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Monitoring your OpenAI & Azure OpenAI services such as GPT, o1, DALL-E, ChatGPT.

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With all those latency, reliability, and cost measurements in place, your operations team can now define their own OpenAI dashboards and SLOs.Dynatrace Intelligence problem feed showing a slowdown caused by OpenAI generative service.Dynatrace Intelligence reported and analyzed a large scale service slowdown that was caused by the OpenAI generative service.
  • Product information

Overview

Businesses are adopting generative AI to drive innovation, leveraging client-side processing and semantic searches over domain-specific knowledge bases. Various tools use large language models (LLMs) like GPT-4 to create AI agents that perform complex queries and integrate with third-party services, where efficient cost tracking and management are key success factors for businesses.

Dynatrace provides a solution for tracking and revealing the full context of used technologies and service interaction topology, analyzing security vulnerabilities, and observing metrics, traces, logs, and business events in real-time – all automatically and applicable to both OpenAI and Azure OpenAI services. A dashboard can show consumption, latency, availability, response time, error count, and request quantity for these AI services.

Use cases

  • proactive cost and capacity control: track token consumption and cost trends for OpenAI calls; forecast and alert on anomalies before budgets are impacted.
  • troubleshoot model and pipeline issues faster: trace prompts through orchestration and tool calls to pinpoint where latency, errors, or timeouts originate.
  • operationalize governance signals: monitor safety/guardrail outcomes reported by providers alongside service SLOs to spot risky patterns over time.

Get started

For more information about monitoring your OpenAI requests via Dynatrace, please see OpenAI observability in the Dynatrace documentation.

Details

  • cost, usage, and performance tracking for OpenAI API calls, including tokens, request/response latency, error rates, and throughput.
  • prompt‑level observability and tracing to follow interactions end‑to‑end across orchestration layers, tools, and dependencies.
  • anomaly detection and alerting on OpenAI usage patterns (for example sudden latency spikes or cost surges) with Davis® AI insights.
  • governance and guardrail visibility by centralizing provider‑reported outcomes (for example toxicity flags, denied topics) alongside operational metrics.
  • orrelated context across the full stack (services, pipelines, infra, GPUs/K8s) to speed up root‑cause analysis when OpenAI usage affects app behavior.
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