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AI Capacity Planning Template: Forecast and Scale AI Infrastructure

Complete AI capacity planning template with compute forecasting, cost modeling, scaling triggers, and team resource allocation frameworks for AI products.

By Institute of AI PMFebruary 17, 202611 min read

AI products consume infrastructure differently than traditional software. GPU costs can spiral, inference latency can degrade with scale, and training pipelines compete for the same resources. This template helps you forecast demand, plan capacity, and scale infrastructure without surprises.

Why AI Capacity Planning Is Different

Unique AI Infrastructure Challenges

GPU Scarcity

Compute resources are expensive and often have lead times of weeks or months

Non-Linear Scaling

Doubling users does not simply double compute; model complexity affects scaling curves

Training vs Inference Split

Training and inference workloads have different resource profiles and scheduling needs

Cost Volatility

Cloud GPU pricing, token costs, and spot instance availability fluctuate significantly

AI Capacity Planning Template

Copy and customize this template for your AI infrastructure planning:

╔══════════════════════════════════════════════════════════════════╗ ║ AI CAPACITY PLANNING DOCUMENT ║ ╠══════════════════════════════════════════════════════════════════╣ PLANNING OVERVIEW ──────────────────────────────────────────────────────────────────── Product Name: [AI product or feature name] Planning Lead: [PM Name] Planning Horizon: [e.g., Q2 2026 / Next 6 months] Last Updated: [YYYY-MM-DD] Review Cadence: [Monthly / Quarterly] CURRENT STATE BASELINE ──────────────────────────────────────────────────────────────────── Active Users: [Current MAU] Daily Inference Requests: [Avg daily API calls] Avg Latency (P50 / P99): [X ms / Y ms] Current Monthly Compute Cost: $[Amount] GPU Utilization: [X%] Training Pipeline Frequency: [Daily/Weekly/Monthly] Infrastructure Provider: [AWS/GCP/Azure/Self-hosted] Current GPU Fleet: [e.g., 4x A100 80GB] Model Serving: [e.g., vLLM, TensorRT, SageMaker] Storage Used: [X TB training data, Y GB models] ╠══════════════════════════════════════════════════════════════════╣ ║ DEMAND FORECAST ║ ╠══════════════════════════════════════════════════════════════════╣ USER GROWTH PROJECTION ──────────────────────────────────────────────────────────────────── Timeframe MAU Daily Requests Growth Rate ──────────────────────────────────────────────────────────────────── Current [X] [Y] Baseline Month +1 [X] [Y] [Z%] Month +3 [X] [Y] [Z%] Month +6 [X] [Y] [Z%] Month +12 [X] [Y] [Z%] Growth Assumptions: • [Source of growth - e.g., new feature launch, marketing push] • [Seasonal factors - e.g., holiday spike, end-of-quarter] • [Risk factor - e.g., viral adoption, competitor migration] INFERENCE DEMAND MODEL ──────────────────────────────────────────────────────────────────── Feature Calls/User/Day Avg Tokens GPU Time ──────────────────────────────────────────────────────────────────── [Feature 1] [X] [Y] [Z ms] [Feature 2] [X] [Y] [Z ms] [Feature 3] [X] [Y] [Z ms] [Batch Jobs] [X/day] [Y] [Z min] Peak Multiplier: [e.g., 3x avg during business hours] Burst Capacity: [Max concurrent requests needed]

