How system architectures have changed
Over time we moved from single, bundled applications to highly distributed systems. Monoliths—where frontend, backend, integration layers, and databases were packaged together—gave way to services that run across many hosts, regions, and platforms. This architectural shift accelerated with widespread public cloud adoption.
Where systems run today
Hosting models also evolved: from on-premises data centers to public and private clouds, and now to hybrid and multi-cloud deployments that span multiple providers and regions. This diversification adds operational complexity and increases the need for consistent visibility across environments.
From in-house monoliths to cloud-native platforms
Historically, in-house monoliths ran on application servers and platform stacks such as Microsoft IIS or Linux-based application hosts and connected to local databases. Today’s distributed systems run across regions and cloud providers and often use orchestration and managed platforms like Kubernetes, Azure App Service, and Google Cloud Run.
Why observability matters
Distributed architectures and multi-cloud deployments make root-cause analysis and performance tuning more challenging. Observability provides the telemetry and context teams need to understand system behavior, reduce mean time to detection (MTTD), and shorten mean time to resolution (MTTR).- For business teams: observability turns raw telemetry into actionable business insights and user behavior data, helping prioritize product and operational decisions.
- For engineering teams: it accelerates detection of performance regressions, pinpoints optimization opportunities, and improves incident response.
Observability is the practice of deriving meaningful insight about system state and behavior from telemetry data—metrics, traces, and logs—so teams can diagnose issues, predict problems, and optimize performance.
Core telemetry types and what they reveal
Use a combination of telemetry to get a complete view of system health and performance:
Combining these signals lets teams quickly triangulate the source of problems—e.g., a latency spike in metrics, traces that identify a slow downstream service, and logs that show the exception.
Placing observability in your architecture
Observability should be integrated end-to-end: from the user device through API gateways and integration layers, into microservices or serverless functions, and down to data stores and infrastructure. Instrument the cloud platform, orchestrator, and underlying infrastructure to avoid blind spots.
Quick reference: benefits by team
Further reading and references
- New Relic: What is observability? — definition and context for observability practice.
- Kubernetes documentation — platform guidance for orchestrated deployments.