10 Site Reliability & SRE Project Ideas (2026)

In this tutorial, you'll learn about 10 site reliability & sre project ideas (2026). We cover key concepts, practical examples, and best practices to help you understand and apply this topic effectively.

Site Reliability Engineering bridges software engineering and operations to build scalable, reliable systems. These 10 projects teach you how to implement monitoring, Incident Response, capacity planning, Chaos Engineering, and SLO-based alerting. Each project simulates real production scenarios so you can practice reliability patterns safely.

graph LR
    A[Monitoring & Alerting] --> B[SLI/SLO Frameworks]
    B --> C[Incident Response Tools]
    C --> D[Chaos Engineering & Automation]
    style A fill:#e6f3ff,stroke:#333,stroke-width:2px
    style B fill:#cce5ff,stroke:#333,stroke-width:2px
    style C fill:#99ccff,stroke:#333,stroke-width:2px
    style D fill:#66b3ff,stroke:#333,stroke-width:2px

1. Uptime Monitoring Service — Difficulty: Intermediate Tech stack: Node.js or Go, React, PostgreSQL, Twilio/SendGrid Build an uptime monitoring service that checks HTTP endpoints at configurable intervals and alerts when they go down. Step by step: design check scheduler with configurable intervals; implement HTTP health checks with timeout handling; build alert escalation (email, SMS, Slack); create status dashboard with uptime percentage; implement maintenance window suppression.

2. SLI/SLO Dashboard Builder — Difficulty: Advanced Tech stack: Python or Go, Prometheus, Grafana, React Build a service level indicator dashboard that tracks availability, latency, throughput, and error budgets. Step by step: define SLI data model with metric sources; implement Prometheus metric collection and recording rules; calculate SLO compliance over rolling windows; build error budget burn rate alerts; create Grafana dashboard with SLO panels; implement multi-service comparison view.

3. Incident Management Platform — Difficulty: Advanced Tech stack: Next.js, PostgreSQL, WebSocket, Slack API Build an incident management platform for declaring, tracking, and resolving incidents. Step by step: design incident lifecycle (detected, triaged, mitigated, resolved); implement incident declaration with severity classification; build real-time incident timeline with updates; add Slack integration for automatic channel creation; implement post-incident review template generator.

4. Capacity Planning Simulator — Difficulty: Advanced Tech stack: Python, pandas, React, Chart.js Build a tool that forecasts resource needs based on traffic patterns. Step by step: ingest historical traffic data (requests per second, CPU, memory); implement trend analysis with linear regression and exponential smoothing; build what-if scenario modeling (2x traffic, holiday spike); create resource cost projection view; generate capacity recommendation report.

5. Chaos Engineering Experiment Runner — Difficulty: Advanced Tech stack: Python, Kubernetes API, Litmus or Chaos Mesh, Go Build a Chaos Engineering platform that runs experiments against a Kubernetes cluster and measures system resilience. Step by step: design experiment definition format (steady-state hypothesis, blast radius, duration); implement pod kill and network latency experiments; add steady-state metric validation before and after; build experiment results dashboard with pass/fail; implement automated experiment rollback.

6. Distributed Tracing Visualization — Difficulty: Advanced Tech stack: Jaeger/OpenTelemetry, Python or Node.js, React, D3.js Build a distributed tracing viewer that shows request flows across microservices with latency breakdowns. Step by step: instrument sample microservices with OpenTelemetry; export traces to Jaeger backend; build custom trace viewer with waterfall layout; add service dependency graph; implement latency distribution histogram; add trace search by service, operation, and duration.

7. Auto-Remediation Playbook Engine — Difficulty: Advanced Tech stack: Go or Python, Kubernetes, Ansible, Slack/Webhook Build an engine that automatically responds to common incidents by executing remediation playbooks. Step by step: define playbook format (trigger condition, steps, rollback); implement event listener for alerts; execute remediation actions (restart pod, scale up, clear cache); add safety gates and dry-run mode; build playbook execution history; implement escalation if auto-remediation fails.

8. Error Budget Tracker — Difficulty: Intermediate Tech stack: React, Node.js, PostgreSQL, Prometheus API Build a tool that tracks error budget consumption across services and alerts when budgets are depleted. Step by step: integrate with Prometheus to collect SLI data; calculate error budget (1 - SLO) over monthly window; build error budget burn rate chart; implement alert when budget is 50% and 90% consumed; add deployment gate check; create team-level error budget reports.

9. Load Testing & Benchmarking Platform — Difficulty: Intermediate Tech stack: k6 or Locust, Node.js or Python, React, InfluxDB Build a platform that runs load tests against services and reports performance benchmarks. Step by step: create test scenario definitions with k6/Locust; run load tests with configurable concurrency and duration; collect latency percentiles (p50, p95, p99); build comparison view across test runs; add regression detection with threshold alerts; generate performance report PDF.

10. On-Call Schedule & Alert Routing — Difficulty: Intermediate Tech stack: React, Node.js, PostgreSQL, Twilio, PagerDuty API Build an on-call scheduling system that routes alerts to the right engineer based on schedule, expertise, and escalation policies. Step by step: design calendar-based scheduling with rotation rules; implement alert routing by severity and service; add escalation policies with timeouts; build mobile-friendly on-call view; implement override and swap functionality; add alert acknowledgment tracking.

Practice Questions

1. Your service has a 99.9% SLO but has been at 99.7% for the past week. How do you calculate the remaining error budget and what actions do you take?
2. Design an incident severity classification system (SEV1 through SEV4) with response time targets and escalation paths for each level.
3. A Chaos Engineering experiment caused a real production outage. What went wrong in the experiment design and how would you prevent it?

FAQ

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