Motadata ObserveOps .NET application monitoring instruments .NET 8 and .NET 9 services through a single unified MotaAgent, capturing traces, transaction timing, and errors from inside the running code.
.NET application monitoring tools in Motadata ObserveOps show how .NET code actually behaves in production. Support covers .NET 8 and .NET 9 services, instrumented through a single unified MotaAgent, capturing distributed traces, transaction timing, and errors from inside the running service, so slow requests and failing calls surface as evidence rather than guesswork.
Application Performance Monitoring (APM) watches application code behavior, not the machines underneath it. Infrastructure signals such as host CPU, memory, and Kubernetes-node health belong to the Hybrid Infrastructure module, which cross-links with APM so a .NET service problem and its host context read together. Where a .NET backend serves a browser front end, what the visitor experienced in that browser is the scope of Real User Monitoring, not APM.
The instrumentation is built on OpenTelemetry standards, and the same collection model applies whether a .NET service is a public-facing API, a background worker, or one microservice inside a larger estate. Spans and metrics from each service are stitched into a single trace that follows a request across every service it touches, so a .NET endpoint and the internal services it calls read as one connected picture.
Because .NET services increasingly run across Host and VM, Docker, and every major Kubernetes distribution, monitoring stays consistent as a service moves from a virtual machine into a managed cluster such as AWS EKS or Rancher, with the same traces, endpoint breakdowns, and service metrics carrying across without a change in how the data reads.
Distributed tracing built on OpenTelemetry standards follows each request across .NET services, capturing spans, timing, and dependencies so a single transaction reads as one connected timeline.
.NET monitoring runs across Host and VM, Docker, and full Kubernetes, including Amazon EKS, EKS Fargate, Oracle OKE, Google GKE, and Rancher, so the same instrumentation model applies wherever a service runs.
Per-service and per-transaction views report P99 latency, error rate, throughput in requests per second, and Apdex, so regressions in a specific code path are visible against a clear baseline.
Application errors and exceptions raised inside .NET code are captured and grouped, so recurring failures surface as patterns tied to the exact service and transaction where they occur.
The API Endpoint Summary tab reports per-endpoint behavior and supports single-click filtering by HTTP method across GET, POST, PUT, PATCH, DELETE, HEAD, and OPTIONS, so method-specific traffic and errors are quick to isolate.
OTel-instrumented .NET microservices can be grouped into named Business Services such as "Checkout" or "Payment Processing," turning a set of individual services into a first-class filtering, grouping, and analytics dimension across APM dashboards.
Two .NET services, or two Business Services built from them, can be placed side by side with independent time-range selection per side, so a release regression in a service or domain reads at a glance.
A severity-based heatmap gives a real-time visual read across counters such as response time and error rate for .NET services, built on existing APM counters with no additional telemetry configuration required.
The APM Database tab filters .NET database activity by operation type, including SELECT, INSERT, UPDATE, and DELETE, across the 40+ database systems supported, so a slow query type is isolated without scanning every call.
eBPF/OBI-based instrumentation discovers and monitors .NET services automatically, with no code changes and no agent install, capturing service-to-service communication, request count, latency, error rate, and traces and spans directly in the existing APM service list, service map, and dashboards.
Because coverage spans Host, VM, Docker, and full Kubernetes, teams instrument .NET services once and keep the same trace quality from a single VM to a managed EKS Fargate cluster.
Grouped errors and per-transaction traces point to the exact service and call where a request slowed or failed, so investigation starts at the cause.
P99 latency, error rate, throughput, and Apdex give every .NET service the same yardstick. Heatmaps turn those same counters into a severity-based visual read, so a post-deploy regression is easy to spot and quantify.
Directly discovered databases link into APM traces and service topology across the 40+ database systems supported, and the Database Operation Type filter narrows a slow SELECT or UPDATE to the exact call behind it.
Cross-linking to Hybrid Infrastructure Monitoring means a code-level trace and its underlying host context sit side by side without conflating application behavior with infrastructure health.
Business Service tagging and the Compare view let a .NET estate be analyzed by the domain a service belongs to, so a release comparison covers a Business Service such as "Payment Processing," not only one microservice at a time.
A single unified MotaAgent instruments each .NET 8 or .NET 9 service using OpenTelemetry, collecting spans, transaction timing, errors, and dependency calls. Collection runs identically across Host and VM, in Docker, and across Kubernetes, including EKS, EKS Fargate, OKE, GKE, and Rancher. Where instrumenting .NET source directly is not yet an option, code-free eBPF/OBI collection discovers services and captures request count, latency, and error rate without an agent install.
Collected traces are analyzed for P99 latency, error rate, throughput, and Apdex per service and per transaction, with errors and exceptions grouped and consistent severity color indicators applied so regressions and failing paths stand out. The Database tab breaks activity down by operation type across SELECT, INSERT, UPDATE, and DELETE, and Business Service tags roll service-level analysis up to the domain level when configured.
APM Explorer, the API Endpoint Summary tab, service topology, and Compare view present the data, with Compare view setting two services or two Business Services side by side under independent time ranges. Heatmaps give a severity-based read across counters such as response time and error rate with no added telemetry setup, and directly discovered databases link into traces so a .NET request reads from entry point to its database dependency in one view.
.NET monitoring meets services wherever they run. Instrumentation spans Host and VM and Docker, and extends across Kubernetes, including managed distributions on AWS EKS, Oracle OKE, and Google GKE, plus Rancher-managed clusters and serverless AWS EKS Fargate, where node-level DaemonSet deployment is not possible.
Because collection is standardized on a single unified MotaAgent and OpenTelemetry, the same trace model, service topology, and endpoint analysis apply whether a .NET service runs on a virtual machine, in a container, or in a cloud-native cluster. That gives cloud, hybrid, and containerized .NET workloads one consistent view instead of a separate tool per environment, and it holds whether the cluster is a self-managed Kubernetes install or a managed offering like EKS, OKE, GKE, or Rancher.
.NET application monitoring stays inside the application-code layer so a full-stack view stays accurate rather than blurred. APM owns the traces, service topology, endpoint behavior, and database links produced by a .NET service's own code. When the question is about the host or cluster underneath, such as node CPU, memory, or pod health, that belongs to the Hybrid Infrastructure module. When a .NET service sits behind a web front end, what the visitor experienced belongs to Real User Monitoring.
ObserveOps links these views so a slow .NET service can be read against the infrastructure it runs on, and a browser session can connect back to the .NET backend trace behind it, without APM claiming ground outside its own.
.NET Application Monitoring in Motadata ObserveOps turns .NET 8 and .NET 9 runtime behavior into a clear, trace-level picture of how code performs, from a single distributed trace up to the Business Service it rolls into. By grounding every measurement in OpenTelemetry and keeping the same model across Host, VM, Docker, and full Kubernetes, including EKS, EKS Fargate, OKE, GKE, and Rancher, it gives teams a dependable way to find and fix application problems wherever their services run. Compare view, heatmaps, and code-free eBPF/OBI discovery add the analysis and onboarding depth teams need without changing that foundation.
Discover how Motadata AIOps can help you monitor your infrastructure in real-time and respond to issues instantly.