Gatus vs k6 vs Uptime Kuma: Best Self-Hosted Synthetic Monitoring 2026

When your services go down, you want to know before your users do. Synthetic monitoring proactively simulates user traffic and validates service health on a schedule — catching outages, slow responses, and broken endpoints the moment they happen. Unlike passive monitoring that reacts to incoming telemetry, synthetic checks actively probe your infrastructure from the outside, exactly as a real user would experience it.

In this guide, we compare three leading open-source tools for self-hosted synthetic monitoring: Gatus, k6, and Uptime Kuma. Each takes a different approach — from configuration-driven health dashboards to scriptable load testing to beautiful GUI-based uptime tracking.

Why Self-Host Synthetic Monitoring?

Synthetic monitoring answers questions that metrics dashboards cannot:

• Is my login page actually loading in under 2 seconds?
• Did my API endpoint return the correct response body?
• Can my DNS resolver handle queries from multiple geographic regions?
• Is my TLS certificate about to expire on a subdomain I forgot about?

SaaS synthetic monitoring platforms like Pingdom, Checkly, and Datadog Synthetic Testing charge per check frequency and location. For teams running dozens of endpoints with minute-level intervals, costs escalate quickly. Self-hosted tools give you unlimited checks, full control over probe locations, and zero per-request fees — at the cost of managing the infrastructure yourself.

Gatus: Automated Health Checks with Status Pages

Gatus (10,784 stars) is a developer-oriented health check and status page tool. It validates endpoints across HTTP, TCP, DNS, ICMP, and more — all defined in a single YAML configuration file. Gatus shines when you want declarative, code-reviewable monitoring that lives alongside your infrastructure.

Key Features

• Multi-protocol support: HTTP(S), TCP, DNS, ICMP, StartTLS, TLS, SSH
• Condition-based alerts: Validate response time, status codes, body content, certificate expiry, IP addresses
• Built-in status page: Auto-generated, customizable status page with incident tracking
• Alert integrations: Slack, PagerDuty, Discord, Teams, Email, AWS SNS, Mattermost, and 20+ more
• Storage backends: SQLite (default), PostgreSQL, or in-memory
• Configuration as code: Single YAML file, version-controllable and deployable via GitOps

Docker Deployment

Gatus ships as a single container image. Create a config.yaml with your endpoint definitions:

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endpoints:
  - name: example-api
    url: "https://api.example.com/health"
    interval: 1m
    conditions:
      - "[STATUS] == 200"
      - "[BODY].status == ok"
      - "[RESPONSE_TIME] < 500"
    alerts:
      - type: slack
        enabled: true
        failure-threshold: 2
        success-threshold: 1

  - name: example-dns
    url: "dns://8.8.8.8/example.com"
    interval: 5m
    conditions:
      - "[BODY] == 93.184.216.34"

  - name: example-tls
    url: "tls://example.com:443"
    interval: 1h
    conditions:
      - "[CERTIFICATE_EXPIRATION] > 48h"

Deploy with Docker Compose:

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services:
  gatus:
    image: twinproduction/gatus:latest
    restart: unless-stopped
    ports:
      - "8080:8080"
    volumes:
      - ./config:/config
      - gatus-data:/data
    environment:
      - GATUS_CONFIG_PATH=/config/config.yaml

volumes:
  gatus-data:

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docker compose up -d

Gatus starts immediately and begins probing. Access the dashboard at http://localhost:8080. The status page at http://localhost:8080/status shows a clean, color-coded overview of all endpoints.

Uptime Kuma: Beautiful GUI-Based Uptime Monitoring

Uptime Kuma (86,003 stars) is the most popular self-hosted monitoring tool. Its strength lies in a polished web UI that makes it easy to set up monitors, configure notifications, and view uptime statistics — all without writing configuration files.

