OpenTelemetry vs Prometheus: Choosing the Right Observability Approach

During a post-incident review, when your team needs to understand why customers experienced checkout delays during a high-traffic event, your ability to quickly diagnose the problem hinges on your observability infrastructure.

The OpenTelemetry vs Prometheus debate centers on two fundamentally different approaches to system monitoring:

• Prometheus: An all-in-one monitoring platform that requires jumping between separate tools for traces and logs to reconstruct incidents.
• OpenTelemetry: A unified framework that tracks complete user transactions through shared context across all telemetry signals.

Prometheus delivers a self-contained binary with integrated metrics collection, storage, querying, and alerting. OpenTelemetry functions as an instrumentation standard, collecting metrics, traces, and logs but requiring you to configure backend systems for storage and analysis. This architectural difference shapes how teams implement observability and respond to production issues.

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Core Architecture and Features

Prometheus

• Self-contained monitoring platform packaged as a single executable
• Includes:
  • Time-series data storage
  • HTTP-based collection engine
  • PromQL query interface
  • Alert management
  • Built-in visualization dashboard
• Simple deployment: install binary, start collecting metrics immediately

Example:

• Instrument a Go application by importing the client library and defining metric collectors
• Checkout service tracks transaction volumes via counter vectors with labels
• Prometheus accesses this data through HTTP endpoints exposing metric values

Architecture:

• Centralized server pulls metrics from multiple application instances and exporters
• Single-process system handles collection, storage, and query execution
• Simplifies deployment and reduces moving parts

OpenTelemetry

• Provides standardization, not a full monitoring implementation
• Defines common interfaces and protocols for metrics, traces, and logs
• Includes language-specific SDKs and collector components
• Collector forwards telemetry data to chosen backends (e.g., Prometheus, Jaeger, Loki, ClickHouse)

Architecture:

• Distributed system: applications → collectors → backends
• Modular approach allows flexible backend selection
• Telemetry generation and collection are separated from storage and analysis

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Data Collection Methods

Prometheus

• Pull-based model: central server requests metrics from monitored applications
• HTTP endpoints expose metrics using Prometheus’s dimensional structure
  • Example: checkout_requests_total{method="POST",status="200"} 42
• Failed scrapes indicate potential outages
• Configuration via manual scrape targets or service discovery
• Focuses exclusively on metrics; separate tools needed for traces/logs

OpenTelemetry

• Supports push and pull models
• Unified pipelines preserve correlation across metrics, traces, and logs
• Checkout service emits trace spans, metrics, and logs referencing the same context
• Collector applies processing rules and exports to backend systems
• Enables immediate correlation between traces, metrics, and logs
• Eliminates manual correlation needed in Prometheus-only setups

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Implementation and Operational Considerations

Prometheus

• Integrate client libraries into applications and expose metrics endpoints
• Configure Prometheus server: scrape targets, storage retention, PromQL queries
• Example: track request volumes and latency for checkout service
• Advantages:
  • Simple initial deployment
  • Fewer components to maintain
• Challenges:
  • Scaling beyond a single server requires federated architectures or remote storage
  • Limited correlation between metrics, traces, and logs

OpenTelemetry

• Instrument applications with SDKs, deploy collectors, configure backends
• Collector rules process and route metrics, traces, and logs
• Advantages:
  • Maintains context across telemetry signals
  • Supports multiple backends and modular architecture
  • Flexible and extensible for distributed systems
• Challenges:
  • Requires more upfront planning
  • Higher operational complexity
  • Coordination across multiple systems for storage, processing, and visualization

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Conclusion

Choosing between Prometheus and OpenTelemetry depends on your observability needs:

• Prometheus:

  • Integrated platform: collection, storage, querying, alerting
  • Minimal operational overhead
  • Best for infrastructure-focused monitoring and moderate scale

• OpenTelemetry:

  • Unified framework for metrics, traces, and logs
  • Maintains context across telemetry signals
  • Ideal for distributed applications needing comprehensive observability
  • Requires more operational planning and multiple backend management

Hybrid Approach:

• Some organizations combine both:
  • OpenTelemetry for instrumentation
  • Prometheus for metrics storage and analysis
• Leverages strengths of both approaches to meet specific monitoring requirements