Site Reliability Engineering: SLIs, SLOs, and Monitoring Best Practices

Your application is down. Again. Your team is firefighting. Again. Customers are angry. Again. You promise "this won't happen again"—but it does.

This is what happens without Site Reliability Engineering (SRE). You're reactive instead of proactive. You fix symptoms instead of root causes. You measure nothing, so you improve nothing.

SRE changes this. It's how Google, Netflix, and Amazon achieve 99.99% uptime while deploying hundreds of times per day. In this guide, I'll show you how to implement SRE practices that actually work.

What is Site Reliability Engineering?

SRE is what happens when you treat operations as a software engineering problem. Instead of manual firefighting, you build systems that are reliable by design.

Core SRE Principles:

• Embrace risk: 100% uptime is impossible and unnecessary
• Service Level Objectives: Define acceptable reliability
• Eliminate toil: Automate repetitive work
• Monitor everything: You can't improve what you don't measure
• Blameless postmortems: Learn from failures

SRE vs Traditional Ops:

Traditional Ops	SRE
Manual processes	Automated systems
Reactive firefighting	Proactive prevention
Blame culture	Blameless postmortems
Ops vs Dev silos	Shared responsibility
Stability over speed	Balance reliability and velocity

Understanding SLIs, SLOs, and SLAs

SLI (Service Level Indicator)

What it is: A quantitative measure of service quality

Examples:

• Request success rate: 99.5% of requests return 200 OK
• Request latency: 95% of requests complete in < 200ms
• Availability: Service is reachable 99.9% of the time
• Throughput: System handles 1000 requests/second

How to choose SLIs:

• Focus on user experience, not internal metrics
• Measure what matters to customers
• Keep it simple: 3-5 SLIs per service

SLO (Service Level Objective)

What it is: Target value for an SLI

Examples:

• 99.9% of requests succeed (SLI: success rate)
• 95% of requests complete in < 200ms (SLI: latency)
• Service is available 99.95% of the time (SLI: availability)

How to set SLOs:

• Start with current performance
• Don't aim for 100% (it's impossible and expensive)
• Balance customer expectations with cost
• Make them achievable but challenging

SLA (Service Level Agreement)

What it is: Legal contract with consequences for missing SLOs

Example:

• We guarantee 99.9% uptime
• If we miss it, you get 10% credit
• If we miss 99%, you get 25% credit

Key rule: SLA should be less strict than SLO. If your SLO is 99.9%, your SLA might be 99.5%. This gives you buffer.

Error Budgets: The Game Changer

Error budget is the amount of unreliability you can tolerate. It's calculated from your SLO.

Example Calculation:

SLO: 99.9% availability

Error budget: 0.1% downtime = 43 minutes per month

If you use up your error budget:

• Stop new feature releases
• Focus 100% on reliability
• Fix the root causes
• Only resume features when budget recovers

Why Error Budgets Work:

• Balances innovation and stability
• Gives teams objective decision-making framework
• Prevents endless reliability work
• Aligns dev and ops incentives

The Monitoring Stack Explained

Layer 1: Metrics (Prometheus)

What: Time-series data about system behavior

Examples: CPU usage, request rate, error rate, latency

Why Prometheus:

• Open-source and widely adopted
• Pull-based model (scrapes metrics)
• Powerful query language (PromQL)
• Excellent Kubernetes integration

Layer 2: Visualization (Grafana)

What: Dashboards to visualize metrics

Why Grafana:

• Beautiful, customizable dashboards
• Supports multiple data sources
• Alerting capabilities
• Large community and pre-built dashboards

Layer 3: Logs (ELK Stack or Loki)

What: Detailed event records

Options:

• ELK: Elasticsearch, Logstash, Kibana (powerful but heavy)
• Loki: Grafana's log aggregation (lighter, integrates with Grafana)

Layer 4: Tracing (Jaeger)

What: Track requests across microservices

Why: Essential for debugging distributed systems

Layer 5: Alerting (Alertmanager)

What: Notify team when things go wrong

Integrations: PagerDuty, Slack, email

Essential Metrics to Monitor

The Golden Signals (Google SRE):

1. Latency

How long does it take to serve a request?

• Track p50, p95, p99 percentiles
• Alert if p95 > 500ms

2. Traffic

How much demand is on your system?

• Requests per second
• Concurrent users

3. Errors

What's the rate of failed requests?

