The Downtime Cost Formula
Before investing in HA infrastructure, calculate what you're protecting against. The true cost of downtime has four components:
- Revenue loss: Direct sales lost while the system is down
- Productivity cost: Employees unable to work × hourly rate × duration
- Recovery cost: DBA and ops time to diagnose, restore, verify
- Reputational cost: Customer churn, SLA penalties, brand damage (hardest to quantify)
A mid-sized e-commerce company processing $50K/hour doesn't just lose $50K in an hour of downtime. Add 20 employees idled ($3K/hr), 3 DBAs on emergency recovery ($500/hr), and 0.5% customer churn on 10,000 customers ($50K LTV × 50 = $2.5M). That's a very different number.
The Common Downtime Causes
Based on industry data, database downtime breaks down roughly as:
- 42% — Planned maintenance (patching, upgrades, schema changes)
- 28% — Hardware failure
- 18% — Human error (bad deployment, accidental data deletion)
- 12% — Software bugs / vendor issues
Nearly half of "downtime" is self-inflicted. The good news: that's controllable.
HA Tiers: Match the Solution to the Cost
Not every database warrants Always On AGs. Match your HA investment to your actual downtime cost:
- Tier 1 (RTO < 1 min, RPO ~0): Always On Synchronous + automatic failover. Cost: 2x+ server infrastructure.
- Tier 2 (RTO < 15 min, RPO < 5 min): Always On Asynchronous + manual failover. Lower storage overhead.
- Tier 3 (RTO < 4 hrs, RPO < 1 hr): Log shipping or AG async replica in DR site. Minimal cost.
- Tier 4 (RTO < 24 hrs, RPO < 24 hrs): Nightly backup to offsite. Cheapest. Only appropriate for truly non-critical systems.
Preventing Planned Downtime
Planned maintenance is your biggest opportunity. Best practices:
- Use online index rebuild (
WITH (ONLINE = ON)) instead of offline rebuilds - Use Always On to patch the secondary first, then fail over, then patch the old primary
- Test all schema changes in staging with production-scale data and load
- Deploy at 2 AM, not 2 PM—even with "zero downtime" deployments
The Change Management Gap
Most production incidents are caused by changes, not spontaneous failures. Every schema change, stored procedure modification, or index change should go through:
- Peer review
- Dev/staging deployment first
- Production deployment window (off-peak)
- Rollback plan documented before deployment starts
This sounds like overhead until you've experienced a 3 AM call because a dev pushed a missing index to production without review and caused blocking across the entire application.
Proactive Monitoring: Catch Problems Before They Cause Downtime
The goal is to know about problems before users do. Alert on:
- Disk space above 80% (not 95%)
- Blocking chains older than 30 seconds
- Failed SQL Agent jobs
- Backup age exceeding your RPO + 20%
- Log file growth events
- TempDB space consumption > 70%
- CPU sustained above 90% for > 5 minutes
Incident Response: Speed Matters
When downtime happens (and it will), having a documented incident response process cuts your RTO significantly. Your runbook should include:
- Who to page and in what order
- Triage checklist: services status, disk, memory, blocking, recent changes
- Decision tree: failover vs. restore vs. hotfix
- Communication templates for stakeholder updates
- Post-incident review process
A team that's drilled their incident response resolves issues in 45 minutes. An unprepared team takes 4 hours doing the same work.