Azure Key Vault pricing: estimate operations, keys/secrets, and request spikes

Key Vault cost estimation is mostly a request-based problem. If an application fetches secrets or performs crypto operations on every request, costs can scale linearly with traffic. The fastest way to make cost predictable is to map "what calls Key Vault" to a clear driver metric.

0) Identify operation types (do not blend)

Split calls into buckets. Different operation types behave differently, and different call paths cache differently.

Secrets: get secret, list secrets (list calls can be surprisingly expensive if done often).
Keys: decrypt/encrypt, sign/verify, wrap/unwrap.
Certificates: reads, renewals, and rotation workflows.

1) Choose the real driver (per request vs per startup)

The most important modeling decision is whether calls happen per request or per "startup" event. A secret loaded once per pod start is a totally different cost model than a secret fetched on every API request.

Per request: driver is API requests/month (most expensive if un-cached).
Per instance/pod start: driver is starts/day (scale-outs + deploys create peaks).
Per rotation job: driver is rotation frequency (e.g., daily/weekly/monthly).

Tool: RPS to monthly requests (for per-request models).

2) Estimate monthly operations (baseline + peak)

Build two scenarios. Baseline is normal traffic with warm caches. Peak is a deploy/scale-out/incident window with cold caches and retries.

Baseline ops/month = driver events/month * ops per event (by operation type).
Peak ops/month = baseline + (deploys + scale-outs + incident retries) * ops per event.
If crypto operations are in a tight loop, estimate using TPS (transactions per second) and convert to monthly.

Tool: Request cost (generic request math).

3) Prevent the hot-path trap (caching strategy)

The classic cost bug is an un-cached secret fetch in a request handler. Fixing it usually costs less engineering time than one month of a scaled-out bill.

Cache secrets/config in-memory with a TTL that matches your rotation policy.
Avoid "list secrets" patterns in hot paths; resolve identifiers once and cache.
If you rotate secrets frequently, model the rotation job as a separate bucket (it can create short spikes).

Worked estimate template (copy/paste)

Driver events/month = API requests OR pod starts OR rotation runs
Secret ops/month = driver events/month * secret ops per event (baseline + peak)
Crypto ops/month = TPS * seconds/month (baseline + peak)
Retries multiplier = 1 + retry_rate (apply to the buckets affected)

Common pitfalls

Fetching secrets/keys per request instead of caching.
Ignoring deploy/scale-out peaks (cold caches cause bursts).
Not modeling retries/timeouts (they multiply operation volume).
Using one blended operation rate instead of separating secret vs crypto vs certificate calls.
Hidden hot paths like middleware/auth filters that call Key Vault for every request.

How to validate

Identify the hot path: grep your code for Key Vault client calls and map them to endpoints/jobs.
Validate caching behavior (TTL, cache misses, and cache warm-up during deploys).
Validate retry behavior during incidents (timeouts amplify calls).
Validate which operation types dominate (secrets vs crypto) and optimize the right bucket.