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A Self-Hosted Datadog Alternative in One Docker Compose Command

TL;DR: I built a full observability stack -- metrics, logs, traces, alerting, and AI anomaly detection -- that starts with a single docker compose up -d.

The Problem

Datadog is excellent but expensive. At $23+ per host per month for infrastructure monitoring alone, costs scale fast when you're running multiple environments. I needed production-grade observability -- not a toy Grafana dashboard -- without the SaaS bill. And I wanted it deployable in under two minutes for any new project.

What I Built

observability-stack bundles Prometheus, Grafana, Loki, Tempo, Alertmanager, Node Exporter, cAdvisor, and a custom AI anomaly analyzer into a single Docker Compose file. Every component is pre-wired: Grafana has datasources and dashboards provisioned on first boot, Prometheus scrapes all the right targets, and Alertmanager routes alerts through the AI analyzer before sending notifications.

  • Full metrics pipeline -- Prometheus + Node Exporter + cAdvisor with pre-built dashboards for host and container metrics
  • Centralized logging -- Loki ingests logs from all containers; query them directly in Grafana with LogQL
  • Distributed tracing -- Tempo receives OTLP traces over gRPC (port 4317) and correlates them with logs and metrics
  • AI anomaly detection -- a Python service connected to AWS Bedrock analyzes alert patterns and identifies root causes
  • Pre-built dashboards -- Node Overview (CPU, memory, disk, network) and Container Overview (per-container resource usage) ready on boot

Tech Stack

Docker Compose, Prometheus, Grafana, Loki, Tempo, Alertmanager, Node Exporter, cAdvisor, Python, AWS Bedrock

Usage

cp .env.example .env   # configure credentials
docker compose up -d   # start everything

# Access points:
# Grafana:      http://localhost:3000  (admin/admin)
# Prometheus:   http://localhost:9090
# Alertmanager: http://localhost:9093
# AI Analyzer:  http://localhost:8000

Add a custom scrape target:

# configs/prometheus/prometheus.yml
scrape_configs:
  - job_name: "my-service"
    static_configs:
      - targets: ["my-service:8080"]

Results

New projects get full observability in under two minutes. The pre-wired dashboards mean I stop building the same Grafana panels from scratch every time. The AI analyzer has caught two subtle memory leak patterns that static thresholds missed -- it correlates metric trends with log spikes in ways that simple alerting rules cannot.

GitHub: https://github.com/basel5001/observability-stack