As the cloud services journey through their lifecycle towards becoming commodities, the demand is increasing for 'pay-per-use' pricing model. In this model, users are charged for the amount of resources, e.g., Volume of transactions, CPU usage, etc., being consumed during a given time period. Software as a Service (SaaS) providers charging their customers via pay-per-use (e.g., Microsoft Azure Web Services) and facing Infrastructure as a Service (IaaS) costs per VM per month (e.g., Soft Layer) have to carefully choose and scale their non-revenue generating service management infrastructure to penetrate and stay in the market. In this paper, we focus on the metering and rating aspects of cloud service management, and their scalability with the SaaS business and operational changes. We design a framework for cloud service providers to scale their revenue management systems in a cost-aware manner, where the deployment of these revenue systems dynamically uses existing or newly provisioned SaaS VMs, instead of the extant approach of using dedicated setups. Our experimental analysis shows that service management related tasks can be offloaded to the existing VMs with at most 15% overhead in CPU utilization, 10% overhead for memory usage, and negligible overhead for I/O and network usage. We used traces from IBM production servers to mimic the load on VMs. By dynamically scaling the service management setup, we were able to adapt to increasing metering data processing requirements without incurring additional cost, while preserving the infrastructure footprint.