Compute Scaling Plan

╠══════════════════════════════════════════════════════════════════╣ ║ SCALING TRIGGERS ║ ╠══════════════════════════════════════════════════════════════════╣ AUTO-SCALE RULES ──────────────────────────────────────────────────────────────────── Trigger Threshold Action ──────────────────────────────────────────────────────────────────── GPU Utilization > 80% for 15m Add 1 instance GPU Utilization > 95% for 5m Add 2 instances (urgent) P99 Latency > [X] ms Add 1 instance Request Queue Depth > [X] requests Add 1 instance GPU Utilization < 30% for 1hr Remove 1 instance Error Rate > 2% Alert + investigate MANUAL SCALE TRIGGERS (Require PM approval) ──────────────────────────────────────────────────────────────────── • New model deployment (larger model = more VRAM) • Feature launch expected to increase usage > 50% • Enterprise customer onboarding (dedicated capacity) • Training pipeline scheduled (reserve GPUs) SCALING TIERS ──────────────────────────────────────────────────────────────────── Tier Users GPUs Monthly Cost Latency ──────────────────────────────────────────────────────────────────── Starter < 1K 2x A100 $[X] < 200ms Growth 1K - 10K 4x A100 $[X] < 300ms Scale 10K - 100K 8x A100 $[X] < 400ms Enterprise > 100K 16x A100+ $[X] SLA-based

Cost Forecasting Model

MONTHLY COST BREAKDOWN ──────────────────────────────────────────────────────────────────── Category Current +3 Mo +6 Mo +12 Mo ──────────────────────────────────────────────────────────────────── GPU Compute (Inference) $[X] $[X] $[X] $[X] GPU Compute (Training) $[X] $[X] $[X] $[X] API Costs (3rd Party) $[X] $[X] $[X] $[X] Storage (Data + Models) $[X] $[X] $[X] $[X] Networking / Egress $[X] $[X] $[X] $[X] Monitoring / Logging $[X] $[X] $[X] $[X] ──────────────────────────────────────────────────────────────────── TOTAL $[X] $[X] $[X] $[X] COST PER UNIT METRICS ──────────────────────────────────────────────────────────────────── Metric Current Target Industry Avg ──────────────────────────────────────────────────────────────────── Cost per User / Month $[X] $[X] $[X] Cost per 1K Inferences $[X] $[X] $[X] Cost per Training Run $[X] $[X] $[X] Infra as % of Revenue [X%] [X%] [X%] COST OPTIMIZATION LEVERS ──────────────────────────────────────────────────────────────────── Lever Savings Est. Effort Priority ──────────────────────────────────────────────────────────────────── Model Distillation [X%] High [P1/P2/P3] Response Caching [X%] Low [P1/P2/P3] Spot/Preemptible GPUs [X%] Medium [P1/P2/P3] Batch Processing [X%] Medium [P1/P2/P3] Quantization (INT8/4) [X%] High [P1/P2/P3] Reserved Instances [X%] Low [P1/P2/P3]

Team Resource Allocation

TEAM CAPACITY REQUIREMENTS ──────────────────────────────────────────────────────────────────── Role Current FTE Needed +6 Mo Gap ──────────────────────────────────────────────────────────────────── ML Engineers [X] [Y] [+/-Z] Data Engineers [X] [Y] [+/-Z] MLOps / Platform [X] [Y] [+/-Z] Data Annotators [X] [Y] [+/-Z] AI Product Manager [X] [Y] [+/-Z] SKILL GAPS TO ADDRESS ──────────────────────────────────────────────────────────────────── Skill Priority Resolution ──────────────────────────────────────────────────────────────────── [e.g., LLM Ops] High Hire / Train / Contract [e.g., GPU Infra] Medium Hire / Train / Contract [e.g., Data Pipelines] Medium Hire / Train / Contract

Common AI Capacity Planning Mistakes

Planning for Average, Not Peak

AI workloads are bursty. Size for 3-5x average to handle peak traffic without degradation.

Ignoring Training Compute

Training and fine-tuning compete with inference for GPUs. Schedule training during off-peak hours.

No Cost Ceiling

Without spending alerts and hard caps, a traffic spike or runaway job can cause massive bills.

Single Provider Lock-In

Relying on one cloud provider limits negotiation power. Plan for multi-cloud or hybrid fallback.

Skipping Load Testing

Theoretical capacity and real-world capacity differ. Load test before every major launch.

No Graceful Degradation Plan

When at capacity, have a plan: queue requests, use smaller models, or show cached results.

Quick-Start Checklist

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