Datadog has introduced a new tool called LLM Observability, aimed at assisting AI application developers and ML engineers in effectively monitoring, improving, and securing large language model (LLM) applications. This tool is designed to streamline the deployment of generative AI applications into production environments and ensure their scalability.
The demand for generative AI features is on the rise across various industries, but the complexity of LLM chains, their non-deterministic nature, and the associated security risks often pose challenges during implementation and production. Datadog’s LLM Observability aims to address these challenges by providing visibility into each step of the LLM chain, enabling users to easily identify errors and unexpected responses like hallucinations.
This tool also allows users to monitor operational metrics such as latency and token usage to optimize performance and cost, as well as evaluate the quality of AI applications in terms of relevance and toxicity. Additionally, users can leverage out-of-the-box quality and safety evaluations to mitigate security and privacy risks.
Unlike traditional monitoring tools, Datadog’s LLM Observability offers features such as prompt and response clustering, seamless integration with Datadog Application Performance Monitoring (APM), and built-in evaluation and sensitive data scanning capabilities. These capabilities help enhance the performance, accuracy, and security of generative AI applications, while also ensuring data privacy and security.
Industry leaders have already seen the benefits of using Datadog’s LLM Observability. Bobby Johansen, Senior Director Software at WHOOP, highlighted how the tool has enabled their engineering teams to evaluate model performance changes and maintain coaching interactions for their members. Kyle Triplett, VP of Product at AppFolio, emphasized the importance of understanding and evaluating GenAI application performance in real-world scenarios.
Yrieix Garnier, VP of Product at Datadog, stressed the significance of deep visibility in managing performance, detecting biases, and resolving issues before they impact business operations or end-user experiences. The tool helps organizations in evaluating inference quality, identifying root causes of errors, improving costs and performance, and protecting against security threats.
Datadog’s LLM Observability is now available for organizations looking to enhance their monitoring and management of generative AI applications. Using this tool, businesses can gain valuable insights into their AI applications, optimize performance, and ensure the security and privacy of sensitive data.