Observability problem #1: Fragmentation and complexity
Historically, organizations have deployed a number of observability instruments throughout their expertise stacks to deal with distinct wants like monitoring logs, metrics, or traces. Whereas these specialised instruments excel individually, they hardly ever talk properly, leading to knowledge silos. This fragmentation prevents groups from gaining complete insights, forcing devops and SRE (web site reliability engineering) groups to depend on guide integrations to piece collectively a full image of system well being. The result is delayed insights and an prolonged imply time to decision (MTTR), slowing down efficient difficulty response.
Moreover, organizations now want to include knowledge streams past the normal MELT (metrics, occasions, logs, and traces) framework, similar to digital expertise monitoring (DEM) and steady profiling, to attain complete observability. DEM and its subset, actual person monitoring (RUM), provide priceless insights into person interactions, whereas steady profiling pinpoints low-performing code. With out integrating these knowledge streams, groups wrestle to hyperlink clients’ actual experiences with particular code-level points, leading to knowledge gaps, delayed difficulty detection, and dissatisfied clients.
Observability problem #2: Escalating prices
The price of observability has surged alongside the fragmentation of instruments and the rising quantity of information. SaaS-based observability options, which handle knowledge ingestion, storage, and evaluation for his or her clients, have grow to be significantly costly, with prices rapidly accumulating. In response to a current IDC report, practically 40% of huge enterprises view excessive possession prices as a significant concern with observability instruments, with the median annual spend by massive organizations (10,000+ workers) on AIops and observability instruments reaching $1.4 million.
