{
  "id": 13516251,
  "title": "Defining SLOs and SLIs for Microservices",
  "url": "https://urgent.news/2026/10/10/defining-slos-and-slis-for-microservices",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-10T20:07:17.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/beefedai/defining-slos-and-slis-for-microservices-4mg0"
  },
  "original_language": "en",
  "account": "Defining Service Level Objectives (SLOs) and Service Level Indicators (SLIs) for microservices involves translating business outcomes into measurable technical metrics. This process helps ensure that technical decisions align with business goals and enables teams to make data-driven decisions about deployments, rollbacks, and performance optimizations.\n\nTo define effective SLIs, begin with the user outcomes that matter most. For example, the business outcome \"customers complete checkout\" can be represented by an SLI such as checkout success rate, calculated as the ratio of successful orders to checkout attempts over a specific time period, such as a rolling 30-day window.\n\nWhen choosing SLIs, consider engineering constraints to ensure they are user-focused, low-noise, and low-cardinality. Avoid metrics with excessive label values that can make the data unreadable. Use counters for successful and failed events, and compute ratios with the `rate()` function in PromQL to handle counter resets accurately. For latency measurements, use histograms to calculate percentiles server-side, as client-side Summary aggregations may not provide the needed global quantiles.\n\nIt's crucial to define eligibility criteria for events included in the SLI, excluding irrelevant or non-representative events, such as login attempts from test harnesses. Clearly document these eligibility rules in the SLO documentation to maintain transparency and ensure that the error budget accurately reflects the product team's priorities.\n\nWhen establishing SLO targets, consider the business impact of outages and the risk tolerance of the organization. Avoid setting overly stringent targets that may lead to unsustainable designs and slow deployment velocities. Instead, aim for targets that reflect a balanced approach to reliability and business impact.\n\nCalculating error budgets involves determining the allowed number of errors within a given timeframe. For example, an SLO of 99.9% allows for 1,000 errors in a window of 1,000,000 eligible requests. By expressing these calculations in PromQL, teams can automate the monitoring and alerting processes, ensuring that the system remains within the defined error budget.\n\nRecording rules in Prometheus can help optimize the performance of these calculations, running the expensive PromQL queries once and reusing the results across dashboards and alerts. This approach not only improves efficiency but also ensures that the data displayed to stakeholders is accurate and up-to-date.",
  "summary": "How to translate business outcomes into measurable SLIs Choosing SLIs that survive production reality Practical SLO targets, error budgets, and burn-rate policies SLO-driven monitoring, alerts, and runbooks with Prometheus & Grafana SLO/SLI implementation checklist you can apply today SLOs force the business to choose what reliability costs. SLIs are the measurable signals you use to enforce that…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}