We Got the Prompt Cache Working. Our Pipeline Got Slower.

"You're spawning a fresh codex exec for every single call? Just run the app-server and reuse a thread. The prompt cache alone will pay for it." That sentence sounds so obviously right that nobody ever benchmarks it. We run a small bazaar of headless AI daemons — designers, reviewers, code workers — and each one shells out to OpenAI's Codex CLI dozens of times per work item. A resident codex app-server --stdio with warm threads looked like free money. We measured it four times. The prompt cache eventually hit 86% on the turns that mattered. The pipeline got slower than the boring baseline, and 39% more expensive in raw tokens. This is the story of why, and of what app-server is actually for. The setup was deliberately minimal: no resident daemon, no pool. The server is spawned privately for one formation , runs its turns, and is killed by a context manager on the way out — success or crash. That keeps the comparison honest: same lifecycle as a subprocess, and adopting it stays a measurement problem instead of an architecture debate. Trusting the probe Before writing any integration, we cloned the actual openai/codex source and pointed a probe at a real app-server: handshake, per-turn sandbox overrides, interrupts, forks, kill-and-resume. Eight checks, all green, plus three findings you won't find in any docs: cwd and sandboxPolicy are sticky when omitted — every turn must re-state them explicitly, or turn 3 silently inherits turn 2's write access. Token usage arrives on thread/tokenUsage/updated , not on turn/completed . An empty thread that never ran a turn is never materialized — you cannot resume it later. Pre-warming a thread pool is a trap. And the seductive one: on the probe's trivial two-turn thread, turn 2 reported 15,104 cached input tokens . See? The cache works. Ship it. That number cost us three more measurement runs to un-believe. The four runs Same fixture repo, same greenfield request, one real four-stage formation each: A: codex exec (baseline) B: app