AI-based applications deployed as microservice chains pose challenging configuration problems, as their Feature Models (FMs) quickly grow to sizes where state-of-the-art validation approaches become infeasible. We propose FM4MC, a correctness-preserving method that validates such models efficiently by (i) slicing them into Partial Feature Models (PFMs) and (ii) applying SAT solving selectively based on estimated complexity. This combination drastically reduces the number of solver calls while retaining exact results. Our evaluation on synthetic FMs, sampled to reflect realistic heavy-tail size distributions, shows that FM4MC validates large models up to 23× faster in typical scenarios, reaches speedups of more than 100×, and scales in extreme cases even to 1,000× faster than state-of-the-art techniques. These results demonstrate that FM4MC makes configuration and validation feasible for microservice-based AI applications under mission-critical time constraints, where existing approaches become impractical.