Cost, capacity, and reliability
Inspect token cost, capacity, concurrency, caching, fallback strategy, load signals, and reliability tradeoffs.
What you will practice
Read token cost inputs; Estimate request cost; Compare model cost profiles; Inspect cache effectiveness.
Use the worked examples when needed, then test your understanding without them.
- 01Read token cost inputsToken cost inputs separate input and output rates, their units and the model revision. This case uses fictional training rates, not a provider price quote.Practice
- 02Estimate request costA token cost estimate multiplies each token count by its corresponding per-token rate. Convert per-million rates before summing input and output estimates.Practice
- 03Compare model cost profilesModel cost comparison should use the same workload and required quality. A cheaper request that repeatedly fails the task may not be the better operating choice.Practice
- 04Inspect cache effectivenessCache effectiveness depends on hit rate, eligible work and correctness. A high hit rate is harmful when responses come from stale or improperly shared entries.Practice
- 05Read concurrency limitsConcurrency limits bound in-flight work; they do not directly state requests per second. Throughput also depends on service time and workload size.Practice
- 06Estimate capacity headroomCapacity headroom compares measured demand with a relevant tested limit. State the workload and measurement assumptions instead of calling all unused resources interchangeable.Practice
- 07Inspect queue depthQueue depth is meaningful with arrival and completion rates. A growing queue can signal overload even while workers remain healthy.Practice
- 08Compare latency and costCost and latency tradeoffs must respect task quality and service targets. Choose among configurations that pass the required gates rather than optimizing a single metric.Practice
- 09Review fallback routingFallback routing needs explicit eligibility and limits. A fallback must preserve task, privacy and compatibility boundaries rather than serving any response at any cost.Practice
- 10Plan graceful degradationGraceful degradation retains a safe useful subset when dependencies fail. Make the reduced behavior explicit rather than pretending the full service is healthy.Practice
- 11Read reliability targetsReliability targets need a measure and window. Keep user-visible success separate from process uptime so an alive but unusable system is not counted as healthy.Practice
- 12Prepare a capacity noteA capacity note records observed demand, tested limits, bottlenecks and the next safe experiment. Include uncertainty rather than promising unmeasured scale.Practice
- 13Review cost anomaliesCost anomalies require separating request volume, token length, model selection and retries. Compare those drivers before treating an invoice increase as a provider error.Practice
- 14Cost and reliability checkpointA cost/reliability decision should preserve quality, latency, privacy and bounded failure behavior. Resource savings do not justify silently breaking another contract.Practice