Full-stack observability for AI applications, LLMs, and agents
Monitor performance, cost, quality, and compliance across your entire AI stack from the user experience layer through orchestration to the model and infrastructure below.
See everything across your AI stack
AI applications introduce failure modes, cost unpredictability, and compliance risk that traditional observability tools aren't built to handle. Dynatrace gives you end-to-end visibility from user experience through orchestration to LLM and infrastructure, all in one platform.
Performance and cost
Monitor token usage, latency, and model reliability across every AI service so teams can optimize spend and catch degradation early.
Quality and safety
Detect hallucinations, prompt injection, PII leakage, and toxicity with built-in guardrail monitoring and continuous LLM evaluation scoring.
Compliance and traceability
Maintain full data lineage from prompt to response with long-term storage and audit-ready dashboards that support regulatory standards.
Integrate and observe
Integrate and observe every AI stack layer — from user applications to LLMs and infrastructure — with native support for top AI platforms.
Track how AI creates productivity gains, deflects support tickets, acts autonomously, and delivers return on investment.
Trace end-user experience, availability, and reliability of AI-powered applications.
Track chain performance, guardrails, and prompt caching across orchestration frameworks.
Observe agent protocols, command execution, tool usage, and multi-agent communications.
Assess token usage, cost, stability, latency, invocation errors, and resource utilization of model outputs.
Monitor RAG pipelines, data volume, distribution, and retrieval patterns.
Track utilization, saturation, and errors across GPUs, TPUs, and compute resources.
End-to-end observability for AI, LLMs, and agents
Reduce cost and improve AI performance at scale
Token costs and latency compound quickly across AI applications. Dynatrace monitors your full Gen AI stack, predicts cost spikes before they land, and flags slow requests and errors early. It also captures provider guardrail signals, showing why an interaction was blocked, modified, or allowed.
Find, fix, and evaluate agent failures with Arize
Arize AX helps teams see where their AI agents go wrong and fix it before users notice. It records every agent run as a trace and groups repeated failures into ranked issues, each with a suggested fix. Teams can run evaluations that score quality against their own criteria, using LLM-as-a-judge or code checks.
Trace every request from user to model
Debugging AI failures requires visibility across the full stack. Dynatrace traces each request end to end across frontend, backend, orchestration, RAG, and LLM layers, and automatically pinpoints root cause so teams resolve issues before customers feel them.
