Elastic APM

Elastic APM is the application performance monitoring component of the Elastic Stack (formerly known as the ELK Stack), developed by Elastic NV, a company founded in Amsterdam, Netherlands in 2012 by Shay Banon. Built on top of Elasticsearch, Kibana, and the Elastic Agent, Elastic APM provides comprehensive full-stack observability including distributed tracing, real user monitoring, error tracking, log correlation, and machine learning-powered anomaly detection. As a European-founded company with strong open-source roots, Elastic offers organizations a powerful alternative to US-based APM solutions like New Relic, Datadog, and Dynatrace.

The Elastic Stack Foundation

Elastic APM is not a standalone product but an integral part of the broader Elastic Observability solution, which itself is built on the Elastic Stack. This foundation provides significant advantages: all APM data (traces, metrics, logs, and errors) is stored in Elasticsearch, one of the most powerful search and analytics engines in the world. Kibana provides the visualization and dashboard layer, offering rich, customizable views of application performance data. This unified platform means that APM data can be correlated with infrastructure metrics, application logs, and security events in a single interface.

The Elastic Stack has been used by thousands of organizations worldwide for over a decade, making it one of the most battle-tested data platforms available. By building APM on this foundation, Elastic ensures that teams do not need to manage separate tools for different observability signals. Everything flows into the same Elasticsearch cluster, enabling cross-cutting analysis that would be difficult or impossible with disparate tools. This unified approach is one of Elastic APM's strongest differentiators.

Distributed Tracing

Elastic APM provides comprehensive distributed tracing that follows requests as they traverse microservices, databases, message queues, and external APIs. Each transaction is captured with detailed timing information, showing exactly where time is spent across the entire request lifecycle. The trace waterfall view in Kibana makes it easy to identify slow services, failed calls, and performance bottlenecks in complex distributed architectures.

Elastic APM supports the W3C Trace Context standard and OpenTelemetry, ensuring compatibility with industry-standard instrumentation. This means teams can use Elastic's own APM agents or send data from OpenTelemetry-instrumented applications, providing flexibility in how they instrument their code. The distributed tracing implementation captures service maps automatically, showing the relationships between services and their performance characteristics without requiring manual configuration.

APM Agents and Language Support

Elastic provides official APM agents for all major programming languages and frameworks, including Java, .NET, Node.js, Python, Ruby, Go, PHP, and real user monitoring (RUM) for frontend JavaScript applications. These agents automatically instrument common frameworks and libraries, capturing HTTP requests, database queries, external service calls, and custom transactions with minimal code changes required.

The agents are designed to be lightweight with low overhead, typically adding less than 5% latency to monitored applications. Configuration is flexible, with options for dynamic settings that can be changed without application restart. For teams using OpenTelemetry, Elastic APM Server also accepts data from OpenTelemetry agents and SDKs, allowing organizations to use a vendor-neutral instrumentation approach while still benefiting from Elastic's powerful analytics and visualization capabilities.

Error Tracking and Exception Monitoring

Elastic APM captures application errors and exceptions automatically, grouping them by type, message, and stack trace to reduce noise and help teams focus on the most impactful issues. Each error is linked to the transaction and trace context in which it occurred, providing full context for debugging. Error trends are tracked over time, making it easy to spot new errors, regressions, or error rate spikes after deployments.

The error tracking capabilities go beyond simple logging. Elastic APM captures the full stack trace, relevant metadata (user info, request parameters, custom context), and correlates errors with the specific transaction and distributed trace. This context-rich error data significantly reduces the time needed to diagnose and fix production issues compared to traditional logging approaches where developers must manually piece together information from multiple sources.

Real User Monitoring (RUM)

The Real User Monitoring agent captures frontend performance data from actual user browsers, including page load times, JavaScript errors, network requests, and user interaction timing. RUM data is correlated with backend APM traces, providing end-to-end visibility from the user's browser through the backend services and databases. This full-stack visibility is essential for understanding the actual user experience rather than just server-side performance.

RUM metrics include First Contentful Paint, Largest Contentful Paint, Time to Interactive, and other Core Web Vitals metrics that directly impact user experience and SEO rankings. Teams can segment performance data by geography, browser type, device, and page URL to identify performance issues affecting specific user segments. This granularity enables targeted optimization efforts that have the biggest impact on actual user experience.

Machine Learning Anomaly Detection

Elastic APM leverages the machine learning capabilities built into the Elastic Stack to automatically detect anomalies in application performance. The ML models learn normal behavior patterns for each service, transaction type, and metric, then alert when deviations occur. This approach is more effective than static thresholds because it adapts to seasonal patterns, traffic variations, and gradual changes in application behavior.

