Capacity Planning
The process of determining the computing resources needed to meet current and future demand while balancing performance, cost, and reliability. Capacity planning uses traffic projections, load testing, and resource utilization data to make informed infrastructure decisions.
Capacity planning combines historical usage trends, growth projections, and load testing data to forecast resource needs. It considers organic growth, planned feature launches, marketing campaigns, and seasonal patterns. The goal is to provision enough capacity for peak demand with appropriate headroom while avoiding the waste of significantly over-provisioned resources.
For AI product teams, capacity planning is especially important because AI infrastructure is expensive and has long lead times. GPU instances may need to be reserved months in advance for favorable pricing, and model serving infrastructure takes time to scale up. Growth teams directly influence capacity requirements: a successful viral campaign can generate traffic spikes that overwhelm AI inference capacity if not anticipated. Capacity planning should account for growth team initiatives by including planned experiments, campaign timelines, and projected traffic increases in demand forecasts. Load testing should specifically stress AI endpoints to establish their throughput limits and determine the scaling behavior of model inference under increasing concurrent request loads.
Related Terms
Content Delivery Network
A geographically distributed network of proxy servers that caches and delivers content from locations closest to end users. CDNs reduce latency, improve load times, and absorb traffic spikes by serving content from edge nodes rather than a single origin server.
Edge Computing
A distributed computing paradigm that processes data closer to the source of generation rather than in a centralized data center. Edge computing reduces latency, conserves bandwidth, and enables real-time processing for latency-sensitive applications.
Serverless Computing
A cloud execution model where the provider dynamically manages server allocation and scaling. Developers deploy functions or containers without provisioning infrastructure, paying only for actual compute time consumed rather than reserved capacity.
Function as a Service
A serverless computing category where developers deploy individual functions that execute in response to events. FaaS platforms like AWS Lambda, Google Cloud Functions, and Azure Functions handle all infrastructure management, scaling each function independently.
Platform as a Service
A cloud computing model that provides a complete development and deployment environment without managing underlying infrastructure. PaaS offerings like Heroku, Vercel, and Google App Engine handle servers, storage, networking, and runtime configuration.
Infrastructure as a Service
A cloud computing model that provides virtualized computing resources over the internet. IaaS offerings like AWS EC2, Google Compute Engine, and Azure Virtual Machines give teams full control over servers, storage, and networking without owning physical hardware.