Business Service Monitoring groups OpenTelemetry-instrumented microservices into named business capabilities such as Checkout or Payment Processing, so teams measure application performance by what the business delivers rather than by individual service names.
Modern applications are built from many small services, and no single microservice tells you whether a business capability is healthy. Business Service Monitoring in Motadata ObserveOps groups OpenTelemetry-instrumented microservices into named Business Services, such as Checkout or Payment Processing, so teams can reason about performance the way the business does.
Once a set of services carries a Business Service tag, that tag becomes a first-class filtering, grouping, and analytics dimension across APM dashboards. Instead of reading dozens of service rows, teams see one named capability and the application behavior underneath it, all traced through a single unified MotaAgent on OpenTelemetry standards.
Business Service Monitoring stays within the application layer. It reports on application code behavior, traces, and service performance for the services grouped under a tag. Underlying host CPU, memory, and Kubernetes node health belong to the Hybrid Infrastructure module, and browser-side experience belongs to Real User Monitoring, both of which cross-link from the same platform rather than being described here.
Because a Business Service is built from the same instrumented microservices APM already tracks, it inherits the module's full language coverage and every drill-down view underneath it, from API endpoints to database dependencies.
Group OTel-instrumented microservices into named Business Services, such as Checkout or Payment Processing, so related application services are analyzed together as one unit.
The Business Service tag becomes a first-class filtering, grouping, and analytics dimension across APM dashboards, so any view can pivot to a named capability rather than a raw service list.
Compare two Business Services side by side, with independent time-range selection on each side, to surface performance regressions and release differences at the Business Service level.
4. Application-level performance signals:
Each Business Service rolls up the application metrics that matter for its services, including P99 latency, error rate, throughput measured in requests per second, and Apdex.
Business Services are assembled from OTel-instrumented services, so distributed tracing built on OpenTelemetry standards feeds every grouped view without a separate telemetry pipeline.
The services that make up a Business Service are instrumented through a single unified MotaAgent, so grouping them under a business-facing name adds no second agent or parallel collection path.
Because a Business Service is a grouping of instrumented services, its underlying API Endpoint Summary and HTTP method filters, for GET, POST, PUT, PATCH, DELETE, HEAD, and OPTIONS, are still available for a closer look at any service inside it.
The database calls each grouped service makes still link into APM traces and service topology, so a Business Service's performance story extends down to the specific database operations behind a slow transaction.
Custom Reports in Chart, Grid, Top-N, Counter, and Aggregation formats can be built over the services that make up a Business Service, turning a recurring capability-level review into a saved report.
A No Trace Received alert, configured during service registration, flags a business-critical service when no traces arrive within a defined period, so a Business Service does not go dark without notice.
Microservices are instrumented through a single unified MotaAgent with OpenTelemetry and report application traces and spans to APM. Java, .NET, PHP, Node.js, Python, and Go services run on Host/VM, Docker, and Kubernetes.
For C++ and Ruby, the services that make up a Business Service are instrumented on Host/VM and Docker.
Each service is assigned a Business Service tag, such as Checkout or Payment Processing, which associates its telemetry, its API endpoints, and its database calls with a named capability.
The Business Service tag is applied as a filtering, grouping, and analytics dimension, so application signals such as P99 latency, error rate, throughput, and Apdex are aggregated per named capability. The Compare view then evaluates two Business Services with an independent time range chosen for each side, and a No Trace Received alert flags a grouped service that stops reporting traces.
APM dashboards render each Business Service as a first-class grouping, and the Compare view places two Business Services next to each other so regressions and release differences read directly. From there, a team can drill into a single service's API Endpoint Summary or its database correlation view, or roll the same counters into a Custom Report. Deeper infrastructure and browser context are available through cross-links to the Hybrid Infrastructure and Real User Monitoring modules.
Business Service Monitoring covers a capability wherever its underlying services run. For Java, .NET, PHP, Node.js, Python, and Go, that means Host/VM, Docker, and Kubernetes, including managed distributions on AWS EKS, Oracle OKE, Google GKE, and Rancher, plus serverless AWS EKS Fargate.
For C++ and Ruby, services grouped into a Business Service are instrumented on Host/VM and Docker.
Because every grouped service shares the same single unified MotaAgent and OpenTelemetry foundation, a Business Service reads the same way whether its components run on virtual machines, in containers, or across a cloud-native cluster, giving a business capability one consistent performance view regardless of where each piece of it is deployed.
Named Business Services let teams talk about Checkout or Payment Processing directly, instead of translating between service identifiers and business capabilities.
Filtering and grouping by Business Service narrows a broad dashboard to the services behind one capability, so the path from symptom to cause is shorter.
Comparing two Business Services side by side with independent time ranges makes post-deploy validation and cross-environment troubleshooting a direct read rather than a manual reconstruction.
Because every Business Service draws on the same approved application metrics, P99 latency, error rate, throughput, and Apdex, comparisons stay honest across capabilities and over time.
Business Services are built on the OTel-instrumented services already reporting to APM through a single unified MotaAgent, so no parallel setup is required to see capability-level performance.
Because a Business Service keeps its API endpoint and database drill-downs intact, a capability-level regression can be traced to the specific endpoint and query behind it without switching tools.
A Business Service is a grouping of application services, and its performance story stays inside the application layer: traces, endpoint behavior, and database correlation for the services tagged under it. When a Business Service's regression traces back to the host, container, or Kubernetes node beneath one of its services, that is infrastructure health and belongs to the Hybrid Infrastructure module. When a customer-facing Business Service like Checkout needs to be read from the browser's point of view, that belongs to Real User Monitoring.
ObserveOps links a Business Service to both views without folding either one into it, so a capability owner can move from a named business function to the infrastructure or browser context behind it while each module keeps reporting only on what it actually measures.
Business Service Monitoring turns a sprawl of OTel-instrumented microservices into a small set of named capabilities that match how the business thinks. By making the Business Service tag a first-class filtering, grouping, and analytics dimension, keeping API endpoint and database drill-downs intact underneath it, and letting teams compare two Business Services side by side with independent time ranges, Motadata ObserveOps keeps application performance readable as services multiply.
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