Anomaly detection works across multiple dimensions simultaneously, detecting issues that would be invisible to simple threshold-based alerting. For example, it can detect a subtle increase in response time for a specific transaction type that coincides with higher-than-usual error rates, even if neither metric exceeds a static threshold on its own. This proactive detection helps teams identify and resolve performance issues before they impact users significantly.

Infrastructure Monitoring Integration

Elastic APM integrates seamlessly with Elastic's infrastructure monitoring capabilities. The Elastic Agent can collect system metrics (CPU, memory, disk, network), container metrics (Docker, Kubernetes), and cloud provider metrics alongside APM data. This integration means that application performance issues can be correlated with infrastructure events, such as high CPU usage on a host, memory pressure in a container, or network latency between availability zones.

For Kubernetes environments, Elastic provides rich monitoring including pod-level metrics, container logs, and automatic discovery of services running in the cluster. The service map view shows how applications are connected and how infrastructure resources are utilized, providing a comprehensive operational picture. This holistic approach to observability eliminates the need for separate infrastructure monitoring tools and the integration challenges that come with them.

Log Correlation

One of Elastic APM's most powerful features is log correlation. Each APM trace includes a unique trace ID that can be injected into application logs, creating a direct link between distributed traces and the corresponding log entries. This means that when investigating a slow transaction or error, developers can instantly jump from the APM trace to the relevant log entries without manually searching through log files.

The log correlation works with all major logging frameworks across supported languages. Combined with Elasticsearch's powerful full-text search capabilities, teams can search, filter, and analyze correlated logs in real-time. This integration transforms logs from a separate, often disconnected data source into a contextual part of the observability story, dramatically reducing mean time to resolution for production issues.

Open Source and Licensing

Elastic has a complex but transparent licensing model. The Elastic Stack core, including Elasticsearch and Kibana, is available under the Elastic License 2.0 (ELv2) and Server Side Public License (SSPL). The APM agents are open source under the Apache 2.0 license. Elastic also offers a free tier on Elastic Cloud that includes APM capabilities. The self-managed option allows organizations to run the entire stack on their own infrastructure at no licensing cost for the basic features.

The open-source nature of the APM agents means that organizations can audit the instrumentation code, verify that no sensitive data is being collected without consent, and contribute improvements. The broader Elastic community contributes plugins, integrations, and dashboards that extend the platform's capabilities. For organizations that need the assurance of seeing exactly how their monitoring tools work, this transparency is invaluable.

Deployment Options and GDPR Compliance

Elastic APM can be deployed in three ways: self-managed on your own infrastructure, on Elastic Cloud (the managed service), or on Elastic Cloud with data residency in specific regions including multiple EU locations. The self-managed option gives organizations complete control over where their data is stored and processed, making GDPR compliance straightforward. Elastic Cloud offers EU-region deployments in AWS, GCP, and Azure data centers located in Europe.

Elastic NV, founded in Amsterdam, maintains its European heritage. The company's Dutch incorporation means it operates under EU corporate governance. For the managed cloud service, selecting EU data centers ensures that all observability data - including potentially sensitive request parameters, user information, and error details captured by APM - remains within European jurisdiction. This is a significant advantage over US-headquartered competitors where data sovereignty is more difficult to guarantee.

Pricing Structure

Elastic offers a free tier on Elastic Cloud that includes APM capabilities suitable for development and small-scale production use. Paid Elastic Cloud plans start from $95 per month and scale based on resource consumption (storage, compute, and data transfer). Self-managed deployments using the basic license tier are free, with paid subscriptions (Gold, Platinum, Enterprise) adding features like machine learning, advanced security, and premium support.

The consumption-based pricing model means organizations pay for what they actually use rather than per-host or per-agent fees that can become expensive in dynamic cloud environments. This model is particularly advantageous for organizations with variable workloads or those running on Kubernetes where the number of containers can fluctuate dramatically. However, high-volume environments should carefully model their expected data ingestion to avoid unexpected costs.

Who Should Use Elastic APM

Elastic APM is ideal for organizations that want a unified observability platform combining APM, infrastructure monitoring, log management, and security analytics in a single stack. Teams already using Elasticsearch for logging or search will find the integration seamless and compelling. Organizations that need self-hosted deployment options for data sovereignty or compliance requirements benefit from the flexibility to run the entire stack on their own infrastructure. European companies looking for an APM solution from a company with strong European roots will appreciate Elastic's Amsterdam origins and EU cloud deployment options. DevOps teams and SREs who value open-source transparency and OpenTelemetry compatibility will find Elastic APM a powerful and flexible choice for full-stack observability.