Indexer Monitoring: Prometheus vs Custom Dashboards

Battle-tested ecosystem: Integrates seamlessly with Grafana, Alertmanager, and Kubernetes. This matters for teams needing a production-grade SRE stack with established best practices and community support.

Powerful query language (PromQL): Enables complex, real-time analysis of time-series data (e.g., rate(indexer_rpc_errors_total[5m]) > 0.1). This is critical for deep performance debugging and creating dynamic alerts.

Direct database queries: Build views on top of your indexer's data model (e.g., Postgres). This matters for business-facing metrics like unique active wallets, protocol revenue, or NFT collection health that aren't exposed as system metrics.

No agent overhead: Directly visualize data you already store. This is ideal for smaller teams or early-stage protocols that need to move fast, prototype new views, and have full control over data aggregation without managing a separate metrics pipeline.

Industry-standard tooling: Leverages the CNCF-backed Prometheus for metrics collection and Grafana for visualization. This matters for teams that need vendor-agnostic, portable monitoring with a vast library of pre-built dashboards (e.g., for Node.js, Postgres, Redis) and integrations with Alertmanager for sophisticated notification routing.

Pull-based architecture allows centralized scraping of thousands of indexer targets. This matters for large, distributed deployments where you need to aggregate metrics from multiple RPC nodes, indexer workers, and databases. Supports long-term storage via Thanos or Cortex for analyzing historical performance trends over months.

Direct access to business logic metrics: Build visualizations for specific indexer states like sync lag per chain, missed event counts, or custom health checks. This matters for protocol-specific monitoring where standard metrics fall short, allowing you to track the exact SLAs for your subgraph or indexer service.

Significant engineering overhead: Requires building, maintaining, and scaling your own data collection, storage, and visualization layer. This matters for early-stage projects or small teams where diverting 2-3 senior engineers from core protocol development to build monitoring infrastructure is a prohibitive opportunity cost.

Established Standard: Integrates natively with Grafana, Kubernetes, and 100+ exporters. This matters for teams with existing SRE practices who need to monitor indexer health, pod restarts, and infrastructure metrics alongside application data.

PromQL for Deep Analysis: Enables complex, real-time queries for anomaly detection (e.g., rate(indexer_errors_total[5m]) > 0). This is critical for diagnosing indexing lag, RPC error spikes, or sudden drops in request volume from subgraphs.

Protocol-Specific KPIs: Build visualizations for metrics like Delegator APY, Indexer Cut %, or Subgraph Curation Signals. This matters for stakeholders needing insights beyond system health, directly tied to protocol economics and performance.

Avoid Vendor Roadmaps: Implement alerts for new subgraph versions or specific contract events immediately. This is essential for agile teams managing high-value subgraphs (e.g., for Uniswap v4 hooks) who cannot wait for generic tooling to catch up.

Infrastructure Burden: Requires managing time-series database storage, retention policies, and scaling the Prometheus server itself. This is a significant tax for small teams who should focus on core indexing logic, not monitoring infra.

Brittle Data Pipelines: Custom scripts to ingest chain data (via RPCs) or indexer APIs can break with upstream changes. This creates alert fatigue and hidden blind spots, unlike the stable agent model of Prometheus exporters.