continuous-cost-monitor

# Continuous Cost Monitor Skill Tracks actual infrastructure costs against estimated costs. Alerts when actual spend drifts from projection, identifies cost drivers, and suggests corrective actions. ## When to Use Use this skill to: 1. **Detect cost creep** — infrastructure spending slowly increases over time 2. **Root-cause cost spikes** — which service changed? traffic? resource use? 3. **Compare actual vs. estimated** — am I spending what I projected? 4. **Trigger optimization cycles** — when do I need to re-optimize? 5. **Alert on budget overruns** — notify team before exceeding budget ## Input Provide estimated costs + actual spend data: ```json { "estimated_monthly_cost": 8500, "actual_monthly_cost": 9200, "cost_breakdown_estimated": { "backend_compute": 1200, "database": 1800, "cache": 200, "cdn": 300, "monitoring": 300, "storage": 500, "other": 400 }, "cost_breakdown_actual": { "backend_compute": 1500, "database": 2100, "cache": 200, "cdn": 350, "monitoring": 400, "storage": 450, "other": 200 }, "traffic_metrics": { "dau_estimated": 100000, "dau_actual": 110000, "query_volume_estimated": 500000, "query_volume_actual": 650000 }, "measurement_period_days": 30, "budget_threshold": 9000 } ``` ## Output A cost monitoring report: ```json { "period": "April 2026", "estimated_vs_actual": { "estimated": 8500, "actual": 9200, "variance": 700, "variance_percent": 8.2, "status": "YELLOW (over by $700)" }, "cost_breakdown_analysis": { "backend_compute": { "estimated": 1200, "actual": 1500, "variance": 300, "variance_percent": 25, "driver": "Traffic 10% higher than expected (110k vs 100k DAU)" }, "database": { "estimated": 1800, "actual": 2100, "variance": 300, "variance_percent": 16.7, "driver": "Query volume 30% higher (650k vs 500k queries). Likely N+1 queries or missing cache." }, "cache": { "estimated": 200, "actual": 200, "variance": 0, "variance_percent": 0, "status": "On budget" }, "cdn": { "estimated": 300, "actual": 350, "variance": 50, "variance_percent": 16.7, "driver": "Traffic 10% higher (correlates with DAU increase)" }, "monitoring": { "estimated": 300, "actual": 400, "variance": 100, "variance_percent": 33, "driver": "Increased metric cardinality (added new dashboards or higher scrape frequency)" }, "storage": { "estimated": 500, "actual": 450, "variance": -50, "variance_percent": -10, "status": "Better than expected" }, "other": { "estimated": 400, "actual": 200, "variance": -200, "variance_percent": -50, "status": "Significantly better than expected" } }, "root_cause_analysis": { "primary_driver": "Database query volume 30% higher than expected", "contributing_factors": [ "Traffic 10% higher (110k vs 100k DAU) — accounts for ~$300", "Inefficient queries — N+1 patterns or missing caching — accounts for ~$200", "Monitoring cost creep — added dashboards — accounts for ~$100" ], "confidence": 0.85 }, "trend_analysis": { "month_1_actual": 8100, "month_2_actual": 8800, "month_3_actual": 9200, "trend": "Increasing", "monthly_growth": 5.5, "projected_next_month": 9700, "annualized_growth": 66 }, "alerts": [ { "level": "YELLOW", "message": "Cost overrun: +$700 (+8.2%) vs. estimate", "action": "Review database query patterns and cache hit rates" }, { "level": "ORANGE", "message": "Database costs growing (16.7% overrun). Query volume 30% higher.", "action": "Profile slow queries, implement caching, optimize N+1 patterns" }, { "level": "ORANGE", "message": "Monitoring cost increasing (33% overrun). Cardinality growth?", "action": "Audit new metrics added. Disable unused dashboards." }, { "level": "YELLOW", "message": "Cost trend: +5.5% per month. At this rate, exceed $10k budget in 2 months.", "action": "Run cost optimization (reduce scope or increase efficiency)" } ], "budget_status": { "budget": 9000, "actual": 9200, "over_budget": true, "headroom": -200, "trajectory": "Will exceed budget next month at current trend" }, "recommendations": [ { "action": "Optimize database queries", "potential_savings": 200, "effort": "Medium (1-2 weeks)", "confidence": "High" }, { "action": "Review monitoring cardinality", "potential_savings": 100, "effort": "Low (1-2 days)", "confidence": "High" }, { "action": "Consider caching layer for read-heavy queries", "potential_savings": 150, "effort": "Medium (1 week)", "confidence": "Medium" }, { "action": "Scale traffic analysis — is 110k DAU sustainable?", "potential_savings": 300, "effort": "Low (planning only)", "confidence": "Medium" } ], "escalation_actions": { "if_over_10_percent": "Run `/architect:optimize-costs` to identify quick wins", "if_trend_continues": "Trigger roadmap planning discussion (may need rearchitecture)", "if_exceeded_budget": "Notify finance and product teams; discuss scope/timeline changes" } } ``` ## Monitoring Categories ### 1. Variance Detection **Green (0-5% overrun):** - Expected traffic fluctuation - No action needed - Continue monitoring **Yellow (5-15% overrun):** - Actionable overrun - Identify root cause - Plan optimization (1-2 weeks) **Orange (15-30% overrun):** - Significant drift - Immediate investigation required - Implement fixes within 1 week **Red (>30% overrun):** - Major unexpected costs - Emergency response - May need scaling or rearchitecture ### 2. Service-Level Analysis ``` For each service, calculate: - Estimated cost per unit ($/DAU, $/query, $/GB) - Actual cost per unit - Variance ratio = actual / estimated If variance > 1.2 (20% overrun): - Service is performing worse than expected - Prioritize optimization ``` **Example:** ``` Database estimated: $18/1000 queries = $1.8k at 100k DAU Database actual: $21/1000 queries = $2.1k at 110k DAU (30% more queries!) Root cause: Queries increased 30% (not linear with traffic). Hypothesis: N+1 pattern, missing cache, or change in feature usage. ``` ### 3. Trend Analysis **Linear growth (stable):** ``` Month 1: $8.1k Month 2: $8.8k Month 3: $9.2k Growth: ~$0.5k/month Projection: Will hit $10k at month 8 Action: Schedule optimization for month 5-6 (preventive) ``` **Exponential growth (concerning):** ``` Month 1: $8.1k Month 2: $9.2k (+13%) Month 3: $10.5k (+14%) Projection: Will hit $15k at month 6 Action: Emergency optimization needed this month ``` **Seasonal patterns:** ``` Traffic spikes certain days (weekends, holidays): - Normal: expected pattern - Action: Adjust budget for peak days Unexpected spike (viral event, marketing campaign): - Investigate: did marketing team run unplanned campaign? - Document: for future capacity planning ``` ### 4. Budget Tracking ``` Budget: $9,000/month Threshold red: $10,000 (11% over) Threshold orange: $9,500 (5.5% over) Threshold yellow: $9,200 (2.2% over) Current: $9,200 (YELLOW) Action: Monitor closely; if hits $9,500, trigger optimization ``` ## Monitoring Algorithm ``` run_monthly_cost_monitor(): 1. Collect actual costs from cloud providers (AWS, Vercel, etc.) 2. Calculate total variance total_variance = actual_total - estimated_total variance_percent = (total_variance / estimated_total) × 100 3. For each service: service_variance = actual[service] - estimated[service] if service_variance > 15% of estimated[service]: flag as "needs investigation" 4. Analyze trends growth_rate = (month_3 - month_1) / (month_1 × 2) if growth_rate > 10% per month: projection = estimated × (1 + growth_rate) ^ months_remaining if projection > budget: alert "cost trajectory exceeds budget" 5. Correlate with metrics for each service_with_variance: if traffic increased: expected_increase = service_estimated × (traffic_actual / traffic_estimated) unexplained_variance = service_actual - expected_increase if unexplained_variance > 0: "query efficiency degraded" if traffic flat: "efficiency lost (not traffic-driven)" 6. Generate alerts + recommendations sort recommendations by impact × ease for each, suggest: action, savings, effort, when to implement ``` ## Real-World Monitoring Examples ### Example 1: Normal Growth ``` Month 1 (Jan): $8.1k actual vs $8k estimated (+1.25%) Month 2 (Feb): $8.8k actual vs $8.3k estimated (+6%) Month 3 (Mar): $9.2k actual vs $8.5k estimated (+8.2%) Traffic: 100k → 105k → 110k DAU (linear +5% per month) Cost growth: +5.5% per month Status: ✅ HEALTHY - Cost growth correlates with traffic - Variance stays under 10% - Trend is linear (not exponential) Action: Continue monitoring. No intervention needed yet. ``` ### Example 2: Unexpected Database Spike ``` Month 1 (Jan): Database $1.7k (on budget) Month 2 (Feb): Database $1.9k (+11%, expected for +5% traffic) Month 3 (Mar): Database $2.4k (+26% vs Feb, but traffic only +5%) Root cause investigation: - Query volume: 600k in March (was 500k in Jan) → +20% - Traffic: 110k DAU (was 100k in Jan) → +10% - Mismatch: queries grew 2× traffic growth Hypothesis: New feature (user notifications?) doing N+1 queries Evidence: Queries per user jumped from 5 to 6 Action: Profile notification feature, add caching, optimize queries Expected savings: -$300/mo (-12% database cost) Timeline: 1 week implementation ``` ### Example 3: Monitoring Cost Creep ``` Month 1: Monitoring $250 (expected) Month 2: Monitoring $300 (+20%, no traffic increase) Month 3: Monitoring $400 (+33% vs expected) Analysis: - Dashboards added: 3 new custom dashboards - Metric cardinality: Up from 500 → 2000 metrics Root cause: Team added detailed metrics for each user segment Cost impact: $1.50/metric when cardinality grows Action: Disable unused dashboards, consolidate metrics Expected savings: -$100/mo Timeline: 2 days (administrative) ``` ### Example 4: Capacity-Driven Growth (Healthy) ``` Month 1: 100k DAU, $8.1k Month 2: 150k DAU, $9.8k Month 3: 200k DAU, $11.2k Analysis: - Cost per DAU: $81 → $65 → $56 (improving!) - Reason: Economies of scale (database utilization higher) Status: ✅ EFFICIENT SCALING - Variance: +15% of estimated (acceptable for 2× traffic growth) - Cost per unit improving (good sign) - Trend: Sustainable Action: Continue at current architecture. No optimization needed. Next major action: Plan rearchitecture at 500k DAU. ``` ### Example 5: Warning: Budget Trajectory ``` Budget: $9,000/month Month 1: $8.1k Month 2: $8.9k Month 3: $9.4k Trend: +$650/month growth Projection at month 6: $11.3k (26% over budget) Status: 🔴 ALERT — on collision course with budget Immediate actions (this week): 1. Profile database for N+1 queries (-$300 potential) 2. Review monitoring cardinality (-$100 potential) 3. Implement caching for hot queries (-$200 potential) Total potential: -$600/mo (brings us to $8.8k, under budget) Timeline: 2-3 weeks If not implemented: Will exceed budget in 5 weeks ``` ## Integration with Other Commands ### In `/architect:cost-estimate` ```pseudo // After estimating costs: setup_monitoring(estimated_costs) → Track actual vs estimated → Monthly alerts on variance → Feed into optimization cycles