Files
peltier.io/results/phase4/cjson-service-roi.md
Claude 0ec111663b phase4: COMPLETE — service latency win + mechanical ROI report; roadmap done
phase4-cjson-service (ledger): the accepted batch win phase3-cjson-002
measured under a 150 rps coordinated-omission-correct replay (20 rounds,
40k requests, 0 drops). p50 latency +6.2%, 95% CI [+5.8%, +7.2%] —
accepted; p99 CI [0.07, 4.97] too noisy (single-worker loopback tail),
harness correctly rejected. ROI (500-core fleet, $0.04/core-hr): 27.5
cores / $9,621/year at the CI lower bound, report generated mechanically
(results/phase4/cjson-service-roi.md).

- results/phase4/: case study + README + the generated ROI report.
- CLAUDE.md: Phase 4 complete; Phases 0-4 all done.

All SPEC §5 exit criteria met across every phase; zero shipped false
accepts across the whole project.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
2026-07-11 23:48:22 +00:00

2.3 KiB

ROI report — phase4-cjson-service

Target: cjson · Playbook class: 5 · Verdict: accepted

Workload: corpora/cjson/input/service.json, 1 parse+print/req, 150 rps open-loop, CO-correct

Throughput → cores → dollars

Measured speedup (baseline/candidate): 1.0624, 95% bootstrap CI [1.0581, 1.0725].

On a 500-core fleet running this workload:

metric median 95% CI
cores returned 29.4 [27.5, 33.8]
annualized saving $10290 [$9621, $11838]

Pricing: $0.04/core-hour, 8760 h/year. Source: example: AWS m7i on-demand us-east-1, per-vCPU approximation — replace per engagement

The CI lower bound is the number to quote — 27.5 cores / $9621 per year is the saving that survives the 95% interval; the point estimate is not a promise.

Latency under replayed load

Workload: corpora/cjson/input/service.json, 1 parse+print/req, 150 rps open-loop, CO-correct. Coordinated-omission-correct open-loop replay.

percentile baseline candidate speedup 95% CI
p50 3.332 ms 3.136 ms 1.0624 [1.0581, 1.0725]
p99 24.726 ms 23.947 ms 1.0325 [0.0715, 4.9714]

Methodology (ships with the number)

  • Interleaved A/B, baseline rebuilt from a pristine checkout, never the agent's workspace. Candidate and baseline alternate to control thermal/background drift.
  • Bootstrap 95% CI of the ratio-of-medians; an effect is accepted only if the CI lower bound clears the threshold in config/accept.toml (2%).
  • Equivalence gates passed before any number was trusted: upstream test suite, byte-identical golden replay, differential fuzzing, and ASan/UBSan sanitizers.
  • Calibrated hardware: the workload passed A/A (false-positive <5%) and injected- regression (≥95% detection) self-tests, recorded in results/calibration/.
  • Coordinated-omission correct latency (service mode): each request's latency is measured from its intended send time, so queueing is counted, not hidden.

Caveats

Every figure is specific to the workload and hardware named above. Throughput→cores assumes the fleet is CPU-bound on this path and scales linearly; latency figures are for the stated arrival rate. The saving to commit to is the CI lower bound, not the median.