Observability for Microservices vs Monoliths: Strategies that Worked in 2025
As organizations continue to evolve their software architectures, the debate of microservices versus monoliths remains central not only to development but also to how observability is approached. Observability, i.e., the practice of gaining deep insight into system behavior through telemetry data (logs, metrics and traces), must be aligned with the architecture pattern to monitor, troubleshoot and improve applications, effectively. In 2025, understanding the key differences and applying appropriate strategies is key for DevOps and site reliability engineering (SRE) teams to ensure system reliability and performance.
Observational Challenges in Monoliths
Monolithic applications are traditionally large, single-unit systems where various components are tightly connected to each other. Observability here focuses on:
- Centralized Logging and Monitoring: Because everything runs in the same process or environment, it’s easy to collect logs and metrics centrally.
- Identify Performance Bottlenecks: CPU-, memory- and I/O-related metrics can be tracked at the process or server level.
- Limited Distributed Tracing: Tracing within a monolith mainly focuses on function calls and database interactions within a single runtime.
- The Biggest Challenge Comes as Monoliths Grow: They become increasingly difficult to understand from the inside, and locating problems can take a long time without granular telemetry.
Observability Challenges in Microservices
Microservice architectures break applications down into loosely coupled, independently deployable services that communicate over networks. Thus, the complexity of observability increases significantly:
- Distributed Tracing Is Essential: Tracing requests across multiple services and network calls becomes key to identifying response times or errors.
- Connect Telemetry Data: Logs, metrics and traces come from a multitude of sources and must be connected to gain actionable insights.
- Dynamic Infrastructure: Containerized serverless functionality and staging instances lead to ever-changing operating environments where traditional monitoring is insufficient.
- Managing Data Volumes: The volume of telemetry data grows exponentially, which necessitates scalable data collection and processing.
Strategies for Observability: Microservices vs. Monoliths
| Aspect | Monolith Strategy | Microservices Strategy |
| Instrumentation | Central SDK/libraries for logs, metrics within the app | Use standardized instrumentation (e.g., OpenTelemetry) per service |
| Tracing | Application-level tracing, database query tracing | Distributed tracing across services with trace context propagation |
| Logging | Aggregated logs from a single source | Centralized log aggregation with service-level tagging |
| Metrics | Host or application process metrics | Fine-grained service and infrastructure metrics |
| Alerting & Visualization | Focus on server/application metrics and error logs | Contextual alerts combining distributed traces and metrics |
| Tooling | Traditional application performance management (APM) and monitoring tools | Modern observability platforms supporting distributed topology |
| Data Management | Manage moderate telemetry volume | Scalable telemetry ingestion and correlation |
| Deployment | Static environment; fewer changes | Dynamic, ephemeral environments with frequent deployments |
Grafana, Middleware and Honeycomb are some of the best observability tools for monitoring microservices.
Best Practices to Follow in 2026
- Hardware Standardization Across Services: Regardless of architecture, utilize OpenTelemetry as a vendor-neutral framework to ensure consistency and interoperability.
- Invest in Distributed Tracing for Microservices: For microservices, tracing is not optional — use solutions that support context propagation across asynchronous calls and different languages.
- Centralize Telemetry Data: Use platforms that unify logs, metrics and traces for end-to-end observability, enabling troubleshooting across dimensions.
- Implement Contextual Alerting: Move from static threshold alerts to context-rich smart notifications to reduce alert fatigue and improve response times.
- Build Observability Early (Shift Left): Incorporate observability into development and testing to catch problems before production, especially in microservices.
- Leverage Automation: Use AI/ML-based analytics to detect anomalies and analyze root causes to manage the complexity and volume of data, especially in distributed environments.
- Ensure Secure and Compliant Observability: Integrate security telemetry and meet compliance standards (e.g., GDPR) through data management policies.
- Dynamic Infrastructure Monitoring: Monitor temporary infrastructure components in microservices environments through dynamic service, service discovery and tagging.
Conclusion
While monoliths allow simpler observability settings with a focus on centralized logging and host metrics, microservices require more sophisticated distribution and monitoring strategies. The dynamic nature of microservices requires investment in distributed tracing, modular hardware and intelligent analytics. By implementing monitoring strategies and best practices for architecture, teams can maintain high reliability, performance and customer satisfaction for their software systems in 2025 and beyond.
This thoughtful approach to observability adapts to the evolving complexity of modern applications, enabling DevOps teams to deliver flexible software efficiently.
