Capacity Examples Workbench
What is a capacity estimate?
A capacity estimate converts product demand into explicit traffic, compute, storage, and network assumptions that can be challenged.
Step 1
Model
Start with an illustrative workload, tune its demand and provisioning assumptions independently, then inject a spike or failure.
Step 2
Observe
Read the formulas, find the limiting budget, and turn an overloaded result into a concrete scale-out or load-shedding decision.
Step 3
Challenge
Compare planned load with a launch spike, slow dependency, and regional loss; then restore at least 30% service headroom.
Turn product demand into an operating envelope
Start from an illustrative workload, expose every multiplier, then test whether the provisioned fleet survives a bad day.
1. Choose a workload
Use an example as a starting hypothesis
Demand breakdown
Realtime messaging
Partition by conversation or recipient while keeping retries idempotent.
3. Provision the envelope
Capacity and durability assumptions
This is the supply loop. Change fleet throughput or data durability without changing product demand.
Instances, pods, workers, or accelerator replicas.
A benchmark result at acceptable tail latency, not a vendor maximum.
How long the stored payload remains online.
Primary plus replicas; compression and indexes are excluded.
4. Challenge the plan
What happens on a bad day?
Select a mode to change demand, available capacity, and latency together.
62% of available service capacity
55.1K/s remains for variance; validate the 1.8K RPS-per-unit assumption with a representative load test.
peak RPS / (unit RPS x 70% target x available fleet)
actions x payload x 30 days x 3 copies
1.5 TB/day of logical user payload