A collection templates ported from the SRE Workbook
A simple service level calculator
Calculator to view detection time using error budget consumption rates, based on lessons from Site Reliability Engineering Workbook
Stop burning your error budget. SRE config as code — SLOs, error budgets, runbooks, on-call, and dashboards in one sre.yaml file. Auto-remediates before alerts fire.
Error budget tracker for SLI files: burn rate windows, alert thresholds, and deterministic reports.
端到端 SLO (服务级别目标) 治理平台，集成 MeterSphere 拨测数据，自动计算误差预算，并由 AI 辅助输出结构化诊断报告
Материалы по SRE: принципы надёжности, наблюдаемость, инциденты и метрики. Разборы подходов и вопросы для подготовки к собеседованию.
Azure-native SLO/SLI engine with error budget tracking, burn-rate alerts, and CLI for Azure Monitor, Application Insights & Log Analytics
Identifies underutilized service-level objectives by detecting SLOs that consistently maintain error budget thresholds,generating actionable Excel reports with historical performance trends and optimization recommendations.
Azure SLO Dashboard with AI-powered error budget explainer — Application Insights + Container Apps + Claude
SRE on AWS: SLOs/SLAs/error budgets, runbooks, incident response playbooks, chaos engineering, observability stack
SLO-based alerting for Kubernetes - burn rate alerts, policy guardrails and an error budget release gate, all unit tested without a cluster
Error-budget-driven release gating for Prometheus: one SLO spec generates burn-rate alerts, a Grafana dashboard, and a CI/CD gate that freezes deploys when you're out of budget. Go.
Hands-on SRE practice lab for learning golden signals, observability, SLI/SLOs, error budgets, tracing, resilience, and capacity planning.
Production-inspired Site Reliability Engineering practices covering SLIs, SLOs, incident management, reliability engineering, operational excellence, alerting, and disaster recovery.
Audit API SLO and error-budget docs for targets, SLIs, burn-rate alerts, incidents, exclusions, status reporting, telemetry, owners, and rollout safety.
Multi-window multi-burn-rate SLO alerting over spans, with an honest 3-regime benchmark: a naive threshold detects real regressions faster, but raises 3 false pages on healthy spiky traffic where multi-window raises 0. The value is precision, not speed, and the benchmark proves it both ways.
LLM telemetry you can gate on: cost accounting against a versioned price book, multi-window burn-rate error budgets, and a command that exits non-zero when an objective is missed.
SLO tracker with multi-window burn rate alerting — Google SRE Workbook model in Python
To associate your repository with the error-budget topic, visit your repo's landing page and select "manage topics."