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Monitoring workloads that include azure openai service can be as simple as enabling diagnostics for azure openai and using preconfigured dashboards These platforms help you trace requests, evaluate outputs, monitor performance, and debug issues before they impact users However, this strategy doesn't satisfy several common, more complex, organizational monitoring requirements for generative ai workloads, such as:

This centralized gateway solution addresses common enterprise challenges including provider fragmentation. Managing multiple llm providers without a gateway creates what developers often call integration hell. each provider comes with its own sdk, authentication mechanism, rate limiting rules, and response formats. Monitor performance, control costs, track guardrails, and keep genai workloads running reliably.

Standard application performance monitoring (apm) tools help, but they lack the nuanced visibility needed for llm monitoring

That’s where llm observability tools come in Use model router strategies to control costs and performance. Learn how to build complete llm observability, from tracing and guardrails to cost and governance with frameworks and examples from portkey Large language models are now critical to how organizations build products, automate workflows, and power intelligent assistants.

Langsmith provides unified observability, debugging, testing, and monitoring capabilities tailored specifically for llm applications

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