Reliability Unleashed
Strategic Framework
From Chaos to Confidence
Site Reliability Engineering for Business Leaders
Executive Track | ~60 Minutes | Strategic Overview
The Business Case for Reliability
$400K
Average cost per hour
of downtime
79%
Customers leave after
bad experience
5x
Cost to acquire new
vs retain existing
Reliability isn't just an engineering problem.
It's a business differentiator and competitive advantage.
What is Site Reliability Engineering?
"SRE is what happens when you ask a software engineer to design an operations function."
— Ben Treynor, VP Engineering, Google
Traditional Ops
- Manual, reactive
- Scaling = more people
- Unmeasured reliability
- Dev vs Ops tension
SRE Approach
- Automated, proactive
- Scaling = better software
- Data-driven SLOs
- Shared responsibility
The Research: DORA Metrics
10+ years of research, 39,000+ participants
Deploy Freq
LowMonthly
HighDaily
EliteOn-demand
Lead Time
LowMonths
HighDays
Elite<1 hour
Failure Rate
Low>30%
High5-15%
Elite<5%
Recovery
LowWeeks
High<1 day
Elite<1 hour
Key insight: Elite performers ship faster AND more reliably
SLOs: Making Reliability Measurable
SLI
Service Level Indicator
What we measure
SLO
Service Level Objective
Target we aim for
Error Budget
Acceptable unreliability
Investment capacity
>50% budget Ship features
25-50% Prioritize reliability
<25% Feature freeze
The Cost of Nines
| Availability |
Downtime/Year |
Relative Cost |
Use Case |
| 99% (2 nines) | 3.65 days | $ | Internal tools |
| 99.9% (3 nines) | 8.76 hours | $$ | Business apps |
| 99.95% | 4.38 hours | $$$ | Customer-facing |
| 99.99% (4 nines) | 52.6 min | $$$$ | Core platform |
| 99.999% (5 nines) | 5.26 min | $$$$$ | Life-critical |
Each additional nine roughly 10x the cost. Choose wisely.
Learning from Industry Leaders
Google
Invented SRE, error budgets, 50% toil cap
Netflix
Chaos engineering, test in production
Amazon
Cell-based architecture, blast radius
Meta
SEV culture, move fast safely
Stripe
99.999% uptime, defensive design
Spotify
Golden paths, platform engineering
High-Reliability Organizations
Lessons from Aviation, Nuclear, Healthcare, NASA
1 Preoccupation with Failure — Never ignore small failures
2 Reluctance to Simplify — Embrace complexity, don't oversimplify
3 Sensitivity to Operations — Real-time situational awareness
4 Commitment to Resilience — Detect, contain, recover quickly
5 Deference to Expertise — Let experts decide, regardless of rank
The Observability Investment
"If you can't monitor a service, you can't be reliable."
— Google SRE Book
Metrics
What's happening?
Traces
Why did it happen?
ROI: Faster detection → Faster resolution → Less downtime
Incident Management: The ROI
Without Process
- Chaos during incidents
- Unclear ownership
- Same issues recur
- Blame culture
MTTR: 4+ hours
With SRE Process
- Structured response
- Clear roles (IC, Comms)
- Blameless postmortems
- Learning culture
MTTR: <1 hour
Culture: The Hidden Multiplier
Ron Westrum's Organizational Culture Types
Pathological
- Information is power
- Messengers are shot
- Failure leads to blame
- New ideas are crushed
Bureaucratic
- Information is controlled
- Messengers are tolerated
- Failure leads to justice
- New ideas create problems
Generative
- Information is shared
- Messengers are trained
- Failure leads to inquiry
- New ideas are welcomed
DORA research: Generative culture predicts software delivery performance
Cloud Strategy: Reliability Implications
On-Premises
- Full control, full responsibility
- Capital expenditure model
- Limited geographic distribution
- Predictable costs at scale
Public Cloud
- Shared responsibility model
- Operational expenditure
- Global distribution possible
- Variable costs, auto-scaling
Key DecisionMatch SLO to platform capability
Risk FactorVendor lock-in vs portability
AI/ML: New Reliability Challenges
Traditional Software
- Deterministic behavior
- Clear failure modes
- Static once deployed
- Easy to test
AI/ML Systems
- Non-deterministic output
- Subtle degradation
- Model drift over time
- Harder to validate
New metrics needed:
Model accuracy, prediction confidence, data quality, inference latency
Agentic Operations: The Future
1
Detect
AI anomaly detection
2
Diagnose
Automated analysis
3
Remediate
Execute runbooks
Platform Engineering: Developer Productivity
"A golden path is a paved road to a well-architected production deployment."
— Spotify Engineering
Without Platform
Each team reinvents deployment, monitoring, security. Weeks to production.
With Platform
Self-service, paved paths, built-in best practices. Hours to production.
Make the right thing the easy thing
Investment Priorities by Maturity
1
Foundation
Monitoring, on-call
2
Measurement
SLOs, error budgets
3
Automation
CI/CD, remediation
5
Intelligence
AI/ML, agentic
Each phase builds on the previous. Don't skip steps.
Measuring SRE ROI
Availability Gains
- Reduced downtime cost
- Fewer customer impacts
- Less revenue at risk
Velocity Gains
- Faster time to market
- More deployments/day
- Shorter lead times
Efficiency Gains
- Less manual toil
- Reduced on-call burden
- Better resource use
People Gains
- Lower attrition
- Higher engagement
- Better recruitment
Strategic Anti-Patterns to Avoid
Reliability as Afterthought
"We'll make it reliable after we ship" - technical debt compounds
Tool-First Thinking
"Let's buy Kubernetes" - tools don't solve culture/process problems
Over-Engineering SLOs
"We need 99.999%" - costs escalate, value doesn't
Blame Culture
"Find who caused this" - kills learning, hides problems
Key Takeaways for Leaders
1
Reliability = Business Feature — impacts revenue & retention
2
Measure What Matters — SLOs & DORA enable decisions
3
Culture is the Multiplier — predicts delivery performance
4
Invest Progressively — foundation → automation → intelligence
5
Future is Agentic — autonomous ops reduce cost
Reliability Unleashed
Questions?
34 Detailed One-Pagers Available
Technical deep-dives covering all topics discussed today
Essential Reading
Google SRE Book
Accelerate (DORA)
Next Steps
Assess current maturity
Define SLOs
Build roadmap