Prompt-injection detectors are heterogeneous: each is strong on a different slice of attacks, and none is always reliable. Yet existing systems still treat detection as a fixed single-detector pipeline, committing every request to one detector’s blind spots. We reframe defense as *detector allocation*: given a heterogeneous pool, decide per request which detectors to run and whether to escalate to an LLM judge. Our framework **SCOUT** (**S**calable and **C**ontrollable **O**utcome-prediction for **U**ncertainty-aware **T**riage) makes this decision dynamic by predicting each detector’s per-sample reliability and latency from how it behaved on similar past inputs, and exposes a single safety-utility threshold to the operator (where utility bundles benign-pass rate and wall-clock). To evaluate this setting, we build SCOUT-450, a benchmark that captures the structurally complex, agent-facing injections that older prompt-injection sets under-represent. On SCOUT-450, a safety-oriented operating point reduces attack-success rate by **46%** and total wall-clock by **40%** relative to an always-on GPT-4o judge, at a **5.1**-point benign-utility drop. SCOUT also transfers to three external benchmarks (BIPIA, IPI, and IHEval), improving the safety-utility frontier.