Prometheus

Prometheus revolutionized monitoring when it launched in 2012, introducing a pull-based metrics collection model that perfectly suited the dynamic nature of cloud-native infrastructure. Developed by former Google engineers at SoundCloud, Prometheus brought Google's internal monitoring expertise to the open-source world, offering capabilities similar to Borgmon. Today, Prometheus is the second CNCF project to graduate after Kubernetes, validating its critical role in cloud-native observability. The platform monitors millions of containers and servers worldwide, trusted by companies like DigitalOcean, GitLab, Uber, and Robinhood for mission-critical infrastructure monitoring. With its active community contributing hundreds of exporters and integrations, Prometheus has become the monitoring foundation for modern DevOps and SRE practices, processing billions of time-series data points daily across diverse environments from Kubernetes clusters to IoT devices.

Common Use Cases

DevOps teams deploy Prometheus to monitor Kubernetes clusters, collecting metrics from nodes, pods, and applications with automatic service discovery that adapts to dynamic workloads. SRE teams use it to track service level indicators (SLIs) for SLO compliance, setting up sophisticated alerting rules that notify on-call engineers before user-facing issues occur. Application developers instrument their code with Prometheus client libraries to expose custom business metrics, tracking API response times, request rates, error rates, and user activity patterns. Infrastructure teams monitor traditional servers and services using Node Exporter for system metrics, Blackbox Exporter for endpoint availability, and specialized exporters for databases, message queues, and network equipment. Platform teams build comprehensive observability stacks by combining Prometheus for metrics with Loki for logs and Jaeger for traces, creating unified monitoring across all telemetry signals. IoT operations leverage Prometheus to aggregate sensor data and equipment telemetry, visualizing trends and setting thresholds for predictive maintenance across distributed installations.

Key Features

• Multi-dimensional time-series data model with flexible labels
• PromQL query language for powerful metric analysis and aggregation
• Efficient local time-series database with no distributed storage dependencies
• Pull-based metrics collection via HTTP
• Service discovery integration with Kubernetes, Consul, EC2, and more
• Built-in alerting with Alertmanager integration
• Rich ecosystem of official and community exporters
• Client libraries for instrumenting applications in all major languages
• HTTP pull and push gateway support for batch jobs
• Built-in web UI for queries, graphs, and alerts exploration
• Federation for hierarchical monitoring architectures
• Recording rules for pre-computing frequently needed queries
• Native Grafana integration for advanced visualization
• High availability through federation and external storage adapters

Why deploy Prometheus on Hostinger VPS

Deploying Prometheus on Hostinger VPS provides dedicated resources for reliable metrics collection and querying without the constraints of managed monitoring services that charge per metric or data point. With full control over Prometheus configuration, you can optimize retention policies, scrape intervals, and storage settings for your specific monitoring requirements. The VPS environment ensures consistent query performance even during metric storms, sufficient storage for weeks or months of metrics history, and predictable costs regardless of metrics volume. You benefit from centralized monitoring infrastructure that can scrape targets across your entire infrastructure, complete control over alerting rules and notification routing, and the ability to integrate custom exporters for proprietary systems. The professional hosting ensures your monitoring remains available even when the systems being monitored experience issues, implements proper separation between monitoring and production workloads, and provides the resources needed for complex PromQL queries across large metric datasets. For organizations building comprehensive observability stacks, VPS deployment enables co-location with Grafana, Alertmanager, and other complementary tools while maintaining data sovereignty and avoiding cloud vendor lock-in.