<p>Cardinality and cost estimation are critical components of query optimization, as they directly influence the construction of efficient physical execution plans. While machine learning-based estimators have achieved notable success, they face several challenges: (1) Training data derived from rigid, template-driven benchmarks exhibits significant distributional divergence from real-world query workloads, a challenge further compounded by the manual template design in exhaustively representing the full spectrum of query patterns. (2) These methods demonstrate limited generalization capabilities, especially in scenarios involving sub-plan estimation or queries that significantly deviate from the training query templates. Furthermore, the inherent inefficiency of operator-level cardinality estimation frequently undermines its applicability for accurate cost estimation. (3) These approaches frequently fail to leverage the rich semantic information and dynamic dependencies between operators.</p><p>To address these challenges, we propose a novel operator-level cardinality and cost estimator that simultaneously estimates the cardinality and cost of all sub-plans within a query plan. First, we leverage large language models to generate high-quality and diverse SQL queries, which serve as the foundation for pre-training and fine-tuning our model. Second, we introduce a semantic-based operator encoding strategy, augmented with a novel tree-structure-aware neural network, to effectively represent each sub-plan. Third, we propose a specialized loss function tailored for joint cardinality and cost prediction at the operator level, fully utilizing labels from each sub-plan. Extensive experiments on both synthetic and real-world datasets demonstrate that our method consistently outperforms state-of-the-art approaches.</p>