Elastic vs. Dash0

We contacted our clients to gain insights into the tools and methods they are currently using to handle their virtual preseason sell-in and B2B merchandising processes. This crucial resource is designed to assist sales teams and representatives in adapting to virtual meetings as in-person trade shows remain on hold. Elastic’s capabilities for digital and tailored catalogs remove the need for conventional brand books, leading to considerable cost savings, enhanced sales effectiveness, and beneficial environmental outcomes. The solutions provided are flexible and can evolve along with your organization’s IT strategy and changing sales needs over time. With billions of dollars transacted globally, Elastic’s B2B platform is recognized as the go-to sales solution for brands across various sizes and objectives. By transitioning to expertly curated digital catalogs, businesses can finally eliminate the complications associated with printed materials, reducing waste while boosting sales efficiency and realizing significant cost benefits. This strategy not only optimizes the sales workflow but also aligns seamlessly with contemporary digital trends in the B2B landscape, ensuring that companies remain competitive and relevant in a rapidly changing marketplace.

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.