API endpoint monitoring that names the slow route instead of averaging it away — per-endpoint latency, errors, and Apdex, linked straight to the trace behind it.
API Endpoint Monitoring shows how each API endpoint in your application behaves under real traffic, one route at a time. Instead of judging an application by a single aggregate number, Motadata ObserveOps breaks API monitoring down to the individual endpoint, so you can see which routes are fast, which are failing, and which carry the load.
API Endpoint Analysis captures per-endpoint performance and behavior directly from instrumented application code as part of Application Performance Monitoring (APM). Every request that flows through an endpoint contributes to its performance profile, so slow or error-prone routes surface on their own instead of hiding inside an averaged view. Because the data is gathered at the endpoint level, an API monitoring dashboard covering dozens of routes stays legible: each path keeps its own performance story rather than blending into one service-wide figure.
This capability sits inside APM and reads application-code behavior only. When an endpoint's slowness comes from infrastructure such as node health or storage, that signal belongs to the Hybrid Infrastructure module, and when the question is what a browser experienced calling that endpoint, that belongs to Real User Monitoring. ObserveOps cross-links to both rather than reporting on either layer here.
Because API Endpoint Analysis runs on the same instrumentation collecting APM's traces, it inherits the module's full language coverage and its OpenTelemetry foundation through a single unified MotaAgent, so endpoint data, trace data, and service topology all come from one consistent source.
API Endpoint Analysis measures each endpoint on its own, so latency, error behavior, and traffic are visible route by route instead of collapsed into one application-wide figure.
The API Endpoint Summary tab includes single-click filters for GET, POST, PUT, PATCH, DELETE, HEAD, and OPTIONS, so you can isolate method-specific traffic and compare how each verb performs.
Each endpoint carries the approved APM performance signals: P99 latency, error rate, and throughput measured as requests or transactions per second, alongside an Apdex measure of user satisfaction.
Endpoint behavior connects to the underlying distributed traces built on OpenTelemetry standards, so a slow or failing endpoint leads directly to the request path behind it. Frequently used trace fields can be pinned so they stay visible while an engineer inspects the trace an endpoint produced.
Filtering by HTTP verb on the API Endpoint Summary tab shows how errors distribute across methods, so a spike concentrated in POST or DELETE traffic stands out from healthy read paths.
A single unified MotaAgent instruments the application code behind every endpoint across APM's eight supported languages, so endpoint monitoring does not require a separate agent or a separate setup step per language.
eBPF/OBI-based instrumentation discovers services automatically, without code changes or an agent install, and captures request count, latency, and error rate alongside traces and spans. Those readings surface in the same APM views, giving a service baseline before deeper endpoint-level instrumentation is configured.
Java Auto-Discovery identifies applications ready for monitoring and enables instrumentation in a single click, so endpoint coverage for a Java service does not wait on a manual setup project.
Custom Reports in Chart, Grid, Top-N, Counter, and Aggregation formats can be built over API Endpoint Analysis data, turning a recurring review of the slowest or highest-error endpoints into a saved report rather than a manual pull.
A No Trace Received alert can be configured during service registration to flag an endpoint's service when no traces arrive within a defined period, surfacing a silent or mis-instrumented integration before it is discovered by a support ticket.
A single unified MotaAgent instruments the application code and captures per-endpoint activity as requests flow through each route. Distributed tracing built on OpenTelemetry standards records the underlying spans, so endpoint data and trace data come from the same instrumented source. Java, .NET, PHP, Node.js, Python, and Go endpoints are captured across Host/VM, Docker, and Kubernetes.
For C++ and Ruby, endpoint instrumentation runs on Host/VM and Docker.
Java Auto-Discovery adds one-click instrumentation for eligible Java services, and the code-free eBPF/OBI path collects baseline endpoint telemetry with no agent install at all.
Collected data is analyzed per endpoint against P99 latency, error rate, throughput, and Apdex. On the API Endpoint Summary tab, HTTP method filters for GET, POST, PUT, PATCH, DELETE, HEAD, and OPTIONS narrow the analysis to a single verb so method-specific behavior and error distribution stand out. A No Trace Received alert, configured at service registration, flags an endpoint's service when no traces arrive in the defined window.
The API Endpoint Summary tab presents endpoints with their performance signals and method filters, and each endpoint connects to its distributed traces so the path from the API surface to the code behind it reads as one investigation. Pinned trace fields, Custom Reports, and the module's heatmap and Business Service views give the same endpoint data several complementary ways to be reviewed.
API Endpoint Monitoring meets your API layer wherever it runs. For Java, .NET, PHP, Node.js, Python, and Go, endpoint instrumentation spans Host/VM, Docker, and Kubernetes, and that Kubernetes coverage extends to managed distributions on AWS EKS, Oracle OKE, Google GKE, and Rancher, plus serverless AWS EKS Fargate, where node-level DaemonSet deployment is not possible.
For C++ and Ruby, endpoint monitoring covers Host/VM and Docker deployments.
Because collection is standardized on a single unified MotaAgent and OpenTelemetry, the same per-endpoint metrics and HTTP method filters apply whether an API runs on a virtual machine, in a container, or in a cloud-native cluster, giving web-facing and internal APIs one consistent view rather than a separate tool per environment.
Per-endpoint analysis replaces averaged numbers with route-level detail, so the endpoint dragging down an application is named rather than guessed at.
Single-click filtering for GET, POST, PUT, PATCH, DELETE, HEAD, and OPTIONS isolates the traffic that matters in one action instead of manual sifting.
P99 latency, error rate, throughput, and Apdex give a consistent read on each endpoint, so teams triage against the same measures every time.
Because endpoint data links to OpenTelemetry-based distributed traces, and frequently used fields can be pinned in view, an anomaly at the API surface leads straight to the code path that produced it.
API Endpoint Monitoring keeps the focus on application-code behavior and cross-links to Hybrid Infrastructure when the root cause sits below the application, so the two layers stay distinct during triage.
Java Auto-Discovery and code-free eBPF/OBI instrumentation bring endpoints under observation quickly, so coverage does not wait on a manual instrumentation effort for every service.
API Endpoint Monitoring stays inside the application layer by design. It reports on how an endpoint behaves in code: its latency, its error rate, the method traffic it carries, and the trace behind each request. When the question moves to the host, container, or Kubernetes node an endpoint runs on, that is CPU, memory, and node health, and it belongs to the Hybrid Infrastructure module. When the question moves to what a real visitor experienced calling that endpoint from a browser, that belongs to Real User Monitoring.
ObserveOps links these views at the seams: an endpoint's trace can connect to the infrastructure it ran on and to the browser session that triggered it, without API Endpoint Monitoring describing either layer as its own. Keeping each module inside its confirmed scope is what keeps the combined picture accurate.
API Endpoint Monitoring in Motadata ObserveOps turns raw API traffic into a route-by-route view of application performance. By analyzing each endpoint on its own, letting teams filter by HTTP method on the API Endpoint Summary tab, and layering in code-free eBPF/OBI discovery, Java Auto-Discovery, Custom Reports, and silent-endpoint alerting, it points investigation straight at the endpoints and verbs that need attention, while keeping the scope on application-code behavior and cross-linking to adjacent modules when the cause lies elsewhere.
Discover how Motadata ObserveOps can help you monitor your infrastructure in real-time and respond to issues instantly.