Prove Cloudflare WordPress fleet performance, resilience, and cost

Parent: #1970

## Goal

Establish production readiness for an elastic preview fleet through dimension-based performance, concurrency, failure, multi-region, and cost evidence.

## Workloads

- 100-site and 1,000-site allocation/provisioning campaigns
- concurrent artifacts of varied page count, decoded size, asset volume, and runtime complexity
- anonymous publication reads from multiple regions and cache states
- warm/cold admin, REST, edit, media, and subsequent-import traffic
- D1/R2/queue latency, throttling, duplicate delivery, Worker eviction, timeout, and partial-failure injection
- long-running soak with expiration, deletion, reclamation, and allocation reuse

## Required evidence

- p50/p95/p99 latency by API stage and public/dynamic read path
- allocation and ready-site throughput under bounded concurrency
- PHP-WASM boot/execute/persist memory and CPU high-water marks
- queue age, retries, dead letters, publication lag, and recovery time
- D1 rows/queries, R2 requests/bytes/storage, Worker CPU, and estimated cost per site lifecycle
- isolation and invariant violations, not only aggregate success counts

## Acceptance criteria

- Publish explicit SLOs and pass/fail budgets before running the acceptance campaign.
- No cross-site state, cache, credential, operation, or publication leakage occurs.
- Failure injection proves committed state and previous publications remain recoverable.
- Throughput does not degrade linearly with configured site count.
- Public cached-read latency remains independent of PHP-WASM and coordinator load.
- Known gaps are reported by semantic dimension: lifecycle, state transitions, resources, compatibility, and regional behavior.
- Evidence is reviewer-resolvable and linked to this tracker.
- No production deployment or load test is performed without explicit authorization.

## AI assistance

OpenAI GPT-5.6 Sol via OpenCode translated the current two-site evidence and measured latency gaps into this fleet acceptance contract with Chris Huber.

Iniziare dall’issue padre #1970 e dalle evidenze attuali di due siti qui referenziate. Definire campagne di accettazione autorizzate per i workload elencati, pubblicare prima gli SLOs e i budget pass/fail, quindi raccogliere evidenze verificabili dai reviewer su isolamento, ripristino, latenza, throughput, utilizzo delle risorse e costi, senza eseguire deployment in produzione o load test non approvati.

Scritto dal modello di indicizzazione a partire dal testo della issue.