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Elastic Observability overview

Serverless Stack

Elastic Observability provides unified observability across applications and infrastructure. It combines logs, metrics, application traces, user experience data, and more into a single, integrated platform. This consolidation allows for powerful, cross-referenced analysis, enabling teams to move from detecting issues to understanding their root causes quickly and efficiently. By leveraging the search and analytics capabilities of Elasticsearch, it offers a holistic view of system behavior.

Elastic Observability embraces open standards like OpenTelemetry for flexible data collection, and offers scalable, cost-efficient data retention with tiered storage.

For a complete overview, refer to What is Elastic Observability?.

Apply Observability to various scenarios to improve operational awareness and system reliability.

Use cases
  • Log monitoring and analytics: Centralize and analyze petabytes of log data from any source. This enables quick searching, ad-hoc queries with ES|QL, and visualization with prebuilt dashboards to diagnose issues.
  • Application Performance Monitoring (APM): Gain code-level visibility into application performance. By collecting and analyzing traces with native OTel support, teams can identify bottlenecks, track errors, and optimize the end-user experience.
  • Infrastructure monitoring: Monitor metrics from servers, virtual machines, containers, and serverless environments with over 400 out-of-the-box integrations, including OpenTelemetry. This provides deep insights into resource utilization and overall system health.
  • AI-powered log analysis with Streams: Ingest raw logs in any format directly to a single endpoint without the need for complex agent management or manual parsing pipelines. Streams leverages AI to automatically parse, structure, and analyze log data on the fly.
  • Digital experience monitoring:
    • Real User Monitoring (RUM): Capture and analyze data on how real users interact with web applications to improve perceived performance.
    • Synthetic monitoring: Proactively simulate user journeys and API calls to test application availability and functionality.
    • Uptime monitoring: Continuously check the status of services and applications to ensure they are available.
  • Universal Profiling: Gain visibility into system performance and identify expensive lines of code without application instrumentation, helping to increase CPU efficiency and reduce cloud spend.
  • LLM Observability: Gain deep insights into the performance, usage, and costs of Large Language Model (LLM) prompts and responses.
  • Incident response and management: Investigate operational incidents by correlating data from multiple sources, accelerating root cause analysis and resolution.

To start your Observability journey, read the Get started guide, which presents all the essential steps, with links to valuable resources. You can also browse the Observability Quickstart guides.

At the heart of Elastic Observability are several key components that enable its capabilities.

Concepts

  • The three pillars of Observability are:

    • Logs: Timestamped records of events that provide detailed, contextual information.
    • Metrics: Numerical measurements of system performance and health over time.
    • Traces: Representations of end-to-end journeys of requests as they travel through distributed systems.

  • OpenTelemetry: Observability offers first-class, production-grade support for OpenTelemetry. This allows organizations to use vendor-neutral instrumentation and stream native OTel data without proprietary agents, leveraging the Elastic Distribution of OpenTelemetry (EDOT).

  • AIOps and AI Assistant: Leverages predictive analytics and an LLM-powered AI Assistant to reduce the time required to detect, investigate, and resolve incidents. This includes zero-config anomaly detection, pattern analysis, and the ability to surface correlations and root causes.

  • Alerting and Cases: Allows you to create rules to detect complex conditions and perform actions. Cases allows teams to stay aware of potential issues and track investigation details, assign tasks, and collaborate on resolutions.

  • Service Level Objectives (SLOs): A framework for defining and monitoring the reliability of a service. Elastic Observability allows for creating and tracking SLOs to ensure that performance targets are being met.

Related reference

The Observability reference documentation is available in the Elastic reference documentation.

You can also browse reference documentation for the following components:

Browse the latest Observability release notes for more information on new features, enhancements, and fixes.