AI and LLM Observability

Monitor, optimize, and secure Generative AI applications, LLMs, and agentic workflows — improving performance, explainability, and compliance.

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By combining our Agentic AI initiatives with Dynatrace's AI Observability capabilities, we've successfully optimized our development and operations workflows.

Kulvir Gahunia Director of the Site Reliability Office, TELUS

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Seamless integration across the full AI application stack

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Integrate and observe

Integrate and observe every AI stack layer — from user applications to LLMs and infrastructure — with native support for top AI platforms.

Business impact

Track how AI creates productivity gains, deflects support tickets, acts autonomously, and delivers return on investment.

Application performance

Trace end-user experience, availability, and reliability of AI-powered applications.

Orchestration layer

Track chain performance, guardrails, and prompt caching across orchestration frameworks.

Agent-to-agent communication

Observe agent protocols, command execution, tool usage, and multi-agent communications.

Model integrity

Assess token usage, cost, stability, latency, invocation errors, and resource utilization of model outputs.

Semantic caches and vector databases

Monitor RAG pipelines, data volume, distribution, and retrieval patterns.

Infrastructure monitoring

Track utilization, saturation, and errors across GPUs, TPUs, and compute resources.

To gain our customers' trust, we're going after ISO 42001 certification. Dynatrace’s AI observability helps us to meet a lot of those control requirements because of the insight it provides into LLMs and their behaviors.

Matt Eisengruber Head of Architecture, CDL

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End-to-end observability for Agentic AI, Generative AI, and LLMs

Reduce cost and improve performance of your Agentic, AI, and LLM stack

Monitor operational metrics for Gen AI applications like token cost, request duration, problems, and problems with unified and customizable dashboards that drive proactive action
Leverage intelligent detection to identify changes in user behavior, predict cost increases, and proactively make changes to manage costs
Reduce AI agent and LLM response times and improve reliability by analyzing traces for the slowest requests and errors
Compare different AI model performance with A/B testing insights to make informed decisions about which models to deploy in production

Build trust, monitor guardrails of LLM input and output

Safeguard the quality of AI applications by monitoring and analyzing guardrail metrics to mitigate potential biases, errors, and misuse of AI systems
Recognize model hallucinations, identify attempts at LLM misuse such as malicious prompt injection, prevent
Personally Identifiable Information (PII) leakage, and detect toxic language Analyze the effectiveness of LLM guardrails and make necessary adjustments to ensure optimal user experience and safety

Explain, log, and trace back your AI service outputs

Gain end-to-end visibility into the execution of each user request, with tracing, logs to cover the full application stack: frontend, backend, orchestrations, RAG, LLM and agentic layers
Log, trace, and map dependencies between your services, spanning across your architecture
Intelligent detection to automatically pinpoint the root cause of errors and failures in the LLM chain and proactively accelerate resolution before impacting customers

Reduce compliance risk and secure your GenAI applications

Comprehensively and cost effectively document all inputs and outputs, maintaining full data lineage from prompt to response to build a clear audit trail to ensure compliance with regulatory standards
Store up to 10 years all of your prompts
Build dashboards to visualize the behavior and performance of AI systems to make their operation more transparent and prove compliance
Support carbon-reduction initiatives by monitoring infrastructure data, including temperature, memory utilization, and process usage