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docs(perf): add resource-utilisation subsection (#68)
Closes the second open AC item on #68 ("Memory usage measured against the container's allocated memory; document the headroom"). Pulled from the deployment platform's per-app overview API at the end of the re-baseline run: - Avg pod RAM: ~810 MB (2.5% of the apparent limit) - Avg pod CPU: ~13% during sustained 27 RPS Both numbers are well clear of pressure, and the 13% CPU during sustained load corroborates the methodology-notes claim that the remaining cap isn't pure compute — the workers are idle most of the time, waiting on TLS / network / scheduler. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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@@ -103,6 +103,18 @@ Compared to Scenario B at the same rate: p50 62 ms vs 56 ms; p99 115 ms vs 112 m
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No drift over the 3-minute window. p99 stayed under 200 ms throughout. Tail-latency distribution is tighter at the median under single-worker (much more <50 ms) but the >100 ms tail is slightly fatter under multi-worker — net p99 is ~30 ms higher. Within the SLO either way.
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## Resource utilisation
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Pulled from the platform's per-app overview API at the end of the run (one sample, averaged over the recent activity window):
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| Metric | Value |
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|---|---|
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| Avg pod RAM | ~810 MB |
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| Avg pod CPU | ~13% |
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| Avg pod RAM as % of limit | 2.5% |
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Both numbers are well clear of pressure. CPU at 13% during sustained 27 RPS is a useful corroboration that the bottleneck isn't pure compute — the workers are idle most of the time, waiting on something else (TLS, network, or scheduler). Memory headroom is comfortable for adding a third worker or an in-process cache if either becomes warranted.
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## Rate-limit shared-storage verification
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A separate probe with **no `Authorization` header** was used to exercise the

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