Uptime Kuma vs Prometheus: Monitoring Compared

Two Different Problems

Uptime Kuma and Prometheus appear in the same “monitoring” category, but they solve fundamentally different problems. Uptime Kuma answers “is my service up right now?” — it pings endpoints, checks response codes, and alerts you when something goes down. Prometheus answers “how is my infrastructure performing over time?” — it scrapes numerical metrics from targets, stores time-series data, and powers dashboards and alerting rules based on metric thresholds.

Updated March 2026: Verified with latest Docker images and configurations.

Comparing them directly is like comparing a smoke detector to a building management system. One does one thing well. The other is an extensible platform. Understanding what you actually need determines which is right.

Feature Comparison

Feature	Uptime Kuma	Prometheus
Primary purpose	Uptime monitoring + alerting	Metrics collection + time-series DB
Check types	HTTP(S), TCP, Ping, DNS, Docker, gRPC, MQTT, keyword	Pull-based metric scraping (HTTP)
Web UI	Built-in dashboard (beautiful)	Built-in expression browser (minimal)
Alerting	90+ notification services built-in	Alertmanager (separate component)
Dashboards	Built-in status page + charts	Requires Grafana for visualization
Data model	Up/down + response time per monitor	Multidimensional time-series metrics
Query language	None	PromQL
Service discovery	Manual (add monitors via UI)	Automatic (DNS, Consul, Kubernetes, file-based)
Exporters/integrations	None needed (checks externally)	100+ exporters (node, MySQL, Nginx, etc.)
Data retention	SQLite (configurable)	TSDB (default 15 days, configurable)
RAM usage	100-200 MB	500 MB-2 GB+ (depends on series count)
Setup time	2 minutes	30-60 minutes (with Grafana + exporters)
Status pages	Yes (public, customizable)	No (use Gatus or Cachet separately)
Certificate monitoring	Yes (SSL expiry alerts)	Via blackbox_exporter
Maintenance windows	Yes	No native support
Multi-user	Yes (accounts + teams)	No (proxy-based auth)
Language	Node.js	Go
License	MIT	Apache 2.0

Docker Deployment

Uptime Kuma

A single container, no dependencies:

services:
  uptime-kuma:
    image: louislam/uptime-kuma:2.2.1
    ports:
      - "3001:3001"
    volumes:
      - uptime_kuma_data:/app/data
      - /var/run/docker.sock:/var/run/docker.sock:ro
    restart: unless-stopped

volumes:
  uptime_kuma_data:

Start it, open the UI, create an account, add monitors. The Docker socket mount is optional — it enables Docker container monitoring (check if a container is running).

Prometheus + Grafana Stack

A production Prometheus setup needs at minimum three components:

services:
  prometheus:
    image: prom/prometheus:v3.10.0
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro
      - prometheus_data:/prometheus
    command:
      - "--config.file=/etc/prometheus/prometheus.yml"
      - "--storage.tsdb.retention.time=30d"
      - "--web.enable-lifecycle"
    restart: unless-stopped

  grafana:
    image: grafana/grafana:12.4.1
    ports:
      - "3000:3000"
    environment:
      GF_SECURITY_ADMIN_USER: admin
      GF_SECURITY_ADMIN_PASSWORD: changeme_grafana_password
    volumes:
      - grafana_data:/var/lib/grafana
    restart: unless-stopped

  node-exporter:
    image: prom/node-exporter:v1.10.2
    pid: host
    network_mode: host
    volumes:
      - /proc:/host/proc:ro
      - /sys:/host/sys:ro
      - /:/rootfs:ro
    command:
      - "--path.procfs=/host/proc"
      - "--path.sysfs=/host/sys"
      - "--path.rootfs=/rootfs"
      - "--collector.filesystem.mount-points-exclude=^/(sys|proc|dev|host|etc)($$|/)"
    restart: unless-stopped

volumes:
  prometheus_data:
  grafana_data:

Prometheus configuration file:

# prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: "prometheus"
    static_configs:
      - targets: ["localhost:9090"]

  - job_name: "node"
    static_configs:
      - targets: ["host.docker.internal:9100"]

After deploying, you still need to: configure Grafana to use Prometheus as a data source, import dashboards (Node Exporter Full dashboard ID: 1860), and set up alerting rules. This is a minimum viable setup — production deployments typically add Alertmanager, more exporters, and recording rules.

When Uptime Kuma Is Enough

For most self-hosters, Uptime Kuma covers the monitoring need completely. You want to know:

• Is my Nextcloud up? → HTTP monitor on port 443
• Is my VPN reachable? → TCP monitor on WireGuard port
• Is my SSL certificate expiring soon? → Certificate monitor
• Is my Docker container running? → Docker monitor
• Did my DNS resolve correctly? → DNS monitor

Uptime Kuma answers all of these with a clean dashboard, historical uptime charts, and instant notifications through 90+ services (Discord, Telegram, Slack, email, Pushover, Gotify, ntfy, and more). Setup takes 2 minutes. There’s nothing to configure beyond adding monitors.