Key Features

• 20+ monitor types: HTTP(s), TCP, HTTP keyword, DNS, Push, Steam Game Server, Docker containers, and more
• Real-time dashboard: Beautiful, responsive UI with uptime percentage graphs and response time charts
• Notification channels: 40+ integrations including Telegram, Discord, Slack, Gotify, Webhooks, Email, SMS, and many more
• Multi-language support: Translated into 30+ languages
• Status pages: Create custom-branded status pages with domain mapping
• Incident management: Automatic incident tracking with timeline views
• Proxy and certificate monitoring: Verify proxy chains and SSL certificate details

Docker Deployment

Uptime Kuma uses a single container with a persistent data volume:

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services:
  uptime-kuma:
    image: louislam/uptime-kuma:1
    restart: unless-stopped
    volumes:
      - uptime-kuma-data:/app/data
    ports:
      - "3001:3001"

volumes:
  uptime-kuma-data:

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docker compose up -d

After the container starts, open http://localhost:3001 and walk through the setup wizard. You’ll create an admin account, then begin adding monitors through the UI. Each monitor lets you set the check interval (minimum 20 seconds), notification rules, and alert thresholds through point-and-click forms — no YAML required.

k6: Scriptable Load Testing and Synthetic Monitoring

k6 (30,459 stars) is primarily known as a load testing tool, but its scripting engine makes it equally powerful for synthetic monitoring. Unlike Gatus and Uptime Kuma which run on fixed schedules, k6 checks are defined in JavaScript — enabling complex multi-step scenarios, data validation, and conditional logic that static configuration cannot express.

Key Features

• JavaScript scripting: Write complex multi-step checks with loops, conditionals, and data extraction
• Browser automation: Built-in browser module (Chromium) for real user journey simulation
• Protocol support: HTTP/1.1, HTTP/2, WebSocket, gRPC, and native browser interactions
• Grafana integration: Native output to Grafana Cloud, Prometheus, InfluxDB, or JSON
• Threshold-based pass/fail: Define SLO thresholds that determine check success
• CI/CD pipeline integration: Run as part of your CI pipeline or on a cron schedule
• Extensible ecosystem: xk6 extensions for custom protocols and functionality

Docker Deployment

k6 runs as an ephemeral container — ideal for cron-driven synthetic checks:

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services:
  k6:
    image: grafana/k6:latest
    volumes:
      - ./scripts:/scripts
      - ./output:/output
    command: run /scripts/healthcheck.js
    environment:
      - K6_OUT=influxdb=http://influxdb:8086/k6

For scheduled execution, use a cron wrapper:

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services:
  k6-cron:
    image: grafana/k6:latest
    volumes:
      - ./scripts:/scripts
    entrypoint: ["/bin/sh", "-c"]
    command:
      - |
        while true; do
          k6 run /scripts/healthcheck.js
          sleep 300
        done

Example health check script (healthcheck.js):

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import http from 'k6/http';
import { check, sleep } from 'k6';
import { Trend } from 'k6/metrics';

const responseTime = new Trend('response_time', true);

export const options = {
  thresholds: {
    http_req_duration: ['p(95)<500'],
    response_time: ['p(95)<1000'],
  },
};

export default function () {
  // Step 1: Check API health endpoint
  const healthRes = http.get('https://api.example.com/health');
  check(healthRes, {
    'status is 200': (r) => r.status === 200,
    'response body ok': (r) => r.json('status') === 'ok',
    'response time < 500ms': (r) => r.timings.duration < 500,
  });
  responseTime.add(r.timings.duration);

  sleep(1);

  // Step 2: Verify login flow
  const loginRes = http.post('https://api.example.com/auth/login', JSON.stringify({
    username: 'test-user',
    password: 'test-pass',
  }), {
    headers: { 'Content-Type': 'application/json' },
  });
  check(loginRes, {
    'login succeeds': (r) => r.status === 200,
    'returns token': (r) => r.json('token') !== undefined,
  });

  sleep(1);
}

Run the check:

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docker compose run --rm k6 run /scripts/healthcheck.js

k6 outputs detailed results including response time percentiles, pass/fail status for each check, and can stream metrics to Grafana for visualization.

