> For the complete documentation index, see [llms.txt](https://stephen-tsoi.gitbook.io/stephen-tsoi-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://stephen-tsoi.gitbook.io/stephen-tsoi-docs/opentelemetry/from-missing-events-to-complete-visibility-tracing-enterprise-transactions-with-opentelemetry/day-26-34-shortcomings-of-todays-apm-solutions.md).

# Day 26/34 Shortcomings of Today’s APM Solutions

Application performance monitoring (APM) tools rely on collecting metrics from logs and the operating system (OS) to monitor individual applications and systems. They offer visualized dashboards for operators and developers to track performance. However, these tools primarily focus on individual components and lack the ability to map the entire data flow within a system.

To achieve full system observability, **Code Injection** or **instrumentation** (either manual instrument or auto instrument) is necessary. This involves adding code to system components that emit traces, metrics, and logs. This allows for more comprehensive monitoring and analysis of data flows, but traditional APM tools struggle to connect these traces to the corresponding log files.

Both application **log analysis** and **code-injected** APM tools require a dedicated agent on the target server. Using multiple types of agents, especially those with code injection can lead to program instability and crashes. This renders them unsuitable for dynamic environments like Kubernetes (K8S) and vendor solutions like API Management (APIM) platforms. Furthermore, their requirement client-side SDK library can cause crashes on memory-constrained mobile apps.

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