Edge intelligence places models at the network edge, effectively reducing data transmission latency and alleviating the computational load on the central cloud. However, the deployment and continuous updating of models at the edge make deployment efficiency and cost key criteria for evaluating system performance. Although prior work has proposed various strategies to balance model accuracy and deployment costs, in practical settings with volatile service demands and constrained resources, existing methods still struggle to achieve efficient and adaptive deployment and updates. To address this challenge, this study proposes PACE, a low-cost and rapidly adaptive model deployment and update strategy for edge intelligence. PACE aims to achieve efficient responses to dynamic service demands with lower deployment costs, thus enhancing the sustained service capability and performance of edge intelligence systems. Specifically, shared characteristics among models are exploited to perform fine-grained modeling of deployment costs at different stages, and the age of information (AOI) is incorporated to construct a performance evaluation model, thus enabling more cost-effective deployment decisions. On this basis, meta-reinforcement learning is introduced to learn prior knowledge of task variations, and a variational Bayesian network is combined to achieve online optimization, thus enhancing the rapid adaptability of the deployment strategy in dynamic environments. Finally, the effectiveness of PACE is theoretically validated from the perspectives of generalization and convergence. The evaluation is conducted using a synthetic Zipf request distribution. Experimental results show that, in scenarios with frequently changing tasks, PACE achieves efficient adaptive deployment, significantly reducing model deployment costs while maintaining acceptable model accuracy and system response efficiency.