Modern systems, characterized by cloud-native and microservices architectures, present significant monitoring challenges due to their complexity and the high volume of telemetry data they generate. Traditional monitoring tools like Graphite and StatsD have fallen short in addressing these needs, leading to the rise of Prometheus, which offers flexible querying and efficient metric scraping. However, Prometheus's scalability is limited by its single-node architecture, which struggles with long-term data retention and high cardinality metrics. To overcome this, modern time series databases (TSDBs) have emerged as a solution for long-term storage, allowing organizations to leverage Prometheus's strengths in metrics scraping and querying while providing scalable storage solutions for large volumes of telemetry data. The integration of TSDBs with Prometheus enables the retention of granular historical data without aggressive downsampling, making it an effective approach for managing the complexities of modern system monitoring.