Uptime Monitoring: Grafana with Prometheus vs Custom Scripts

Comprehensive ecosystem: Pre-built dashboards for Node Exporter, Alertmanager, and Loki. This matters for teams needing deep visibility into system metrics (CPU, memory, disk I/O) and logs from day one.

• Standardized Alerts: Rich templating and routing (Slack, PagerDuty, Opsgenie).
• Historical Analysis: Stores 15+ days of metrics by default for trend analysis and post-mortems.

Infrastructure as a service: Requires managing 3-4 services (Prometheus, Grafana, exporters, Alertmanager). This matters for teams without dedicated SRE/DevOps resources.

• Resource Intensive: A single Prometheus instance can use 5-10GB RAM for high-cardinality blockchain data.
• Steeper Learning Curve: Requires knowledge of PromQL, dashboard design, and alert rule management.

Tailored to your stack: Scripts can use specific RPC calls (e.g., eth_blockNumber, cosmos_status) and logic for your chain. This matters for protocol-specific health checks beyond basic HTTP pings.

• Minimal Dependencies: Often just a cron job and a logging service. Faster initial setup for a single metric.
• Direct Integration: Easily embeds into existing deployment pipelines or admin panels.

Becomes unmanageable: Adding new nodes or metrics requires code changes. This matters for growing validator sets or multi-chain operations.

• No Built-in History: Requires building a separate data layer (e.g., a database) for any historical analysis.
• Alert Fatigue: Logic for deduplication, silencing, and escalation must be built from scratch, increasing bug risk.

Integrated ecosystem with Prometheus for metrics collection, Grafana for visualization, and Alertmanager for notifications. This provides a single pane of glass for thousands of time-series metrics, enabling complex dashboards and correlation that scripts cannot match. Ideal for teams needing historical trend analysis and deep, visual debugging.

Zero dependency bloat. Write scripts in Bash, Python, or Go to check exactly what you need, with no unnecessary metric collection. Perfect for simple, atomic health checks (e.g., curl -f, port checks) or proprietary logic that doesn't fit a metrics model. Minimal resource footprint on monitored hosts.

Seamless integration with existing CI/CD pipelines, internal APIs, or legacy systems. You can parse custom log formats or interact with hardware directly. Enables rapid prototyping for one-off investigations or monitoring for a new protocol/feature before building a full exporter. The development speed for a single check is often faster.

• Engineering teams > 5 people needing shared visibility.
• Long-term trend analysis and capacity planning.
• Complex service architectures (microservices, k8s) requiring standardized monitoring.
• Scenarios where alert history, silencing, and delegation are critical.

• Small, focused projects or MVP stages with limited scope.
• Edge cases and proprietary logic not covered by standard exporters.
• Environments with extreme resource constraints or security policies limiting new daemons.
• Temporary debugging or integrating with niche internal tooling.

Protocol-specific deep dives: Write scripts to monitor niche metrics like MEV bundle inclusion rates, validator churn in Cosmos, or L2 sequencer health checks that off-the-shelf tools don't capture. This matters for protocols with novel consensus mechanisms or performance requirements.

Rapid prototyping: Deploy a Python script using web3.py or ethers.js to ping an RPC endpoint in under an hour, versus days spent configuring and securing a Prometheus stack. This matters for small teams or during the early PoC phase where speed trumps scalability.

Infrastructure burden: Requires managing 3+ services (Prometheus, Grafana, exporters), persistent storage, and high-availability setups. This matters for teams without dedicated DevOps/SRE resources, as it can consume 20+ engineering hours per month in maintenance.

Alert fatigue and blind spots: Scripts often lack centralized state, leading to duplicate alerts and no historical context. Scaling beyond 20 nodes typically requires rebuilding on a framework like Prometheus anyway. This matters for growing networks where reliability becomes critical.