driven practice.

This service monitoring journey begins with the Discovery Phase, where teams learn to observe system behavior, identify key user flows, and document their architecture using tools like the SLODLC Discovery Worksheet. 

Then, the Implementation Phase focuses on defining meaningful SLIs, setting practical SLOs, and tracking error budgets to balance innovation and reliability. Alerts become actionable with smart alerting (based on burn rate and SLO violations), visual dashboards, and structured reliability reviews. 

These practices ensure that monitoring isn’t a reactive process but a proactive strategy that drives improvements and accountability across teams. The last pillar in this process is the importance of continuous improvement. Tuning your SLOs as systems evolve, and the critical practice of monitoring your monitoring to keep your observability stack healthy.

To begin your service monitoring journey, start by downloading and working through the SLODLC Discovery Worksheet. Define SLIs for your most critical user journeys, align on realistic SLOs, and begin tracking your error budgets. With these steps, your team can move from reactive alerting to reliable, user-focused service operations.