Spot the slowdown. Find the root cause. Fix issues faster. All from a single platform built for the complexity of modern application stacks.
ManageEngine delivers end-to-end visibility across your entire application stack and provides AI-powered insights to prevent bottlenecks before they impact users, help developers debug faster, and continuously optimize application performance so your system gets better over time — not just recovered.
End-to-end traces across every service, queue, and database in your architecture. ManageEngine uses tail-based sampling to capture the traces that matter most, the slow ones, the error-prone ones, and the outliers, making sure nothing important is randomly discarded.
Knowing the trace is only half the answer. ManageEngine's profiler goes deeper into the execution stack, the function calls, and the memory allocations so you can walk directly from a slow span to the exact line of code responsible, without a separate profiling tool or a reproduction environment.
Service topology diagrams become outdated the moment developers push new code. ManageEngine builds your dependency map automatically from live trace data, updated in real time as your architecture evolves. No manual configuration, no service catalogs to maintain.
ManageEngine captures every unhandled exception, caught error, and HTTP 4xx/5xx response, groups them by root cause, and surfaces what matters before your support queue does. Each error group links to a representative trace showing the full request context, the span that threw, and the logs from the same window, so you go from alert to deployable fix without switching tools.
ManageEngine is built on OpenTelemetry from the ground up. Instrument once using the OTel SDK. Your telemetry data is yours, your instrumentation efforts are portable, and your architectural choices are not locked in to a single observability vendor.
ManageEngine supports every major language, framework, and infrastructure layer — out of the box, with no custom parsers or proprietary agents required.
Every decision in ManageEngine — from the data model to the pricing structure — was made with one question in mind: what does an engineering team actually need from an observability platform at scale?
Most platforms show you that a service is slow. ManageEngine shows you the exact function, the exact query, and the exact line of code responsible.
ManageEngine is OpenTelemetry-native from the ground up. Instrument once with the OTel SDK, send data to ManageEngine, and retain the freedom to route that same data anywhere else.
Predictable costs that scale with your infrastructure. ManageEngine uses a straightforward host-based pricing model with no usage-based surprises at month end.
Production visibility from day one. Enterprise observability platforms have historically demanded weeks of professional services before a single trace appears.
Most teams are seeing live traces within 30 minutes of starting setup. ManageEngine auto-instruments your application stack on install — no manual span creation, no framework-specific configuration files to write. If you are running Kubernetes, the operator handles agent injection across your pods automatically. The typical enterprise deployment that would take days with other platforms is measured in minutes here.
No, ManageEngine accepts telemetry over OTLP natively, which means if your team has already instrumented with the OTel SDK, you point your exporter at ManageEngine's endpoint and your traces, metrics, and logs start flowing immediately. Nothing gets re-instrumented. If you are evaluating ManageEngine alongside your current vendor, the OTel Collector lets you route to both simultaneously during the transition.
ManageEngine uses host-based pricing. You pay for the number of hosts you monitor, not for the volume of spans ingested, traces stored, or metrics collected. This means a traffic spike on Black Friday or a load test before a major release does not show up as a surprise line item at the end of the month. Your observability costs scale with your infrastructure footprint, not with how hard your application is working.
Yes, this is the environment ManageEngine is designed for. Distributed traces are reconstructed across service boundaries regardless of the language each service is written in. A Java API calling a Python worker calling a Go service all appear in the same trace waterfall, with span-level timing and context propagated automatically using W3C Trace Context. Service dependency maps are built from live trace data, so the topology view reflects your actual running architecture across all services simultaneously.
Yes, ManageEngine monitors applications running on all three major cloud providers, including containerized workloads on EKS, AKS, and GKE. AWS Lambda and Google Cloud Run are supported for serverless observability. For Lambda specifically, cold start duration, invocation count, error rate, and duration percentiles are captured alongside application-level traces, so you have full visibility from the function invocation through to any downstream services it calls.
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