Capacity planning begins with constraints

Capacity planning is not forecasting one traffic number and adding a fixed percentage. It is identifying which resource limits the user outcome, how demand approaches that limit, and how quickly the system can respond. Model demand in useful units Requests per second may be insufficient when requests have different cost. Include dimensions such as payload size, records scanned, concurrent sessions, event fan out, model tokens, or storage growth. Separate steady demand from bursts and scheduled work. Use historical distributions, product events, seasonal patterns, and known launches. Forecasts should be ranges with assumptions, not a single precise value. Record which assumptions would invalidate the plan. Find the limiting resource Map each demand unit to CPU, memory, connections, queue depth, I/O, network, third party quota, and human operational load. The first exhausted dependency determines effective capacity. Managed services still have quotas, scaling rates, partition limits, and regional constraints. Load tests should reproduce representative mixes and data sizes. Measure user visible latency and error behavior while increasing load gradually. A test that stops before saturation cannot show the failure mode or recovery behavior. Preserve headroom Headroom covers traffic variance, node loss, failover, deployments, rebalancing, and forecast error. Define it per constrained resource. Autoscaling reduces provisioning time only if metrics, limits, quotas, and downstream dependencies can support the added instances. The Google SRE discussion of handling overload explains load shedding, request prioritisation, and graceful behavior as capacity protections. Those controls should be designed before saturation. Connect capacity to action Create thresholds with lead time: when to tune, scale, repartition, request quota, or change architecture. Assign owners and account for the time needed to purchase or approve capacity. Monitor forecast error and update the model after launches and incidents. Test reduced capacity states, including zone loss and dependency throttling. A system sized only for normal conditions may fail exactly when redundancy is needed. Capacity planning is an operating loop: measure demand, identify constraints, validate behavior, preserve recovery margin, and revisit assumptions. The output is not a spreadsheet. It is enough time and evidence to act before users discover the limit. Use queueing signals Utilisation alone can look healthy while latency rises sharply near saturation. Track concurrency, queue wait, service time, rejection, and retry volume. Little’s Law—average items in a stable system equals arrival rate multiplied by average time—can help connect throughput, latency, and work in progress, provided the measured boundary and time window are consistent. Retries amplify demand during failure. Model retry policy, client timeouts, batch catch up, and failover traffic as part of capacity rather than treating them as unusual. A dependency recovery can create a second spike when queued work is released. Separate scaling horizons Application replicas may scale in seconds, nodes in minutes, database partitions in hours, and procurement or architecture changes in months. Maintain triggers for each horizon. Fast autoscaling cannot fix an exhausted regional IP range or a third party quota that takes weeks to raise. Reserve enough capacity for maintenance and failure. If normal traffic consumes the capacity required to lose a zone, redundancy exists only on a diagram. Test failover while representative load is present. Connect technical and financial limits For elastic systems, define the cost of meeting peak demand and a guardrail for runaway scaling. Track unit economics such as cost per transaction, active tenant, or processed gigabyte. A capacity change that lowers latency but multiplies unit cost needs an explicit product decision. Planning checklist Record demand units, seasonal range, workload mix, constraint map, saturation behavior, headroom policy, scaling lead times, quotas, failover load, cost envelope, and owners. Review actual versus forecast, explain material error, and update the model. Forecast accuracy improves through a measured loop, not through increasingly detailed unsupported estimates.