AI observability is the practice of monitoring, analyzing, and visualizing the internal states, inputs, and outputs of AI models embedded in modern applications. It helps ensure correctness, reliability, and effectiveness, while also supporting compliance requirements. By observing AI systems, data scientists, engineers, and operators can uncover insights to optimize and refine performance of your whole stack.
AI architectures are often complex, dynamic, and probabilistic, operating in unpredictable environments. Observability and transparency are critical to detect biases, understand limitations, and identify potential issues—an emphasis highlighted by emerging regulations like the European Union Artificial Intelligence Act.
Dynatrace unifies metrics, logs, traces, problem analytics, and root cause information in dashboards and notebooks, providing a single operational view of your AI-powered cloud applications end-to-end.
Use Dynatrace with Traceloop OpenLLMetry to gain detailed insights into your generative AI stack.
This approach covers the complete AI stack, from foundational models and vector databases to RAG orchestration frameworks, ensuring visibility across every layer of modern AI applications.
Observing AI models is inherently domain-driven: model owners must expose critical logs, metrics, and data to enable effective monitoring.
By embracing AI observability, organizations improve reliability, trustworthiness, and overall performance, leading to more robust and responsible AI deployments.
Dynatrace integrates with providers such as OpenAI, Amazon Bedrock, NVIDIA NIM, Ollama to monitor performance (token consumption, latency, availability, and errors) at scale.
Vector databases and semantic caches are central to RAG architectures. Dynatrace monitors solutions like Milvus, Weaviate, and Qdrant to help identify performance bottlenecks and usage anomalies.
Frameworks like LangChain manage data ingestion and prompt engineering for RAG applications. Dynatrace ensures you can track performance, versions, and degradation points in these pipelines.
Monitor infrastructure usage (GPU/TPU metrics, temperature, memory, etc.) for cloud services such as Amazon Elastic Inference and Google TPU, or custom hardware like NVIDIA GPU. This helps optimize resources and supports sustainability initiatives.
An overview of all of our integrations can be found on our Dynatrace Hub page
Dynatrace, a software intelligence company, has implemented its own AI observability solution to monitor, analyze, and visualize the internal states, inputs, and outputs of its own AI models.
The example below shows one of many self-monitoring dashboards that Dynatrace data scientists use to observe the operation of Davis® AI across all monitoring environments.