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Enhanced AI model observability with Dynatrace and Traceloop OpenLLMetry

Dynatrace

Resource consumption: Observing computational resource availability and saturation, whether deployed in cloud-native environments like Kubernetes or CPU-enabled servers. Data quality and drift: Monitoring the quality and characteristics of training and runtime data to detect significant changes that might impact model accuracy.

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Benchmark (YCSB) numbers for Redis, MongoDB, Couchbase2, Yugabyte and BangDB

High Scalability

We note that for MongoDB update latency is really very low (low is better) compared to other dbs, however the read latency is on the higher side. The latency table shows that 99th percentile latency for Yugabyte is quite high compared to others (lower is better). Again Yugabyte latency is quite high. Conclusion.

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Dynatrace automatically monitors OpenAI ChatGPT for companies that deliver reliable, cost-effective services powered by generative AI

Dynatrace

A typical design pattern is the use of a semantic search over a domain-specific knowledge base, like internal documentation, to provide the required context in the prompt. With these latency, reliability, and cost measurements in place, your operations team can now define their own OpenAI dashboards and SLOs.

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AI-driven analysis of Spring Micrometer metrics in context, with typology at scale

Dynatrace

Spring Boot 2 uses Micrometer as its default application metrics collector and automatically registers metrics for a wide variety of technologies, like JVM, CPU Usage, Spring MVC, and WebFlux request latencies, cache utilization, data source utilization, Rabbit MQ connection factories, and more. To learn more, see our documentation.

Metrics 200
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AI-driven analysis of Spring Micrometer metrics in context, with topology at scale

Dynatrace

Spring Boot 2 uses Micrometer as its default application metrics collector and automatically registers metrics for a wide variety of technologies, like JVM, CPU Usage, Spring MVC, and WebFlux request latencies, cache utilization, data source utilization, Rabbit MQ connection factories, and more. To learn more, see our documentation.

Metrics 130
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AI-driven analysis of Spring Micrometer metrics in context, with topology at scale

Dynatrace

Spring Boot 2 uses Micrometer as its default application metrics collector and automatically registers metrics for a wide variety of technologies, like JVM, CPU Usage, Spring MVC, and WebFlux request latencies, cache utilization, data source utilization, Rabbit MQ connection factories, and more. To learn more, see our documentation.

Metrics 130
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Automated observability, security, and reliability at scale

Dynatrace

Whether tracking internal, workload-centric indicators such as errors, duration, or saturation or focusing on the golden signals and other user-centric views such as availability, latency, traffic, or engagement, SLOs-as-code enables coherent and consistent monitoring throughout the environment at scale.