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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.Davis problem feed showing a slowdown caused by OpenAI generative service.Davis reported and analyzed a large scale service slowdown that was caused by the OpenAI generative service.
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Overview

Businesses are adopting generative AI for innovation, using 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 various third-party services where one of the success factors for businesses becomes efficient cost tracking and management.

Dynatrace provides a solution for tracking and revealing the full context of used technologies, service interaction topology, analyzing security vulnerabilities, and observing metrics, traces, logs and business events in real-time – all done 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.

Details

Monitor service health and performance

  • Detect bottlenecks by tracking real-time metrics, including request counts, durations, and error rates.
  • Manage service costs with automated cost calculations for each request.
  • Stay on track with SLOs

Monitor service quality and cost

  • Setup error budgets for performance and cost controls
  • Validate consumption and response time by models
  • Prevent quality degradation by validating models and usage patterns in real time.

End-to-end tracing and debugging

  • Achieve complete visibility of prompt flows, from initial request to final response, for faster root cause analysis.
  • Capture detailed debug data to troubleshoot issues in complex pipelines.
  • Streamline your workflows with granular tracing of LLM prompts, including response latency and model-level metrics.
  • Resolve issues quicker by pinpointing exact problem areas in prompts, tokens, or system integrations.
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