Enabling End-to-End Traceability for an Azure AI-Powered Application Using OpenTelemetry
What Changed With Observability ?
Overview
With the rapid growth of AI applications, having clear visibility into how everything works behind the scenes has become more important than ever. In this case study, we share how we partnered with a global asset management firm to bring full end-to-end traceability to their AI application running on Azure. By combining the power of OpenTelemetry for distributed tracing with Azure Application Insights for centralized observability, we helped them gain the insights they needed to keep their systems running smoothly and reliably.
Client Background
Our client is a leading global asset management firm known for its innovative approach to alternative investments and retirement solutions. Guided by values like pushing boundaries, creating opportunities and leading with integrity, they support both institutional and individual investors across credit, equity, real assets and retirement strategies.
As part of their digital transformation journey, the firm built an AI-powered Application on Azure to enhance customer engagement—showcasing their commitment to responsible, scalable technology and modern client experiences.
Challenges
Building a scalable AI application isn’t just about delivering answers it’s about understanding the entire user journey. For our client, having complete traceability was key. They needed to see exactly how each prompt was processed so they could quickly spot issues, ensure reliable performance and maintain compliance across their Azure-based AI and search systems.
- Lack of End-to-End Visibility: The primary challenge centered on achieving comprehensive visibility across the entire request lifecycle. Without proper traceability, the client faced significant obstacles in:
- Understanding user journey mapping from initial prompt to final response
- Identifying bottlenecks in their complex Azure AI service integrations
- Maintaining compliance standards across their regulated financial services environment
- Complex Multi-Service Architecture: The application was built on a sophisticated ecosystem of tightly integrated Azure services, each playing a key role in delivering a seamless user experience:
- Azure OpenAI powered the natural language understanding and generation capabilities.
- Azure AI Search enabled intelligent document indexing and retrieval.
- Backend APIs handled the core business logic and processing workflows.
- Orchestration components ensured smooth coordination and communication across all services.
- Operational Reliability Challenges: In the absence of end-to-end monitoring, the client faced several reliability issues that impacted day-to-day operations:
- Trouble identifying root causes when system failures occurred, leading to longer resolution times.
- No proactive alerts for emerging issues, resulting in delayed responses and potential user impact.
- Limited visibility into AI model behavior, including performance and accuracy, which hindered continuous improvement efforts.
Our Strategic Solution Architecture
To give our client complete transparency, reliability and quick issue resolution within their AI application, we built a full end-to-end observability setup using OpenTelemetry (Otel). We made sure to track every key part of their Azure-based architecture—like Azure AI Search, OpenAI models, backend APIs, and orchestration workflows. This allowed us to capture detailed insights into how each request was handled from start to finish.
Comprehensive OpenTelemetry Implementation
We focused on implementing a full-stack observability solution built on OpenTelemetry, a leading open-source standard that’s rapidly shaping modern monitoring practices. OpenTelemetry provided a unified way to collect and analyze telemetry data across all layers of the application.
- Parent-Child Span Hierarchy:
To achieve complete visibility into the request lifecycle, we implemented a robust tracing architecture based on a parent-child span hierarchy. This structure allowed us to break down each user request into meaningful, traceable steps:- Root Span: Captured the initial user prompt or interaction.
- Child Spans: Represented each downstream processing activity, including:
- Document retrieval via Azure AI Search
- Data transformation and preprocessing
- Invocation of Azure OpenAI models
- Calls to backend APIs
- Final response aggregation and delivery
- Each span was enriched with detailed contextual metadata, enabling deep insights into system behavior:
- Document relevance scores and retrieval details
- Execution times, input parameters, and function names
- AI model configurations and version tracking
- Logged errors, exceptions, and failure points
- Correlation IDs for end-to-end session tracking
- Seamless Azure Service Integration:
To ensure smooth observability across the entire ecosystem, we leveraged OpenTelemetry SDKs along with Azure-compatible exporters for deep, native integration with key Azure services:- Azure Monitor for centralized logging and real-time metrics collection
- Application Insights to power advanced analytics, dashboards, and visualizations
- Azure Functions to monitor serverless components and execution behavior
- Enhanced Debugging and Troubleshooting:
We significantly enriched the telemetry data to streamline root cause analysis and accelerate issue resolution. Each trace was augmented with essential debugging insights, including:- Error flags and detailed exception logs for precise failure diagnosis
- Performance metrics for every service call, highlighting latency hotspots
- Business context metadata, such as the specific documents retrieved and AI models invoked
- Custom correlation IDs to trace user sessions across multiple components
- This depth of observability allowed teams to quickly answer key operational questions, such as:
- “Which document led to an irrelevant response?”
- “Was the delay caused by the AI model or Azure AI Search?”
- “Which component failed, and what exactly went wrong?”
By leveraging OpenTelemetry effectively, it’s possible to track and attribute AI model costs by monitoring token usage, enabling more accurate cost management and forecasting. Additionally, we can create a token usage dashboard segmented by model to provide clearer visibility into usage patterns and cost distribution.
Below Image shows the full request tracing in of the AI application including user prompt, model name, latency time, Invoked function name, etc.
Implementation Results and Business Impact
Measurable Performance Improvements: The OpenTelemetry implementation delivered significant operational improvements:
Cost Optimization Benefits: Adopting OpenTelemetry-based observability strategies has led to significant cost savings for many organizations. Key benefits include:
- Total 30% reduction in observability-related costs, driven by more efficient data collection and analysis.
- Faster issue detection and resolution, with reduced Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR)—minimized downtime and its associated costs.
- Improved resource utilization, as teams gained deeper visibility into system performance and were able to optimize infrastructure usage.
- Lower operational overhead, thanks to reduced reliance on manual monitoring and reactive troubleshooting.
Industry Context and Market Trends
- OpenTelemetry Adoption Trends: OpenTelemetry has rapidly become the de facto standard for observability across the industry, driven by its flexibility, vendor-neutrality, and robust community support:
- One of the fastest-growing projects under the CNCF umbrella, with backing from major cloud and observability vendors
- Over 10,000 contributors from 1,200+ companies worldwide
- A 445% year-over-year increase in Python library downloads, highlighting widespread adoption in AI and data applications
- 98.7% of organizations express confidence in OpenTelemetry’s long-term vision and direction
- AI Application Monitoring Priorities: As AI becomes a core component of modern applications, observability strategies must evolve to address AI-specific concerns. Key monitoring priorities include:
- Token usage and cost tracking to manage AI model consumption effectively
- Quality and safety evaluation of AI-generated outputs for alignment and risk mitigation
- Latency and performance monitoring to ensure responsiveness in real-time AI applications
- Security observability tailored to detect and respond to AI-specific vulnerabilities and threats
By implementing OpenTelemetry for full traceability across their Azure AI application, Our client gained deep visibility into how every user prompt was handled from the moment it was received to the final response. This didn’t just solve their immediate issues it also laid the foundation for ongoing performance, reliability and user experience improvements.
Here’s what they achieved:
Conclusion
Implementing end-to-end tracing with OpenTelemetry transformed how our client understood and managed their Azure AI Application. By capturing every step from prompt to response they gained clear visibility into the system, making it easier to fix issues, improve performance, and deliver more accurate answers. With trace data now flowing into Azure Application Insights, the team can quickly respond to feedback, ensure reliability and keep improving turning observability into a key driver of trust and continuous improvement.
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