Certification cost of quantum models: measurement correlation, not parameter count

Reporting the Fisher geometry of a trained variational quantum model is routine; quoting the shot budget that would establish it is not. Certifying an empirical Fisher matrix to relative Frobenius error epsilon under coordinate-wise parameter shift costs Θ(B p squared V/(epsilon squared G)) circuit executions, where V is the measured readout variance and G the measured squared gradient norm, with uniform allocation optimal in that class. One constant reproduces the cost of two circuit families whose exponents differ by a full power of p. The exponent is an identity in how nV and nG scale with the register, holding family by family to 0.001 across 624 matrix-product-state cells once the finite- p prefactor is removed. The cubic cost is therefore a finite-size window, set by whether the readout light cone grows with the register. A product family to 256 qubits gives 1.966 (95% CI 1.934 -- 1.997); a brickwork entangler falls from 2.853 below ten qubits to 1.715 beyond sixty-four; a blocked entangler gives 1.984 at a fixed cone width against 3.034 at a proportional one. Fixed device connectivity fixes the cone, so a cubic budget from a small simulation overestimates a large machine, on top of hardware multipliers 2.07 times (1.41 -- 3.02), 2.38 times and 1.91 times on ibm marrakesh, ibm fez and ibm kingston. Cost-optimal readout weights cut the measured shot budget by 2.67 times (1.33 -- 4.00) on hardware, flat from four to twelve qubits. A discrepancy model fitted on cheap circuits transfers its mean inside the calibration grid and, at six larger sizes named before the data, does not: nominal 90% intervals cover 36%, and split conformal is the only rung that stays near nominal.