• SRE is not about hope-based reliability.
• It is about numbers, thresholds, and consequences.
• At the core of SRE are SLIs, SLOs, and SLAs.
• If you cannot measure it, you cannot make it reliable.

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🎯 Big Picture (Mental Model)

• 🟢 SLIs → What we measure
• 🟡 SLOs → What we aim for
• 🔴 SLAs → What we promise legally

SLIs feed SLAs, and SLAs define SLOs

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📏 Service Level Indicators (SLIs)

✔ Raw measurements of service behavior

SLIs are quantitative metrics that describe how a service behaves from the user’s perspective.

🔍 Common SLI Types

SLI Type	What It Measures
🟢 Availability	Was the service reachable
⚡ Latency	How fast responses are
❌ Error Rate	How many requests failed
📦 Throughput	Requests per second
🔄 Freshness	Data staleness

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✅ Example SLIs (API Service)

Availability SLI = Successful requests / Total requests
Latency SLI = % of requests under 300ms
Error Rate SLI = 5xx responses / Total requests

📌 SLIs do not define targets. They only provide truthful signals.

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🎯 Service Level Objectives (SLOs)

✔ Target reliability goals

SLOs define how reliable the service must be.

They are engineering targets, not legal contracts.

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🔢 Example SLOs

Availability SLO: 99.9% monthly uptime
Latency SLO: 95% of requests under 300ms
Error Rate SLO: Less than 0.1% failed requests

📌 SLOs are based on user expectations, not perfection.

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🔥 Error Budget (Why SLOs Matter)

99.9% uptime = 43.2 minutes of downtime per month

That downtime is your error budget.

If error budget exists	If error budget is exhausted
🚀 Ship features	🛑 Freeze releases
🧪 Experiment	🔧 Focus on stability

This is SRE discipline in action.

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📜 Service Level Agreements (SLAs)

✔ Legal and business commitments

SLAs are contracts with customers.

They reference SLIs and define:

• Acceptable performance
• Measurement windows
• Penalties or credits

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🧾 Example SLA Clause

The service will maintain 99.5% monthly availability.
If availability falls below 99.5%, customers receive a 10% service credit.

📌 SLAs are intentionally less strict than SLOs.

Why? Because breaking an SLA costs money and trust.

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🔗 How SLIs, SLOs, and SLAs Connect

SLI → Measured data
SLA → Contractual minimums using SLIs
SLO → Internal reliability target set above SLA

🧠 Visual Flow

📊 SLIs (metrics)
↓
📜 SLAs (legal promises)
↓
🎯 SLOs (engineering goals)

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🏗️ Real-World Example (E-commerce App)

📊 SLIs

• Availability: Successful HTTP responses
• Latency: Request duration
• Error Rate: 5xx responses

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📜 SLA (Customer-Facing)

99.5% monthly availability

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🎯 SLO (Engineering Target)

99.9% monthly availability
95% requests < 250ms
Error rate < 0.1%

Why higher than SLA?

✔ Buffer for incidents ✔ Protect customer trust ✔ Avoid financial penalties

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❌ Common Mistakes (Callout)

🚫 Setting SLOs without SLIs 🚫 100% uptime targets 🚫 SLAs tighter than SLOs 🚫 Measuring system metrics instead of user experience

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✅ SRE Golden Rules

• Measure what users feel
• Target less than perfect
• Use error budgets to guide decisions
• Protect engineers from endless firefighting

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🏁 Final Takeaway

SLIs tell the truth SLOs define reliability goals SLAs define consequences

This trio is what turns reliability from wishful thinking into engineering discipline 💪

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