gPROMS vs. Dash0

Siemens' gPROMS technology revolutionizes digital process design and operations, enabling organizations in the process industry to effectively tackle complex design and operational decisions daily, which ultimately leads to sustainable value creation. By leveraging in-depth process expertise through advanced predictive models, gPROMS allows for a quick and thorough exploration of the decision-making landscape concerning processes and products. This technology captures vital insights about processes, facilitating the use of advanced mathematical techniques for detailed analysis and optimization, thereby enhancing design and operational efficiency. Not only does this approach foster innovation, but it also aids in crafting economically viable process designs while reducing technology-related risks. The gPROMS Process modeling environment integrates the entire journey from concept to operations in process design, employing state-of-the-art analysis and optimization features on high-fidelity models. As a result, it accelerates the design timeline, accurately evaluates and mitigates risks, and pinpoints the most effective process configurations that yield value throughout the plant's lifecycle. Moreover, this comprehensive strategy helps organizations maintain their competitive edge in a swiftly transforming market landscape, ensuring their long-term success. Ultimately, such capabilities position companies not only to adapt but also to thrive amidst continuous change.

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.