Application Performance Monitoring
The practice of measuring and analyzing application behavior from the end-user perspective, tracking response times, error rates, throughput, and transaction traces. APM tools provide visibility into code-level performance issues that infrastructure monitoring cannot detect.
APM goes beyond infrastructure metrics to measure how the application itself performs. It tracks request latency distributions, identifies slow database queries, profiles code execution hotspots, and traces requests across microservice boundaries. Tools like New Relic, Datadog APM, Sentry, and Dynatrace provide automatic instrumentation that captures performance data without requiring manual code changes.
For AI product teams, APM reveals performance bottlenecks specific to AI workloads: slow model serialization, inefficient feature preprocessing, excessive database queries for context retrieval, and unnecessary data transformations. Growth teams rely on APM to ensure that AI features perform acceptably across all user segments: a recommendation engine might perform well for users with short histories but degrade for power users with extensive interaction data. APM data helps identify these segment-specific performance issues. Real User Monitoring, a subset of APM, measures actual end-user experience including network latency and client-side rendering, providing the complete picture of how AI features perform from the user's perspective.
Related Terms
Content Delivery Network
A geographically distributed network of proxy servers that caches and delivers content from locations closest to end users. CDNs reduce latency, improve load times, and absorb traffic spikes by serving content from edge nodes rather than a single origin server.
Edge Computing
A distributed computing paradigm that processes data closer to the source of generation rather than in a centralized data center. Edge computing reduces latency, conserves bandwidth, and enables real-time processing for latency-sensitive applications.
Serverless Computing
A cloud execution model where the provider dynamically manages server allocation and scaling. Developers deploy functions or containers without provisioning infrastructure, paying only for actual compute time consumed rather than reserved capacity.
Function as a Service
A serverless computing category where developers deploy individual functions that execute in response to events. FaaS platforms like AWS Lambda, Google Cloud Functions, and Azure Functions handle all infrastructure management, scaling each function independently.
Platform as a Service
A cloud computing model that provides a complete development and deployment environment without managing underlying infrastructure. PaaS offerings like Heroku, Vercel, and Google App Engine handle servers, storage, networking, and runtime configuration.
Infrastructure as a Service
A cloud computing model that provides virtualized computing resources over the internet. IaaS offerings like AWS EC2, Google Compute Engine, and Azure Virtual Machines give teams full control over servers, storage, and networking without owning physical hardware.