Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality.