Key Highlights
Motadata APM traces each request end to end, from entry point to execution, pinpointing exactly where performance breaks down with span-level evidence, not assumptions.
Trace transactions through services and dependencies, with span timelines that show where latency accumulates and failures originate.
Trace all requests from entry point to execution spanning APIs, databases, services, and infrastructure in one end-to-end record.
Span timelines provide the timing evidence to pinpoint latency and failures, not assumptions based on averages.
Each service call, database interaction, and downstream dependency captured as a span within the parent trace.
Navigation from a slow response alert to the exact service and operation responsible, eliminating manual investigation between tools.
Detect instability patterns in specific transaction types and identify dominant operations fast.
Transaction Trends track Trace Count, Trace Duration, and Traces with Errors over time.
Instability detection before it becomes an incident.
Root Span Summary Grid views root spans with their Traces, Spans, Trace Duration, and Error Count.
Highlight dominant operations at a glance.
Trace Journey Drilldown navigates from any root span into the full trace journey for deeper operational context.
Focus on the transaction entry points generating the most operational noise before drilling into individual spans.
Move directly from traces to errors, hosts, metrics, logs, databases, and JVM insights in one workflow.
Navigate from trace to errors, host metrics, logs, databases, and JVM insights, all inside the APM workspace.
Determine whether an issue originates in application code, JVM, database, or infrastructure using aligned visual evidence.
Full-stack correlation through dashboards and widgets provides the context to identify root cause. No platform switching required.
Accelerate root cause analysis by keeping all relevant signals within the same investigation workflow.
Extend APM beyond automatic instrumentation with language-specific SDKs and custom business context.
Language-specific SDK integration for custom spans covering business logic not covered by automatic framework instrumentation.
Custom attributes and parameters for injecting business-critical context into traces.
Tenant ID, transaction type, and feature flag enrichment per record.
Custom metadata enrichment delivers deeper, environment-specific insights beyond what framework auto-instrumentation provides.
Span enrichment at the point of instrumentation ensures business context flows through all trace records.
Instrument applications for all supported languages and deployment architectures, no code changes required.
Java, .NET, Python, Go, Node.js, and PHP instrumented for Host/VM, Docker, and Kubernetes-family targets including OpenShift, EKS, AWS EKS Fargate, OKE, GKE, and Rancher.
Ruby and C++ instrumented on Host/VM and Docker deployments only (not supported in Kubernetes-family deployments).
Auto-instrumentation via MotaAgent, no code changes required for supported frameworks. Agents instrument at runtime.
For the Kubernetes-supported languages, single DaemonSet agent per node auto-detects languages and instruments cluster-wide, no per-pod sidecar, no per-service configuration.
Intelligence
Performance problems rarely show where they originate. A slow API response may trace to a database query three hops away, a timeout to a microservice holding a connection slot. Symptom and cause are rarely in the same service.
Motadata APM captures each service call, database interaction, and downstream dependency in one consolidated trace record spanning entry point to final dependency, with origin identified from span timing. The Single Investigation Workspace connects the trace to JVM insights, host metrics, and logs in one workflow when the trace alone does not explain the behavior.
How It Works
Instrument code via MotaAgent for Java, .NET, PHP, Node.js, Python, Go, Ruby, and C++ with no code changes.
Capture requests as traces of spans, recording timing for service calls, DB queries, and dependencies.
Reconstruct end-to-end call paths from span records and visualize them through timelines and analytics.
Apply Transaction & Root-Span Prioritization to flag operations causing the most instability first.
Correlate traces with JVM insights, host metrics, and log events in the Single Investigation Workspace.
Use Custom Instrumentation SDKs to inject business metadata into spans for environment-specific analysis.
Use Custom Instrumentation SDKs to inject business metadata into spans for environment-specific analysis.
Role-Based Value
Show stakeholders that performance regressions are identified at the trace and span level, not surfaced through user complaints or SLA breach reports.
Show stakeholders that performance regressions are identified at the trace and span level, not surfaced through user complaints or SLA breach reports.
Reduce time spent on performance investigations by providing trace-level evidence of root cause at the start of each incident.
Reduce time spent on performance investigations by providing trace-level evidence of root cause at the start of each incident.
Reduce time spent on performance investigations by providing trace-level evidence of root cause at the start of each incident.
Reduce time spent on performance investigations by providing trace-level evidence of root cause at the start of each incident.
Reduce time spent on performance investigations by providing trace-level evidence of root cause at the start of each incident.
Reduce time spent on performance investigations by providing trace-level evidence of root cause at the start of each incident.
From Visibility to Control
End-to-End Trace Proof: span timelines pinpoint latency and failures throughout all services and dependencies.
Transaction Trends and Root Span Summary Grid reveal instability before service-level indicators reflect it.
Trace Journey Drilldown advancing from root span to full trace journey for complete operational context.
Single Investigation Workspace: traces, errors, hosts, metrics, logs, databases, and JVM in one workflow.
Custom Instrumentation SDKs inject business-critical context into each trace for root cause analysis.
Java, .NET, Python, Go, Node.js, and PHP spanning Host/VM, Docker, Kubernetes, OpenShift, EKS, OKE, GKE, Rancher.
Ruby and C++ on Host/VM and Docker deployments only (not supported in Kubernetes-family deployments).
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