• HTTP 5xx errors
• Failed database queries
• Alert if error rate > 1%

4. Saturation

How full is your service?

• CPU usage
• Memory usage
• Disk I/O
• Alert if > 80%

Additional Important Metrics:

• Availability: % of time service is up
• Success rate: % of requests that succeed
• Queue depth: Backlog of work
• Database connections: Connection pool usage

Incident Management

Incident Severity Levels:

SEV 1 (Critical):

• Service completely down
• Data loss or security breach
• Response: Immediate, all hands on deck

SEV 2 (High):

• Major feature broken
• Significant performance degradation
• Response: Within 30 minutes

SEV 3 (Medium):

• Minor feature broken
• Workaround available
• Response: Within 4 hours

SEV 4 (Low):

• Cosmetic issues
• No user impact
• Response: Next business day

Incident Response Process:

Blameless Postmortems:

The goal is learning, not punishment.

Postmortem Template:

• Summary: What happened in one paragraph
• Timeline: Detailed sequence of events
• Root cause: Why it happened
• Impact: How many users affected, for how long
• What went well: Positive aspects
• What went wrong: Areas for improvement
• Action items: Specific tasks to prevent recurrence

On-Call Best Practices

On-Call Rotation:

• 1-week rotations (not longer)
• Primary and secondary on-call
• Handoff meetings between rotations
• Compensate on-call time fairly

Reducing On-Call Burden:

• Fix root causes, not symptoms
• Automate common responses
• Improve monitoring to reduce false positives
• Document runbooks for common issues
• Set up self-healing systems

On-Call Runbooks:

For each alert, document:

• What the alert means
• How to investigate
• Common causes
• How to fix
• When to escalate

Achieving 99.9% Uptime

What 99.9% Means:

• 43 minutes of downtime per month
• 8.7 hours per year
• 1.4 minutes per day

How to Get There:

1. Eliminate Single Points of Failure

• Deploy across multiple availability zones
• Use load balancers
• Replicate databases
• Have backup systems

2. Implement Health Checks

• Application health endpoints
• Database connectivity checks
• Dependency health checks
• Automatic removal of unhealthy instances

3. Use Circuit Breakers

• Prevent cascading failures
• Fail fast when dependencies are down
• Automatic recovery when dependencies recover

4. Implement Graceful Degradation

• Core features work even if some services fail
• Cache aggressively
• Serve stale data rather than errors

5. Test Failure Scenarios

• Chaos engineering (Netflix's Chaos Monkey)
• Regular disaster recovery drills
• Load testing
• Failure injection testing

Business Impact of Reliability

Cost of Downtime:

Company Size	Cost per Hour
Small (< 50 employees)	$10,000 - $50,000
Medium (50-500 employees)	$50,000 - $250,000
Large (500+ employees)	$250,000 - $1,000,000+

Benefits of High Reliability:

• Customer trust: Users rely on your service
• Revenue protection: No lost sales due to downtime
• Competitive advantage: More reliable than competitors
• Team morale: Less firefighting, more building
• Faster innovation: Confidence to deploy frequently

Getting Started with SRE

Week 1-2: Foundation

• Define your SLIs
• Set initial SLOs (based on current performance)
• Calculate error budgets
• Set up basic monitoring (Prometheus + Grafana)

Week 3-4: Monitoring

• Instrument your application
• Create dashboards
• Set up alerts
• Configure on-call rotation

Month 2: Process

• Document incident response process
• Create runbooks
• Conduct first postmortem
• Start tracking error budget

Month 3+: Optimization

• Eliminate toil through automation
• Improve SLOs gradually
• Reduce MTTR (Mean Time To Recovery)
• Build self-healing systems

Conclusion

SRE isn't just about keeping systems running—it's about building systems that are reliable by design. It's about balancing innovation with stability. It's about learning from failures instead of hiding them.

Start small. Pick one service. Define SLIs and SLOs. Set up monitoring. Respond to incidents systematically. Learn from postmortems. Improve continuously.

In 6 months, you'll have transformed from reactive firefighting to proactive reliability engineering. Your systems will be more stable, your team will be happier, and your customers will trust you more.

Contact us: kloudsyncofficial@gmail.com | +91 9384763917

Related Articles:
DevOps Automation Guide | Kubernetes Monitoring | DevOps Mistakes to Avoid