Despite the rapid adoption of generative AI, the technology remains unpredictable and challenging to control. Large language models (LLMs) can hallucinate or produce factually incorrect responses, while sensitive data leaks and operational cost spikes pose significant enterprise risks.
The solution isn’t to restrict generative AI but to make it observable. Modern observability tools now extend beyond microservices and infrastructure, enabling full visibility into AI components. This marks a new era for AI adoption: no longer a black box, but a measurable, traceable, and optimizable system
What is LLM Observability – and Why Do You Need It?
One of the most significant challenges with generative AI systems is their inherent lack of visibility. A user submits a query, the system returns a response – but what actually happens behind the scenes remains opaque:
- What data sources did the model access?
- How long did response generation take?
- Why did a query incur specific computational costs?
- Did outputs potentially expose sensitive data?
This is where Observability becomes critical. Imagine extending monitoring beyond just infrastructure and application logic to:
- Prompt tracking (input analysis)
- Token consumption (resource utilization)
- Model responses (output validation)
- Cost parameters (performance-to-expense ratios)
Dynatrace and Amazon’s joint solution addresses this. Amazon Bedrock provides access to LLMs (Anthropic Claude, Mistral, Meta Llama), while Dynatrace – powered by Davis AI and OpenTelemetry – monitors the entire pipeline in real time.
Key Capabilities:
- Performance tracking: Response times, stability, and version changes (model fingerprinting),
- Cost control: Token usage metrics and cost predictions. View dashboards in Dynatrace Playground: https://dynatr.ac/4dnkuLX
- Security: Automated detection of prompt injections, data leaks, and inappropriate responses.
How LLM Observability Works in Practice?
To make a generative AI application truly observable, you need end-to-end visibility across the entire pipeline – from the initial user interaction through the LLM response and beyond. This requires monitoring multiple interdependent technology layers, which Dynatrace delivers through a unified platform capable of tracking, measuring, and visualizing all components.
The solution is built on these core pillars:
1. Application Layer – User Experience Monitoring
This layer connects LLMs with end-users. Dynatrace monitors:
- Response times
- User interactions (searches, queries)
- Error occurrences
- Usage patterns
Ensuring AI-powered experiences remain fast and reliable.
2. Orchestration Layer – Prompt Pipeline Tracking
Generative AI apps often use frameworks like LangChain for:
- Prompt composition (data injection)
- Component orchestration (search → query → response generation).
Dynatrace provides:
- Performance metrics per workflow step
- Error identification
- Distributed tracing to pinpoint processing delays
3. Semantic Layer – Data Enrichment Monitoring
For RAG (Retrieval-Augmented Generation) systems using vector DBs (e.g., Pinecone), Dynatrace tracks:
- Data relevance (helpful vs. noisy information)
- Retrieval latency (impact on response time)
- Search errors (incorrect data → flawed responses)
Dynatrace provides comprehensive monitoring at this level, tracking search operation quality and query execution times. Proactive alerting automatically triggers when a vector database responds too slowly or returned data contains errors or inconsistencies.
4. Model Layer – LLM Behavior Analysis
Core LLM monitoring includes:
- Token usage (input/output)
- Response latency and stability
- Output anomalies (hallucinations, biases)
- Model versioning (fingerprinting)
This makes it possible to immediately detect performance regressions such as increased response time or degraded response quality from model updates in real-time.
5. Infrastructure Layer – GPU, CPU, and Network Monitoring
Generative AI demands massive computational power – especially when running in Kubernetes environments. Dynatrace provides comprehensive monitoring of:
- GPU/CPU utilization
- Memory and bandwidth usage
- Infrastructure faults affecting response times
Unified Tracing: The Complete Picture
Dynatrace builds end-to-end traces for each query showing:
- Full request lifecycle
- Bottleneck locations
- Resource consumption
- Data flow pathways
Enabling real-time collaboration across Dev, Ops, Security, and Business teams.
See it in action: AI and LLM Observability with Dynatrace – YouTube
Hands-On Exploration:
- Demo Deployment:
Launch the generative AI sample app via GitHub Codespace: https://github.com/Dynatrace/obslab-llm-observability/tree/ollama-pinecone
- Production Implementation:
Follow the detailed Dynatrace AI Observability documentation: https://docs.dynatrace.com/docs/analyze-explore-automate/dynatrace-for-ai-observability/get-started/sample-use-cases/self-service-ai-observability-tutorial
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