What is an observability platform?

An observability platform is a comprehensive set of observability tools and dashboards that allows users to get a better understanding of the state and health of their applications and systems.

As software grows more complex, using a robust observability platform is more important than ever to achieve better reliability, performance, and user experience.

Components of an observability platform

Observability platforms ingest various types of data and process them in an efficient manner to surface critical insights.

At the very least, observability platforms must handle the three pillars of observability, namely logs, metrics, and traces. A more robust platform would centralize these data streams, show correlation between them, and visualize them.

These pillars alone do not make for a great observability platform. They are just building blocks for individual observability tools or software. A great observability platform in turn drive the following outcomes:

  • Continuous monitoring for issue detection
  • Alerts and incident management
  • Anomaly detection
  • Capacity planning and recommendations for resource optimization
  • Proactive issue detection

Popular observability platforms include a collection of open source tools and enterprise offerings.

Examples of open source observability platforms:

  • Prometheus + Grafana
  • ELK (Elasticsearch, Logstash, Kibana) stack
  • Loki, Jaeger, Zipkin

Enterprise observability platforms include:

  • Datadog
  • New Relic
  • Dynatrace
  • Honeycomb
  • Splunk

There are also developer observability platforms such as Lightrun that implement an alternative approach. Instead of diving through existing logs, metrics, traces and events that may or may not help in troubleshooting, Lightrun allows developers to understand anything that happens in a live application — on demand, in real time.

Choosing an observability platform

When choosing an observability platform. first decide if you want to build a custom platform by combining various open source tools together, or go with an enterprise option.

Using open source tools may be cheaper but could present a maintenance burden. Also, if data governance and ownership is a requirement either for compliance or security reasons, going with an open source option may be necessary.

Next, it’s important to consider the scalability and performance of each platform, especially against the cost. While most modern observability platforms support top standards like OpenTelemetry and provide SDKs for various programming languages and deployment models, it’s critical to look at software compatibility and support. Consider integrations with other existing software and toolchains like incident management or task management software.

Finally, consider the security and compliance requirements for data retention or immutability. If you work in industries were data may be privileged or sensitive (e.g., healthcare, finance, government), then these compliance-related features may be top of mind.