I&B monitoring vs. Dash0

I&B monitoring is a Business & IT infrastructure monitoring platform, available in the cloud or deployed on-premise inside your own perimeter. It answers a question most monitoring tools leave open: when something breaks, what does it cost the business? Business performance, IT services and infrastructure sit in one observability model, so every role reads the same data. Data collection covers metrics, logs and traces from servers, databases, containers, virtual machines, network gear and applications. Prometheus-style exporters and templates handle common technologies out of the box: you decide what to monitor and which metrics matter. Rollout is automated: download the generated Ansible files and deploy monitoring across the whole estate with one command, instead of configuring exporters host by host. The model is hierarchical: business units and subunits, IT services, service layers, individual resources. Graph-based visualisation exposes the relationships between them, so teams can trace a degraded KPI to the service, layer and resource behind it, or see which business units a failing component affects right now. Dashboards match the role. Executives and business unit managers track KPIs and overall status; operations teams watch real-time service and resource health; DevOps and SRE engineers work with incident context and correlated metrics; security teams gain operational context around anomalies. Ready-made dashboards for business, service and layer monitoring remove the setup phase. Alerting is contextual rather than mechanical. Related threshold breaches are grouped into single actionable incidents, and the platform separates component faults, layer problems, outages and business-impacting events, keeping noise down. Jira, Zendesk, Zoho Desk and email integrations open enriched tickets automatically. Flexible XaaS licensing. Used in telecom, finance, healthcare and technology, with on-premise for regulated environments.

Engineering teams that adopt OpenTelemetry often hit the same wall: instrumentation is standardized, but the backend receiving it is not. Dash0 was built to close that gap. Every signal, whether a trace, a log record, a metric, or the resource emitting it, is stored against OpenTelemetry semantic conventions and correlated automatically. A request that ran long can be examined next to the log lines it produced and the pod it ran on, with no manual joins and no hopping between products. Ingestion happens through a standard OTLP endpoint. Nothing proprietary gets deployed, and existing instrumentation keeps working untouched. Because the wire format is open, data can be redirected to a different destination later without changes to application code. Prometheus users are treated as first-class. Full PromQL is supported, existing recording and alerting rules carry over, and Grafana dashboard definitions import directly. Cluster-level collection is handled by a dedicated Kubernetes operator covering workloads, nodes, and control plane components. Visualization runs on Perses, with dashboard, check, and alert definitions expressed declaratively and kept under version control. Investigations start broad and get narrow: heatmaps expose the shape of a latency distribution, then filters on high-cardinality attributes isolate the affected requests. Machine learning is applied to telemetry during processing rather than surfaced as a chatbot. Log AI assigns severity to records that arrive without it, discovers recurring patterns, and clusters similar entries, turning noisy third-party output into something queryable. For failing requests, the SIFT methodology structures the path from symptom to root cause. Consumption stays transparent throughout. Teams can identify which services, attributes, and log volumes are responsible for their bill and reduce them at the source, before the invoice arrives.