Open-Source Observability · prometheus

prometheus

Prometheus is a widely-adopted, open-source monitoring and time-series database system developed under the CNCF. It collects metrics from applications and infrastructure via pull-based HTTP collection, evaluates rules, and triggers alerts. It emphasizes simplicity, autonomous single-server operation, and a powerful query language (PromQL).

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Source: GitHub — github.com/prometheus/prometheus

65k

GitHub stars

10.6k

Forks

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	prometheus/prometheus
Owner	prometheus
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	65k
Forks	10.6k
Open issues	859
Latest release	v3.13.0 (2026-07-01)
Last updated	2026-07-08
Source	https://github.com/prometheus/prometheus

What prometheus is

Written in Go, Prometheus uses a multi-dimensional data model (metrics with key-value labels) and PromQL for flexible querying. It supports service discovery (Kubernetes, static, file-based, HTTP), remote write/read, federation, and multiple graphing backends. No external database required for typical deployments.

Quickstart

Get the prometheus source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/prometheus/prometheus.git2cd prometheus3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Kubernetes and cloud-native infrastructure monitoring

Native Kubernetes service discovery, extensive ecosystem of exporters, and horizontal federation make Prometheus the de facto standard for monitoring containerized workloads and cloud infrastructure.

Application and systems performance metrics

Pull-based model, PromQL expressiveness, and alerting rules excel at collecting and correlating application-level and infrastructure metrics (CPU, memory, latency, business KPIs) for real-time visibility.

Alert generation and incident response workflows

Rule evaluation, alert grouping, and webhook/integration capabilities enable teams to detect anomalies and route notifications to on-call workflows, dashboards, and ticketing systems.

Implementation considerations

Cardinality explosion (unbounded label values) is the primary operational risk; design label strategies carefully and enforce limits via relabeling rules.
Pull-based collection requires network reachability to targets; consider firewalls, service mesh integration (mTLS), and target discovery configuration.
PromQL learning curve and performance tuning (aggregation, recording rules, scrape intervals) are necessary for production scale.
Data retention and remote storage backend selection (Thanos, Cortex, or cloud-native options) should align with compliance and analytics retention policies.
High-cardinality metrics (e.g., per-request latency histograms with unbound dimensions) can degrade performance; use careful instrumentation practices.

When to avoid it — and what to weigh

Require log aggregation or trace correlation — Prometheus stores only numeric metrics. Logs and traces require separate systems (e.g., Loki, Jaeger). For full observability, plan for integration with complementary tools.
Need guaranteed long-term data durability without external storage — Default local filesystem storage is suitable for retention windows of weeks to months. Long-term archival (years) requires remote storage backends, adding operational complexity.
Push metrics from short-lived batch jobs at massive scale — While Prometheus Pushgateway exists, the pull model is optimized for persistent services. High-cardinality push scenarios may cause performance or operational issues.
Operate with minimal operational overhead on resource-constrained environments — Prometheus requires careful tuning (scrape intervals, retention, cardinality limits) to avoid memory bloat. Large deployments need dedicated monitoring infrastructure.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), a permissive open-source license that allows modification, distribution, and commercial use with attribution and liability disclaimer.

Apache-2.0 permits commercial use without requirement to open-source derivative works or pay license fees. However, review your specific use case (e.g., bundling, modification, resale) to ensure compliance with attribution requirements. No explicit warranty or indemnification; typical OSI practice applies.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Considerations: Run Prometheus with least-privilege (non-root user), restrict network access to metrics endpoints and Alertmanager, enable TLS for remote write, validate scrape target configuration to prevent SSRF. Default no authentication on metrics API; deploy behind reverse proxy or service mesh for authentication/authorization. Vulnerability scanning enabled (govulncheck, OpenSSF Scorecard). No exploit details disclosed here.

Alternatives to consider

Grafana Mimir

Cloud-native, horizontally scalable time-series database; requires managed infrastructure (Kubernetes, object storage). Better for multi-tenant or massive-scale scenarios; higher operational overhead than standalone Prometheus.

InfluxDB

Commercial and OSS time-series options; supports push ingestion and SQL-like query language. Different data model and operational model; not Kubernetes-native by default; useful if push metrics or alternate query paradigms are required.

Datadog, New Relic, Honeycomb

Fully managed SaaS observability platforms. Eliminate operational burden but introduce vendor lock-in, cost scaling, and data residency considerations. Suit teams prioritizing speed over self-hosting.

Software development agency

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prometheus FAQ

Is Prometheus suitable for long-term data retention?

Default local storage is suited to weeks–months retention. For years-long retention, deploy a remote storage backend (Thanos, Cortex, or cloud services). This adds operational complexity but is well-established.

How does Prometheus handle high-cardinality metrics?

High-cardinality (unbounded label values) causes memory bloat and query slowness. Mitigate via relabeling rules (drop/keep labels), cardinality limits in scrape configs, and careful instrumentation (avoid per-request IDs as labels).

Can I use Prometheus in a multi-tenant environment?

Vanilla Prometheus has no built-in multi-tenancy. Use Cortex, Mimir, or Thanos for isolated tenants. Alternatively, deploy separate Prometheus instances per tenant with shared infrastructure.

What is the difference between Prometheus pull and Pushgateway push?

Prometheus pulls metrics from persistent targets via HTTP (ideal for services, exporters). Pushgateway is an intermediary for short-lived jobs (batch, cron). Push adds latency and a single point of failure; pull is preferred.

Work with a software development agency

Adopting prometheus is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source observability software in production.

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