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Capacity Planning & Forecasting

ML-powered demand forecasting with auto-scaling recommendations achieving 30-50% infrastructure cost reduction and predictive versus reactive scaling eliminating over-provisioning waste.

Business Outcome

time reduction in capacity review cycles

Complexity:

Medium

Time to Value:

3-6 months

Why This Matters

What It Is

ML-powered demand forecasting with auto-scaling recommendations achieving 30-50% infrastructure cost reduction and predictive versus reactive scaling eliminating over-provisioning waste.

Current State

(Traditional)

1. Application deployed with fixed infrastructure: 10 web servers, 5 application servers, 2 database servers. 2. Quarterly capacity review meeting: infrastructure team reviews CPU/memory utilization reports. 3. Utilization averages 40-60% (over-provisioned for peak capacity, wasted during normal periods). 4. Black Friday approaching, team manually adds 20 more web servers 'to be safe' (guess-based scaling). 5. Black Friday traffic spike occurs, infrastructure handles load but grossly over-provisioned (30% utilization). 6. Post-Black Friday, team forgets to scale down servers for 2-3 weeks (paying for unused capacity). 7. 40-60% over-provisioning waste due to reactive scaling and lack of forecasting.

Characteristics

• Excel
• Legacy IT Infrastructure Systems
• Email Communication
• AIOps Platforms

Pain Points

⚠ Reactive nature of planning leads to delays in scaling resources and system inefficiencies.
⚠ Limited data integration hampers comprehensive analysis and forecasting accuracy.
⚠ Manual processes create operational inefficiencies and time-consuming calculations.
⚠ Difficulty in identifying resource bottlenecks in complex IT environments.

Future State

(Agentic)

1. Capacity Forecasting Agent analyzes historical traffic patterns: learns Black Friday traffic 5x normal, Cyber Monday 3x, summer months 20% lower than winter. 2. Agent forecasts demand 30-90 days ahead: 'Black Friday traffic projected at 500K requests/min (vs 100K normal) - recommend scaling web servers from 10 to 35 units starting Nov 20'. 3. Auto-Scaling Recommendation Agent provides optimal infrastructure configuration: 'Current: 10 web servers at 45% utilization ($5K/month), Recommended: 6 web servers at 70% utilization ($3K/month) - save $2K/month'. 4. Agent implements auto-scaling policies: scale up when CPU >70% for 5 min, scale down when CPU <40% for 30 min (prevent over-provisioning). 5. Agent monitors cost optimization opportunities: 'Application servers can move from c5.2xlarge to c5.xlarge instances - save 40% ($8K/month) with no performance impact'. 6. Agent auto-scales for Black Friday (10 → 35 servers at peak), auto-scales down after event (35 → 12 servers) - optimal utilization 65-75%. 7. 30-50% infrastructure cost reduction through ML-powered forecasting and auto-scaling vs manual quarterly reviews.

Characteristics

• Historical traffic and resource utilization patterns
• Seasonal trends and business event calendar (Black Friday, product launches)
• Current infrastructure configuration and costs
• Cloud provider pricing and instance type performance data
• Application performance requirements and SLA targets
• Growth projections and business forecasts
• Auto-scaling policy configurations and history

Benefits

✓ 30-50% infrastructure cost reduction through rightsizing and auto-scaling
✓ ML forecasting predicts demand 30-90 days ahead (proactive vs reactive)
✓ Auto-scaling eliminates manual scale-up/scale-down operations
✓ Optimal utilization 65-75% (vs 40-60% over-provisioning waste)
✓ Event scaling automated (Black Friday, Cyber Monday) with post-event scale-down
✓ Instance type optimization (move to smaller instances when appropriate)

Is This Right for You?

39% match

This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.

Why this score:

• Applicable across multiple industries
• Higher complexity - requires more resources and planning
• Moderate expected business value
• Time to value: 3-6 months
• (Score based on general applicability - set preferences for personalized matching)

You might benefit from Capacity Planning & Forecasting if:

You're experiencing: Reactive nature of planning leads to delays in scaling resources and system inefficiencies.
You're experiencing: Limited data integration hampers comprehensive analysis and forecasting accuracy.

This may not be right for you if:

High implementation complexity - ensure adequate technical resources
Requires human oversight for critical decision points - not fully autonomous

Parent Capability

Infrastructure Operations & Monitoring (AIOps)

Transforms reactive incident response into predictive, self-healing infrastructure with dramatic alert noise reduction, faster MTTR, and high auto-remediation through AI-powered monitoring, anomaly detection, and automated runbooks.