Estimation of product procurement cost is a key capability for price governance on large-scale e-commerce platforms: from images, structured attributes, comparable products, and pricing knowledge, a practical system must infer a reasonable cost to surface suspiciously inflated or potentially non-compliant listings. Directly invoking general-purpose VLMs is prohibitive at high QPS (queries per second), and their verbose rationales complicate auditing and rule integration. We present Cost-LLM, a compact 7B multimodal cost-estimation model trained by a two-stage post-training pipeline: segment-normalized SFT on progressive pricing rationales, followed by GRPO with an asymmetric MAPE-based reward aligned with cost accuracy. On an in-house benchmark covering 105 fine-grained categories, Cost-LLM reaches MAPE 29.47% and Acc@20 40.42%, yielding the best point estimates among the evaluated systems. Deployed as an auxiliary signal in a price-anomaly pipeline, it provides a 30.73% relative complementary-detection yield over the production baseline at an audited precision-matched operating point. Its locally deployed serving latency is 0.4-0.8 s per listing, compared with 3-8 s observed for external commercial LLM/VLM APIs. We share the design choices, training recipe, and deployment lessons.