The public status page feature is a bonus — share a read-only page showing your service status with users or family members.

When You Need Prometheus

Prometheus becomes necessary when you need to answer questions that uptime checks can’t:

• Why is my server slow right now? → CPU, memory, disk I/O metrics over time
• Which container is using the most memory? → cAdvisor metrics
• How many requests is my reverse proxy handling? → Nginx/Traefik exporter metrics
• Is my database connection pool exhausted? → PostgreSQL/MySQL exporter metrics
• How does today’s resource usage compare to last week? → PromQL range queries

The power of Prometheus is PromQL — a query language for slicing and aggregating time-series data. Combined with Grafana dashboards, you get full observability into every aspect of your infrastructure. But this power comes with complexity: you need to understand metric types (counters, gauges, histograms), write PromQL queries, configure exporters for each service you want to monitor, and maintain the Grafana dashboards.

Resource Comparison

Component	RAM	CPU	Disk (30 days)
Uptime Kuma (50 monitors)	150-200 MB	Minimal	~100 MB
Prometheus (50 targets, 1000 series)	500 MB-1 GB	Low-Moderate	2-5 GB
Grafana	100-200 MB	Minimal	~50 MB
Node Exporter	15-30 MB	Minimal	0 (stateless)
Uptime Kuma total	~200 MB	Minimal	~100 MB
Prometheus stack total	~800 MB-1.4 GB	Low-Moderate	~2-5 GB

Uptime Kuma uses 4-7x less RAM than a basic Prometheus stack. On a Raspberry Pi or small VPS, this difference matters.

Use Cases

Choose Uptime Kuma If…

• You need uptime monitoring and alerting for your services
• A clean, visual dashboard matters more than raw metrics
• You want public status pages for your services
• Setup time should be minutes, not hours
• You run on limited hardware (Pi, 1 GB VPS)
• SSL certificate monitoring is important
• You don’t need infrastructure metrics (CPU, RAM, disk usage over time)

Choose Prometheus If…

• You need infrastructure and application metrics, not just up/down checks
• You want to build custom Grafana dashboards for your setup
• You need to correlate metrics across services during incidents
• PromQL queries for alerting rules (e.g., “alert if disk fills within 4 hours at current rate”)
• You monitor 10+ services that expose Prometheus metrics
• You’re running Kubernetes (Prometheus is the de facto monitoring standard)

Use Both

Many self-hosters run both. Uptime Kuma for quick “is it up?” monitoring with beautiful dashboards and instant alerts. Prometheus + Grafana for deeper infrastructure observability when troubleshooting. They don’t conflict — Uptime Kuma checks externally (pings services), while Prometheus scrapes internal metrics.

Final Verdict

If you need monitoring for a self-hosted setup and don’t currently have any, start with Uptime Kuma. It solves the most common need — knowing when something goes down — in 2 minutes with zero configuration complexity. You can always add Prometheus later when you need deeper observability.

If you already know you need time-series metrics, custom dashboards, and PromQL-powered alerting — or if you’re running a larger infrastructure — deploy a Grafana + Prometheus stack from the start. Uptime Kuma can still complement it as your status page and quick-check tool.

FAQ

Can Prometheus do uptime monitoring?

Yes, using the blackbox_exporter. It probes HTTP, TCP, DNS, and ICMP endpoints and exposes the results as Prometheus metrics. But the setup is more complex than Uptime Kuma — you configure probe targets in YAML, set up alerting rules in Prometheus, and use Grafana for visualization. For pure uptime monitoring, Uptime Kuma is simpler.

Does Uptime Kuma have an API?

Yes. Uptime Kuma exposes a REST API for managing monitors, status pages, and notifications programmatically. It also supports Prometheus metric export at /metrics — so Prometheus can scrape Uptime Kuma’s data if you want both.

Can Uptime Kuma monitor server resources?

Not directly. Uptime Kuma monitors service availability (is port X responding?), not resource usage (how much RAM is used?). For server resource monitoring, use Beszel, Netdata, or a Prometheus + Node Exporter setup.

How much disk does Prometheus use?

Roughly 1-2 bytes per sample. With 1,000 time series scraped every 15 seconds and 30-day retention, expect 2-5 GB. More targets and higher scrape frequency increase storage proportionally.

Related

• How to Self-Host Uptime Kuma
• How to Self-Host Prometheus
• How to Self-Host Grafana
• Grafana vs Prometheus: Monitoring Stack
• Beszel vs Netdata: Server Monitoring
• Grafana vs Netdata: Dashboards Compared
• Best Self-Hosted Monitoring Tools
• Docker Compose Basics