Slo Implementation

SLO Implementation

Framework for defining and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error budgets.

Purpose

Implement measurable reliability targets using SLIs, SLOs, and error budgets to balance reliability with innovation velocity.

When to Use

• Define service reliability targets
• Measure user-perceived reliability
• Implement error budgets
• Create SLO-based alerts
• Track reliability goals

SLI/SLO/SLA Hierarchy

SLA (Service Level Agreement)
  ↓ Contract with customers
SLO (Service Level Objective)
  ↓ Internal reliability target
SLI (Service Level Indicator)
  ↓ Actual measurement

Defining SLIs

Common SLI Types

1. Availability SLI

# Successful requests / Total requests
sum(rate(http_requests_total{status!~"5.."}[28d]))
/
sum(rate(http_requests_total[28d]))

2. Latency SLI

# Requests below latency threshold / Total requests
sum(rate(http_request_duration_seconds_bucket{le="0.5"}[28d]))
/
sum(rate(http_request_duration_seconds_count[28d]))

3. Durability SLI

# Successful writes / Total writes
sum(storage_writes_successful_total)
/
sum(storage_writes_total)

Reference: See references/slo-definitions.md

Setting SLO Targets

Availability SLO Examples

| SLO % | Downtime/Month | Downtime/Year | | ------ | -------------- | ------------- | | 99% | 7.2 hours | 3.65 days | | 99.9% | 43.2 minutes | 8.76 hours | | 99.95% | 21.6 minutes | 4.38 hours | | 99.99% | 4.32 minutes | 52.56 minutes |

Choose Appropriate SLOs

Consider:

• User expectations
• Business requirements
• Current performance
• Cost of reliability
• Competitor benchmarks

Example SLOs:

slos:
  - name: api_availability
    target: 99.9
    window: 28d
    sli: |
      sum(rate(http_requests_total{status!~"5.."}[28d]))
      /
      sum(rate(http_requests_total[28d]))

  - name: api_latency_p95
    target: 99
    window: 28d
    sli: |
      sum(rate(http_request_duration_seconds_bucket{le="0.5"}[28d]))
      /
      sum(rate(http_request_duration_seconds_count[28d]))

Error Budget Calculation

Error Budget Formula

Error Budget = 1 - SLO Target

Example:

• SLO: 99.9% availability
• Error Budget: 0.1% = 43.2 minutes/month
• Current Error: 0.05% = 21.6 minutes/month
• Remaining Budget: 50%

Error Budget Policy

error_budget_policy:
  - remaining_budget: 100%
    action: Normal development velocity
  - remaining_budget: 50%
    action: Consider postponing risky changes
  - remaining_budget: 10%
    action: Freeze non-critical changes
  - remaining_budget: 0%
    action: Feature freeze, focus on reliability

Reference: See references/error-budget.md

SLO Implementation

Prometheus Recording Rules

# SLI Recording Rules
groups:
  - name: sli_rules
    interval: 30s
    rules:
      # Availability SLI
      - record: sli:http_availability:ratio
        expr: |
          sum(rate(http_requests_total{status!~"5.."}[28d]))
          /
          sum(rate(http_requests_total[28d]))

      # Latency SLI (requests = bool 0.999

      - record: slo:http_latency:compliance
        expr: sli:http_latency:ratio >= bool 0.99

      # Error budget remaining (percentage)
      - record: slo:http_availability:error_budget_remaining
        expr: |
          (sli:http_availability:ratio - 0.999) / (1 - 0.999) * 100

      # Error budget burn rate
      - record: slo:http_availability:burn_rate_5m
        expr: |
          (1 - (
            sum(rate(http_requests_total{status!~"5.."}[5m]))
            /
            sum(rate(http_requests_total[5m]))
          )) / (1 - 0.999)

SLO Alerting Rules

groups:
  - name: slo_alerts
    interval: 1m
    rules:
      # Fast burn: 14.4x rate, 1 hour window
      # Consumes 2% error budget in 1 hour
      - alert: SLOErrorBudgetBurnFast
        expr: |
          slo:http_availability:burn_rate_1h > 14.4
          and
          slo:http_availability:burn_rate_5m > 14.4
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Fast error budget burn detected"
          description: "Error budget burning at {{ $value }}x rate"

      # Slow burn: 6x rate, 6 hour window
      # Consumes 5% error budget in 6 hours
      - alert: SLOErrorBudgetBurnSlow
        expr: |
          slo:http_availability:burn_rate_6h > 6
          and
          slo:http_availability:burn_rate_30m > 6
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "Slow error budget burn detected"
          description: "Error budget burning at {{ $value }}x rate"

      # Error budget exhausted
      - alert: SLOErrorBudgetExhausted
        expr: slo:http_availability:error_budget_remaining  14.4
        and
        slo:http_availability:burn_rate_5m > 14.4
      )
      or
      (
        slo:http_availability:burn_rate_6h > 6
        and
        slo:http_availability:burn_rate_30m > 6
      )
    labels:
      severity: critical

SLO Review Process

Weekly Review

• Current SLO compliance
• Error budget status
• Trend analysis
• Incident impact

Monthly Review

• SLO achievement
• Error budget usage
• Incident postmortems
• SLO adjustments

Quarterly Review

• SLO relevance
• Target adjustments
• Process improvements
• Tooling enhancements

Best Practices

1. Start with user-facing services
2. Use multiple SLIs (availability, latency, etc.)
3. Set achievable SLOs (don't aim for 100%)
4. Implement multi-window alerts to reduce noise
5. Track error budget consistently
6. Review SLOs regularly
7. Document SLO decisions
8. Align with business goals
9. Automate SLO reporting
10. Use SLOs for prioritization

Reference Files

• assets/slo-template.md - SLO definition template
• references/slo-definitions.md - SLO definition patterns
• references/error-budget.md - Error budget calculations

Related Skills

• prometheus-configuration - For metric collection
• grafana-dashboards - For SLO visualization

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: GeniusHTX
• Source: GeniusHTX/SWE-Skills-Bench
• License: MIT

Install and usage instructions live in the source repository linked above.