Overview
🔥Latest Use Cases🔥​
Don't Panic When Services Slow Down: Find Root Cause in Minutes with Syncause​
Multiple tools, scattered data, lack of intelligent capabilities
In modern enterprises, observability environments often exhibit the following characteristics:
- Tool diversity: Teams may simultaneously use different tools like Datadog, Prometheus, Loki, Tempo, Jaeger, etc.
- Data fragmentation: Metrics, logs, and traces are scattered across multiple systems, lacking a unified intelligent analysis layer.
- Vendor lock-in risk: Commercial tools provide some intelligent features, but are usually tightly coupled with platforms. Once migrated or used in hybrid scenarios, the same capabilities cannot be continued.
The result is: Teams pay significant maintenance costs, but still need engineers to manually "piece together clues" at critical moments.
Quickly Identify Causes of Business Error Rate Spikes​
Error alerts are noisy, difficult to quickly locate root cause
UCindex: UC02
In distributed systems, error rate spikes are a common challenge.
- Frequent alerts: When a service's error rate suddenly increases, it often triggers numerous alerts with high noise levels.
- Cumbersome process, low efficiency: Engineers often have to search through massive logs for exception stack traces, or repeatedly compare call traces to gradually narrow down the scope, which is time-consuming and inefficient.
Analyze CPU/Memory Usage Anomaly Causes to Prevent Service and Infrastructure Downtime​
Resource anomalies may lead to service avalanche
UCindex: UC03
In complex distributed systems, CPU or memory usage anomalies are the most common root causes of failures:
- CPU consistently maxed out → Requests cannot be scheduled in time, latency continues to rise
- Memory leaks or spikes → OOM Kill causes services to exit directly
- Difficult troubleshooting: Traditional monitoring can only show "high resource usage" but cannot quickly answer:
- Which service's which API consumed a lot of CPU?
- Is it a memory leak or a transient burst?
- Is the root cause application logic, increased data volume, or downstream dependency anomalies?
Once troubleshooting is slow, it may trigger service avalanche or even infrastructure downtime.
Build a No Vendor Lock-in Observability Intelligence Layer​
Multiple tools, scattered data, lack of intelligent capabilities
In modern enterprises, observability environments often exhibit the following characteristics:
- Tool diversity: Teams may simultaneously use different tools like Datadog, Prometheus, Loki, Tempo, Jaeger, etc.
- Data fragmentation: Metrics, logs, and traces are scattered across multiple systems, lacking a unified intelligent analysis layer.
- Vendor lock-in risk: Commercial tools provide some intelligent features, but are usually tightly coupled with platforms. Once migrated or used in hybrid scenarios, the same capabilities cannot be continued.
The result is: Teams pay significant maintenance costs, but still need engineers to manually "piece together clues" at critical moments.