Prometheus vs Datadog
Open-source pull-based monitoring vs fully managed observability platform. Compare cost, scalability, features, and operational burden.
| Criteria | Prometheus | Datadog |
|---|---|---|
| Architecture | Pull-based metrics collection. Local TSDB storage. Federation and remote write for scaling. Runs in your infrastructure. | SaaS platform with agents pushing metrics. Fully managed storage, ingestion, and querying. No infrastructure to manage. |
| Cost | Free and open-source. Costs are compute/storage for running it. Scales linearly — predictable but requires ops effort. | Per-host pricing ($15-23/host/month) plus per-metric custom costs. Can get expensive at scale. Predictable bills but watch for overages. |
| Query Language | PromQL — powerful, purpose-built for time series. Steep learning curve but extremely expressive for alerting and dashboards. | Proprietary query syntax. More approachable for beginners. Point-and-click metric explorer. Less powerful for complex aggregations. |
| Alerting | Alertmanager handles routing, grouping, silencing, and inhibition. Highly configurable but requires YAML configuration. | Built-in alerting with UI-based configuration. Anomaly detection, forecast alerts, and composite monitors out of the box. |
| Ecosystem Integration | Native Kubernetes service discovery. Thousands of exporters. Grafana for visualization. CNCF project — cloud-native standard. | 600+ integrations. APM, logs, RUM, synthetics all in one platform. Single pane of glass for full-stack observability. |
| Long-term Storage | Local storage limited (15-30 days typical). Thanos, Cortex, or Mimir for long-term. Additional operational complexity. | 15-month retention included. No infrastructure to manage for storage. Historical queries work seamlessly. |
Verdict
Choose Prometheus if you have the ops capacity, want to avoid vendor lock-in, and need deep Kubernetes-native monitoring. Choose Datadog if you want a managed platform with broad integrations and can budget for per-host pricing.
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