Serverless computing has transformed cloud application deployment by abstracting infrastructure management while providing on-demand scalability. However, current serverless query engines often lack effective mechanisms for handling multi-tenant workloads with varying Quality of Service (QoS) expectations. In such environments, rigid pricing and static resource allocation lead to inefficiencies, SLA violations, and unfair resource distribution. This paper presents a Multi-Tenant Serverless Query Engine (MTSQE) that integrates dynamic pricing models and adaptive QoS guarantees for fair and efficient resource utilization across diverse tenants. The proposed architecture employs workload profiling, priority-based scheduling, and real-time performance feedback loops to dynamically adjust pricing and execution parameters according to SLA tiers. Through simulation using CloudSim 7G and Kubernetes-based deployment tests, the system demonstrates up to 22% improvement in resource utilization, 18% reduction in SLA violations, and 16% higher cost fairness index compared to baseline serverless frameworks. These results validate the feasibility of integrating cost-aware query management with SLA-driven adaptability for next-generation multi-tenant cloud services.