Introduction
Prometheus is an open-source, cloud-native monitoring system and time-series database built for metrics collection, querying, alerting, and visualization at scale. Developed as part of the CNCF ecosystem, Prometheus provides a reliable, scalable foundation for instrumenting infrastructure, services, and ML workloads, helping teams detect issues early, analyze trends, and enforce service-level objectives (SLOs). It is tightly integrated into an observability stack alongside tools like Grafana, Alertmanager, and OpenTelemetry.
Key benefits of using Prometheus include:
Powerful, Pull-Based Metrics Collection: Prometheus scrapes metrics from exporters and services via HTTP, making it simple to monitor containers, APIs, databases, and model servers across Kubernetes.
Flexible Query Language (PromQL): Enables detailed querying, aggregation, and analysis of time-series metrics, supporting dashboards, alerts, and custom diagnostics.
Dynamic Service Discovery: Automatically discovers targets in Kubernetes environments, adapting to dynamic scaling and deployments with minimal configuration.
Built-In Alerting Integration: Works seamlessly with Alertmanager to trigger alerts on SLA violations, resource bottlenecks, or anomalies—pushing notifications to Slack, PagerDuty, and other systems.
Ecosystem Compatibility: Supports a wide range of exporters and integrations across the Cake stack, including Ray, KServe, MLServer, Triton, Airflow, Postgres, and model observability tools.
Prometheus is used to monitor everything from core infrastructure (CPU, memory, pod health) to higher-level metrics like API latency, model inference duration, data pipeline throughput, and autoscaler activity. It underpins both real-time alerting and long-term capacity planning, offering visibility at every layer of the platform. By adopting Prometheus, you can ensure its systems are observable, debuggable, and governed by real-time insights—empowering engineers to respond quickly to incidents and continuously improve platform performance.