What is Capacity Planning?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Plan 6-12 months ahead for infrastructure that takes weeks to provision like GPU clusters and dedicated instances. Plan 1-3 months ahead for cloud auto-scaling configurations. Update forecasts monthly with actual traffic data. Factor in planned product launches, seasonal traffic patterns, and model complexity increases. The cost of underplanning is service degradation during traffic spikes. The cost of overplanning is wasted budget on idle resources. Maintain a 20-30% buffer above forecasted peak as safety margin.

Track prediction request volume trends, model inference latency under load, GPU and CPU utilization rates, training job queue wait times, and storage growth rates. Project forward using linear or exponential growth models depending on your business stage. Include model complexity trends since more complex models need more compute per prediction. Factor in planned changes like new models, feature additions, or serving region expansion. Correlate with business metrics like user growth and feature adoption rates.

Frame capacity in terms of business risk: what happens when we exceed capacity? Quantify the cost of service degradation during peak traffic. Show the cost curve of reactive versus proactive provisioning, where emergency scaling typically costs 2-3x planned scaling. Present capacity planning as risk management rather than engineering spending. Include cost optimization results from right-sizing efforts to demonstrate fiscal responsibility alongside growth requests.

Plan 6-12 months ahead for infrastructure that takes weeks to provision like GPU clusters and dedicated instances. Plan 1-3 months ahead for cloud auto-scaling configurations. Update forecasts monthly with actual traffic data. Factor in planned product launches, seasonal traffic patterns, and model complexity increases. The cost of underplanning is service degradation during traffic spikes. The cost of overplanning is wasted budget on idle resources. Maintain a 20-30% buffer above forecasted peak as safety margin.

Track prediction request volume trends, model inference latency under load, GPU and CPU utilization rates, training job queue wait times, and storage growth rates. Project forward using linear or exponential growth models depending on your business stage. Include model complexity trends since more complex models need more compute per prediction. Factor in planned changes like new models, feature additions, or serving region expansion. Correlate with business metrics like user growth and feature adoption rates.

Frame capacity in terms of business risk: what happens when we exceed capacity? Quantify the cost of service degradation during peak traffic. Show the cost curve of reactive versus proactive provisioning, where emergency scaling typically costs 2-3x planned scaling. Present capacity planning as risk management rather than engineering spending. Include cost optimization results from right-sizing efforts to demonstrate fiscal responsibility alongside growth requests.

Plan 6-12 months ahead for infrastructure that takes weeks to provision like GPU clusters and dedicated instances. Plan 1-3 months ahead for cloud auto-scaling configurations. Update forecasts monthly with actual traffic data. Factor in planned product launches, seasonal traffic patterns, and model complexity increases. The cost of underplanning is service degradation during traffic spikes. The cost of overplanning is wasted budget on idle resources. Maintain a 20-30% buffer above forecasted peak as safety margin.

Track prediction request volume trends, model inference latency under load, GPU and CPU utilization rates, training job queue wait times, and storage growth rates. Project forward using linear or exponential growth models depending on your business stage. Include model complexity trends since more complex models need more compute per prediction. Factor in planned changes like new models, feature additions, or serving region expansion. Correlate with business metrics like user growth and feature adoption rates.

Frame capacity in terms of business risk: what happens when we exceed capacity? Quantify the cost of service degradation during peak traffic. Show the cost curve of reactive versus proactive provisioning, where emergency scaling typically costs 2-3x planned scaling. Present capacity planning as risk management rather than engineering spending. Include cost optimization results from right-sizing efforts to demonstrate fiscal responsibility alongside growth requests.