Observability problem #1: Fragmentation and complexity
Historically, organizations have deployed a number of observability instruments throughout their know-how 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 (web site reliability engineering) groups to depend on handbook 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 information streams past the normal MELT (metrics, occasions, logs, and traces) framework, resembling digital expertise monitoring (DEM) and steady profiling, to attain complete observability. DEM and its subset, actual person monitoring (RUM), supply useful insights into person interactions, whereas steady profiling pinpoints low-performing code. With out integrating these information streams, groups battle to hyperlink clients’ actual experiences with particular code-level points, leading to information 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 knowledge. SaaS-based observability options, which handle information ingestion, storage, and evaluation for his or her clients, have turn into significantly costly, with prices shortly accumulating. In response to a latest IDC report, practically 40% of enormous enterprises view excessive possession prices as a serious concern with observability instruments, with the median annual spend by massive organizations (10,000+ staff) on AIops and observability instruments reaching $1.4 million.