AI-native error monitoring
Sentinel identifies the root cause of service failures across your entire stack, mapping dependencies in real-time to pinpoint the exact line of code responsible.
Reliability at scale for
Infrastructure-aware monitoring
Sentinel maps error propagation across microservices, isolating the single point of failure in seconds.
Our LLM-native engine correlates trace IDs and stack traces to identify the specific commit that triggered a regression.
Maintains span metadata across service boundaries, ensuring high-cardinality data is never lost during high-traffic events.
Detect latency bottlenecks before they cause service-wide cascading failures. Built-in thresholding for P99 monitoring.
Sentinel reads directly from your existing telemetry sinks. No proprietary agents or heavy sidecars required.
Sensitive customer data is stripped locally before it leaves your VPC, ensuring compliance without sacrificing visibility.
Automatically benchmarks new releases against previous baselines to flag unexpected resource consumption patterns.
Implementation
No manual instrumentation or heavy agents. Sentinel hooks into your runtime to map dependencies automatically.
Add our lightweight binary to your container spec. It sits at the kernel level to capture traces without code changes.
Sentinel automatically discovers service boundaries, databases, and third-party APIs to build a live dependency graph.
When latency spikes or errors occur, the AI correlates logs and traces to point to the exact line of code responsible.
From the front lines
Real feedback from teams managing high-throughput distributed architectures.
Sentinel identified a race condition in our message bus that three other tools missed. The trace went straight to the offending commit.
We replaced our legacy logging stack. Now we get root-cause analysis instead of just a wall of stack traces. It actually understands our service mesh.
The AI noise suppression is the first one I haven't disabled. It grouped 40,000 downstream errors into a single actionable incident.
It’s the first monitoring tool that feels like it was built for distributed systems, not just monolithic apps with a few APIs.
Sentinel doesn't just tell us things are broken; it shows us exactly why the state drifted across three different microservices.
Zero-config instrumentation that actually works on Kubernetes. We had visibility into our gRPC calls within ten minutes.
Deploy the Sentinel agent. Surface hidden bottlenecks across your distributed services without manual logging.