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 nicely, leading to information silos. This fragmentation prevents groups from gaining complete insights, forcing devops and SRE (website reliability engineering) groups to depend on handbook integrations to piece collectively a full image of system well being. The end result is delayed insights and an prolonged imply time to decision (MTTR), slowing down efficient concern response.
Moreover, organizations now want to include information streams past the normal MELT (metrics, occasions, logs, and traces) framework, comparable to digital expertise monitoring (DEM) and steady profiling, to attain complete observability. DEM and its subset, actual person monitoring (RUM), supply beneficial insights into person interactions, whereas steady profiling pinpoints low-performing code. With out integrating these information streams, groups wrestle to hyperlink prospects’ actual experiences with particular code-level points, leading to information gaps, delayed concern detection, and dissatisfied prospects.
Observability problem #2: Escalating prices
The price of observability has surged alongside the fragmentation of instruments and the rising quantity of knowledge. SaaS-based observability options, which handle information ingestion, storage, and evaluation for his or her prospects, have develop into significantly costly, with prices rapidly accumulating. In accordance with a current IDC report, practically 40% of enormous enterprises view excessive possession prices as a serious concern with observability instruments, with the median annual spend by giant organizations (10,000+ workers) on AIops and observability instruments reaching $1.4 million.
