Business challenge
As organizations scale GenAI, LLM applications and agentic systems into production environments, they face a new class of operational challenges that traditional observability tools cannot address. AI behavior is probabilistic, costs fluctuate unpredictably, and multi-step agent workflows introduce failures that are difficult to diagnose and resolve. Without deep visibility into prompts, models, tokens, reasoning paths, and agent tool use, teams struggle to ensure performance, reliability, and cost control.
GenAI systems are probabilistic, causing inconsistent outputs, hallucinations, and hard-to-explain decisions that teams cannot diagnose without deep visibility into prompts, steps, and reasoning.
AI inferencing introduces latency, GPU saturation, and dependency failures. Traditional observability lacks insight into model steps, agent flows, and tool-use delays.
Token usage, model calls, and external API dependencies create unpredictable cost spikes that teams cannot easily trace back to specific prompts, agents, or services.
Agent chains can loop, misfire, or contradict each other. Failures in one step cascade through tools, APIs, and workflows without clear root-cause visibility.
The Instana LLM observability solution
IBM® Instana® GenAI observability brings AI monitoring into the same powerful workflows teams already use for their full-stack applications. It automatically discovers and maps agents, chains and tasks, then connects their data to the rest of your infrastructure. With built-in visibility into prompts, outputs, tokens, latency, cost and errors, teams can quickly trace issues, optimize performance and operate AI-driven apps with confidence.
Backed by OpenLLMetry standards, Instana supports leading providers and frameworks, including IBM watsonx.ai®, Amazon Bedrock, OpenAI, Groq, DeepSeek, LangChain, LangGraph and CrewAI, as well as runtimes like vLLM and GPUs. This open ecosystem approach keeps your AI observability portable, cost-aware and governance-ready, giving you end-to-end clarity from model to infrastructure without switching tools.
Features
Instana automatically discovers AI components, including agents, chains and tasks, and maps their relationships to services and infrastructure. This capability includes task hierarchy modeling and monitoring to visualize flows and pinpoint where issues originate.
Trace every step across agents, tool calls, retrievals and model invocations while correlating with traditional APM signals. Real-time incident investigation brings AI spans into the same timeline as microservices, databases and networks.
Track token usage and modeled cost at granular levels to optimize models, prompts and tenancy. Use these insights to prevent overruns and align expenditure with performance objectives.
Capture prompts and outputs (with configurable redaction), latency, throughput, error rates and model or provider metadata by using OpenLLMetry conventions. These operational signals can drive dashboards and alerts that help detect risky behavior and route it to the right owners.
Leverage native OpenLLMetry (Trace loop) instrumentation with coverage for major model providers and frameworks, GPUs and runtimes like vLLM. Keep AI data portable and vendor-neutral while unifying it with mature APM and infrastructure monitoring from Instana.
See how it works with an interactive demo
Benefits
Resources
Get complimentary access to the full Gartner Magic Quadrant report and explore how the Observability Platforms market is evolving, what to look for in a modern observability solution, and why IBM is a trusted choice.
See how AI Agents transform anomaly detection & resolution.
See how AI agents and LLMs predict and prevent IT issues in real-time.
Related use cases
Frequently asked questions
IBM Instana automatically traces every AI request across the entire GenAI workflow - from the user prompt to model inference and any downstream services, without requiring manual instrumentation. Instana maps all dependencies, correlates latency and error patterns, and highlights issues like slow inference, token bottlenecks, degraded GPU performance, or prompt failures.
IBM Instana captures AI-specific metrics such as prompt execution time, inference latency, token counts, model routing behaviour, embedding generation time, and vector database retrieval latency. It also surfaces errors like context-window limits, malformed prompts, and timeout events, helping teams monitor both model behaviour and surrounding services.
Instana visualizes every step in a RAG or multi-model pipeline, including embedding services, vector stores, API calls, LLM endpoints, and downstream microservices. Its analytics automatically identify the root cause of issues such as slow retrieval, fallback loops, model saturation, or API bottlenecks, making troubleshooting more efficiently.