Feature Comparison

When to Choose Each Tool

Choose Gatus if:

• You want configuration-as-code monitoring that lives in Git
• You need a status page that automatically reflects endpoint health
• Your team prefers YAML over GUI for infrastructure management
• You need StartTLS and certificate expiry checks alongside HTTP probes
• You want lightweight, single-container deployment with minimal dependencies

Choose Uptime Kuma if:

• You want a polished, zero-config web UI for non-technical team members
• You need broad notification channel support (40+ options out of the box)
• You want uptime percentage dashboards with minimal setup
• You monitor a mix of web services, game servers, and Docker containers
• You prefer point-and-click configuration over writing YAML or scripts

Choose k6 if:

• You need multi-step user journey simulation (login → browse → checkout)
• You want to combine synthetic checks with load testing using the same scripts
• You need browser-based synthetic monitoring with real Chromium rendering
• Your team is already invested in the Grafana ecosystem
• You run checks as part of CI/CD pipelines and want threshold-based pass/fail gates

Combining Tools for Complete Coverage

Many teams use two or more of these tools together. A common pattern:

1. Gatus runs every 60 seconds, checking 50+ endpoints via YAML config, serving the public status page
2. k6 runs every 5 minutes via cron, executing multi-step user journey scripts with browser validation
3. Uptime Kuma provides a secondary visual dashboard and sends notifications to channels Gatus doesn’t support

This layered approach catches different failure modes: Gatus validates endpoint-level health, k6 catches user-flow regressions, and Uptime Kuma ensures human operators see real-time status updates.

FAQ

What is the difference between synthetic monitoring and passive monitoring?

Synthetic monitoring actively probes your services on a schedule, simulating real user requests to verify availability and performance. Passive monitoring (like Prometheus, Grafana, or NetData) collects metrics from services that are already running. Synthetic monitoring catches outages that prevent any data from being collected — for example, if your entire web server crashes, passive monitoring can’t report on it, but a synthetic check from an external machine will detect the failure immediately.

Can Gatus and Uptime Kuma replace SaaS tools like Pingdom or Checkly?

Yes, for most self-hosted use cases. Both tools support HTTP, TCP, and DNS checks with configurable intervals and alerting. The main trade-off is geographic coverage: SaaS tools probe from dozens of global locations, while self-hosted tools probe from wherever you deploy the container. To replicate multi-region monitoring, you can run instances in different cloud regions and aggregate results.

How does k6 differ from traditional synthetic monitoring tools?

k6 is scriptable — you write JavaScript to define checks, which means you can handle authentication flows, parse JSON responses, extract tokens, and perform conditional logic. Traditional tools like Gatus and Uptime Kuma use static configurations (YAML or GUI forms) that validate single requests. k6 is more powerful but requires coding skills; the others are faster to set up for simple health checks.

Can I run synthetic checks from multiple geographic locations?

Yes. Deploy Docker containers of any of these tools in different cloud regions (AWS us-east-1, eu-west-1, ap-southeast-1, etc.) and aggregate results. For Gatus, each instance can send results to a central PostgreSQL database. For k6, stream metrics to a central Grafana or Prometheus instance. Uptime Kuma does not natively support multi-instance aggregation, but you can use webhooks to forward alerts to a central system.

What is the minimum resource requirement for self-hosted synthetic monitoring?

All three tools run comfortably on a single small VPS (1 vCPU, 512 MB RAM). Gatus is the lightest — a single container with SQLite uses under 50 MB RAM. Uptime Kuma with its Node.js backend uses around 150-200 MB. k6 is ephemeral, so it only uses resources while checks are running. For hundreds of endpoints at 1-minute intervals, a 2 vCPU / 1 GB RAM instance is sufficient.

Further Reading

For a broader perspective on self-hosted monitoring infrastructure, see our HertzBeat vs Prometheus vs NetData comparison and the complete observability stack guide. If you’re also looking for status pages, our Cachet vs Statping vs Upptime guide covers complementary tools.