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Start observability by deciding what success looks like for users and the business—not by collecting infrastructure metrics and hoping they explain it. Define a measurable outcome, choose a service-level indicator (SLI) that reflects it, and then use technical telemetry to understand what is helping or hurting that result.
Why begin with business outcomes?
Infrastructure health matters, but it is not the same as service success. A server can appear healthy while customers cannot complete a purchase; a rise in CPU use may be harmless if users still complete their tasks. An outcome-oriented approach gives teams a way to spot problems in terms stakeholders recognize, while technical signals help explain what happened.
AWS Well-Architected says KPI selection should begin with the workload’s desired business outcomes and then connect technical metrics to business objectives. It flags KPIs that are undefined, static, or misaligned with current goals as anti-patterns. AWS DevOps Guidance likewise recommends aligning observability with both business and technical goals. AWS Well-Architected KPI guidance and AWS DevOps Guidance provide the rationale.
Choose an outcome that fits the workload
Ask product, operations, and business stakeholders what result the service is meant to deliver. There is no single KPI that suits every service. Possible outcomes include completed transactions, engagement with a feature, availability of a critical workflow, or customer satisfaction. For an online store, AWS gives orders per minute as an example business KPI; another service may need a different measure.
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Make the outcome concrete enough to guide a decision. “Improve reliability” is too vague on its own. Specify which user journey matters, what counts as success or failure, and what level of performance the organization intends to maintain.
Turn the outcome into an SLO and an SLI
A service-level objective (SLO) states the measurable outcome or target the service aims to meet. A service-level indicator (SLI) is the measurement used to assess progress against that objective. For example, if the promise is that customers can complete checkout, an SLI could measure the share of checkout attempts completed successfully. The precise definition should reflect the actual user promise, not merely whichever metric is easiest to collect.
Google Cloud describes SLIs as useful proxy measures for user happiness. Its guidance recommends choosing an implementation by balancing fidelity to the user’s experience, coverage of relevant interactions, and cost. A measurement taken closer to the user generally offers higher fidelity, though it may require more implementation effort or expense. Google Cloud’s SLI overview explains these trade-offs.
Illustrative targets are not universal benchmarks
AWS Prescriptive Guidance offers example targets such as reducing mean time to recovery (MTTR) by 60 percent, maintaining application availability at 99.99 percent, or improving developer productivity by 30 percent. These are examples for defining a North Star, not observed results or recommended targets for every team. Set targets according to the service, users, and business priorities. See AWS Prescriptive Guidance on defining a North Star.
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Measure the user journey, then retain diagnostic signals
Where practical, instrument the application and user journey so the SLI reflects what users actually experience. For a page-load-time objective, possible measurement points include server request logs, application-server metrics, load-balancer metrics, synthetic checks, or browser-side instrumentation. They differ in how closely they represent the user’s experience, how much of the journey they cover, and their cost in money and engineering time.
Keep technical KPIs alongside the outcome measure. AWS DevOps Guidance identifies latency, traffic, errors, and saturation as useful technical measures for user-facing systems. They help teams locate causes and dependencies when an outcome changes; they are not substitutes for measuring whether the service delivered what users needed. AWS Well-Architected also identifies metrics, logs, and traces as primary observability signals, and notes that application telemetry can help evaluate a feature’s impact and its alignment with business KPIs. See AWS guidance on application telemetry.
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Review business and technical measures together
When an outcome KPI changes, use technical telemetry to investigate rather than treating correlation as proof of cause. Check which user journeys, releases, dependencies, and operating conditions changed around the same time. A latency increase may explain a fall in completed transactions, but teams need evidence from the affected journey and its dependencies before attributing the change to latency.
Set measurement windows to suit the decision. Google Cloud recommends 28 days as a starting point for measuring an SLI, not as a mandatory period. Shorter windows can be useful for alerting; longer windows can support tactical or strategic decisions. Choose a window that captures enough relevant behavior for the question at hand.
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Revisit the KPIs as priorities change
A KPI that once represented success can become stale as a product, workload, or business priority evolves. Review whether each measure still reflects a meaningful user outcome, whether its SLI is a credible proxy, and whether the technical signals help explain changes. AWS associates disconnected observability signals with longer times to identify and recover from incidents, as well as possible harm to user experience, trust, brand reputation, and revenue. AWS Prescriptive Guidance on observability outcomes describes these risks.
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