Open source alternative to Datadog, Dynatrace, Grafana Cloud and New Relic
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Prometheus is an open-source monitoring solution with a dimensional data model and powerful query language for time series data.
Track request latency, error rate, saturation, and service health with PromQL queries and alerting rules that match each team's reliability targets.
Skip if the team needs logs, traces, and metrics in one managed product from day one.
Use service discovery, labels, exporters, and dashboards to monitor changing workloads without manually registering every target.
Skip if the team cannot own metric naming, scrape configs, and retention settings.
Run metrics collection and alerting on owned infrastructure instead of paying usage-based SaaS rates for every host, service, and time series.
Skip if internal operations time costs more than the managed monitoring bill.
Expose application metrics through Prometheus client libraries, then query and alert on application-specific behavior without waiting for a vendor integration.
Skip if the application already emits only vendor-specific telemetry and migration is not planned.
Managed monitoring platforms become expensive as services, labels, retention, and alert volume grow. Platform teams also lose direct control over scrape strategy, storage policy, and where operational telemetry lives. Prometheus gives those teams a self-hosted metrics system that runs close to their infrastructure and keeps collection, querying, and alerting under their control.
Prometheus scrapes configured HTTP endpoints on a schedule, so teams control exactly which targets emit metrics and how often data is collected. Static configs and service discovery both work for changing infrastructure.
PromQL lets teams aggregate, filter, and calculate over metric labels for latency, error rate, saturation, and capacity questions. Queries can drive dashboards, alerts, and recording rules.
Metrics use names plus key-value labels, which makes it practical to slice data by service, instance, job, environment, or cluster without creating separate metric families for every view.
Prometheus evaluates recording and alerting rules locally, then can route firing alerts through Alertmanager for grouping, deduplication, and notification workflows.
Teams can instrument applications with client libraries or collect metrics from existing systems through exporters for services such as databases, hosts, proxies, and container platforms.
Prometheus and Datadog both help teams monitor infrastructure and applications, but they make opposite tradeoffs. Prometheus is an Apache 2.0 self-hosted metrics system: you own scrape configuration, storage, PromQL queries, and alert rules. Datadog is proprietary SaaS: it reduces setup work and combines metrics, logs, traces, dashboards, and managed integrations in one hosted product.
Choose Prometheus when data ownership, cost control, open standards, and custom metric design matter more than managed convenience. It fits SRE and platform teams that can operate their own monitoring stack and want telemetry close to their infrastructure. Choose Datadog when a team needs an all-in-one managed observability product with less internal operations work, especially if logs and traces must ship with metrics immediately.
# Docker quick start from the official README
docker run --name prometheus -d -p 127.0.0.1:9090:9090 prom/prometheus
# Local binary flow from the official getting-started guide
tar xvfz prometheus-*.tar.gz
cd prometheus-*
./prometheus --config.file=prometheus.ymlPrometheus can replace the metrics and alerting parts of Datadog for teams that want self-hosted control. It does not replace Datadog's full managed logs, traces, dashboards, and incident workflow package by itself.
No. Prometheus can monitor hosts, applications, and services outside Kubernetes. Kubernetes is a common fit because Prometheus handles dynamic targets and labeled time-series data well.
PromQL is the query language for selecting, filtering, aggregating, and calculating over Prometheus time-series data. Teams use it for dashboards, alerts, recording rules, and incident investigation.
Prometheus is built for numeric metrics, not log storage or distributed tracing. Teams commonly pair it with Grafana, Loki, Tempo, Jaeger, or another tool when they need those telemetry types.
The official README shows a Docker command that starts Prometheus on port 9090. The official getting-started guide also shows how to download a release, create a prometheus.yml file, and run the binary locally.
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