Site Reliability Engineering as a Service

We define Service Level Objectives for your critical user journeys based on historical performance data and business requirements. Each SLO has measurable Service Level Indicators — request latency at p95 and p99, availability measured at the user level, error rates by endpoint, and throughput for batch workloads.

Error budgets translate SLOs into actionable engineering decisions. When the error budget is healthy, your team ships fast. When the budget is consumed, feature work pauses to focus on reliability improvements. We build the dashboards, alerts, and policies that make this framework work in practice — not just in theory.

We design and implement a structured incident management process covering detection, response, communication, resolution, and post-incident review. On-call rotations are configured with escalation policies, paging rules, and handoff procedures that prevent burnout while ensuring coverage.

Every incident triggers an automated workflow — severity classification, communication channel creation, stakeholder notification, and timeline tracking. Post-incident reviews follow a blameless format focused on systemic improvements rather than individual mistakes. Action items are tracked to completion, and recurring issues trigger architectural reviews.

Toil is manual, repetitive operational work that scales linearly with service growth and provides no lasting value. We audit your operational tasks, quantify toil hours, and prioritize automation investments. Common targets include manual deployments, certificate rotations, capacity adjustments, log cleanup, and incident remediation steps that follow a predictable runbook.

We track toil metrics over time, measuring the percentage of engineering hours spent on operational work versus feature development. Every automation we build reduces the toil percentage and returns engineering capacity to your team. The goal is keeping toil below 50% of on-call time — the threshold recommended by Google's SRE book.

We build capacity models that predict resource requirements based on growth trends, seasonal patterns, and planned feature launches. Load testing validates that your infrastructure handles projected traffic with acceptable performance margins. Scaling decisions are made proactively based on data, not reactively based on outages.

Performance budgets define acceptable latency and throughput targets for each service. Regressions are caught in CI/CD through automated performance tests that compare against baseline measurements. Your team ships with confidence knowing that performance is monitored continuously and regressions are flagged before they reach production users.