Fuse SLOs into composites

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Learn to mix different SLOs in a composite SLO

Does your organization rely on multiple data sources to monitor different aspects of system reliability? Or do you need a high-level view of system health without dealing with complex queries? Composite SLOs provide a unified approach to reliability monitoring by aggregating and prioritizing individual SLOs. Whether you're managing diverse data streams or streamlining high-level monitoring, this method simplifies observability.

A composite SLO aggregates performance data from multiple systems and components into a single, cohesive reliability metric. This approach helps:

Unify metrics from various monitoring tools.

Reduce complexity by eliminating the need for custom queries.

Provide a holistic reliability overview, making it easier to track service health and performance trends.

Organizations with distributed architectures, microservices, or multi-cloud environments often struggle to track reliability across multiple sources. Composite SLOs address this challenge by:

• Combining multiple SLOs into a single reliability measure.
• Improving decision-making by weighing critical components more heavily.
• Enhancing error budget tracking with detailed visualizations.

Step-by-step

Learn about prerequisites, access requirements, and different ways to create a composite SLO.

Step1: Define general settings

• Choose a name and description for your composite SLO.
• Set an evaluation time window—rolling or calendar-aligned.
• Select a budgeting method—time-based time slices or event-based occurrences.

Step 2: Add component SLOs

Instead of writing queries, you choose existing SLOs and configure their impact on your composite SLO

• Assign weights to each component to define its importance.
  The weights you enter are mutually relative—Nobl9 normalizes them for calculations.
• Specify how to handle delayed data from a component:
  • Set the maximum acceptable delay for the entire composite SLO.
    It defines the interval that must end before applying the below setting to missing data from a component.
  • For each component, select how to treat this missing data.
    Consider it good (adds to the composite's error budget), bad (burns the composite's error budget), or ignore it (no impact on your composite's error budget).

Adding components

Step 3: Assess your composite SLO

• Use visualizations to track your system performance—charts, information tiles, and the component impact graph.
• Include your composite SLOs in reports and monitor your service health with the dashboards.

Composite SLO charts and the Component impact graph

Tips on naming and structuring

Multi-cloud monitoring

A SaaS company running services on AWS, Azure, and Google Cloud needs a centralized reliability SLO to track system health efficiently.

Name example: Use a clear name—Multi-cloud composite SLO

Component breakdown: Aggregate uptime and latency SLOs from each provider.

Component weight: Assign higher weights to mission-critical cloud services based on traffic and dependencies.

Microservices observability

An e-commerce company with multiple microservices—checkout, payments, and inventory management—uses composite SLOs that prioritizes customer-impacting services.

Name example: Emphasize user experience—E-commerce transaction health SLO.

Component breakdown: Include SLOs for critical microservices such as checkout response time, payment processing success rates, and inventory sync accuracy.

Component weight: Assign higher weights to components that directly affect transaction completion.

Best practices for composite SLOs

Choose SLO components wisely—focus on metrics that truly impact reliability.

Balance weight distribution—avoid over-prioritizing one component to maintain accuracy.

Account for delayed data—define handling rules to prevent false alarms.

Monitor trends over time—use dashboards to identify patterns and potential risks.

Continuously refine SLOs—adjust weightings and thresholds as your system evolves.

Conclusion

Composite SLOs simplify site reliability engineering (SRE) monitoring, making it easier to track performance across multiple data sources. By structuring composite SLOs effectively, teams can improve error budget tracking, streamline observability, and prioritize system health.

Try implementing a composite SLO today—experiment with different weight configurations and monitor the impact on your error budget

related topics

• Composite SLOs
• Composite SLOs troubleshooting