Performance Isolation in Multi-Tenant Cloud Data Centers (MTCDCs) consists of a set of mechanisms to make sure tenants’ use of resources does not impact other tenants. In this context, traffic shapers and rate limiters are fundamental to addressing the challenges of performance isolation in MTCDCs, which include predictable performance as minimum bandwidth guarantees, tenants-level fairness, and optimal resource utilization. However, the classical linear programming process to find the optimal rates to apply does not scale in terms of computing time, especially with the huge number of nodes, dominated mainly by virtual machines in an MTCDC environment. Motivated by this observation, this paper introduces gPerfIsol, a novel Graph Neural Network (GNN)-based approach designed to find near-optimal rates allocation in near-real-time to ensure performance isolation in MTCDC. gPerfIsol’s key innovation leverages Heterogeneous GNNs to capture MTCDC-specific topological information and demand traffic matrix. Evaluations based on datasets generated through simulation demonstrate the effectiveness of gPerfIsol’s binary classification model with a precision score of 0.964 and a recall score of 0.973. Ultimately, gPerfIso1 offers a promising solution for nearoptimal rate limit allocation for traffic shapers in multi-tenant environments, enhancing performance isolation.