How a leading FinTech enterprise went from fragmented, siloed AI operations to a governed, enterprise-grade observability platform built on Azure and Datadog.
Token Attribution
Distributed Tracing
FinTech Technology (FinTech)
Enterprise, multiple distributed engineering teams
Azure AI Foundry, Azure OpenAI
Uncontrolled AI costs, inconsistent model governance, fragmented tracing
Cloud and AI Platform Architecture Consulting
A leading FinTech enterprise faced a familiar but costly problem: multiple engineering teams consuming shared Azure AI services with no unified view of usage, costs, or model performance. As their AI portfolio grew, this gap created real risk, overruns had no early warning, model migrations were guesswork, and production incidents were slow to diagnose.
The organization engaged a senior cloud and AI consulting practice to design and deploy a scalable observability and governance platform. The result is a centralized architecture that combines Azure-native controls with Datadog-based observability, delivering token-level cost attribution, structured model benchmarking, and end-to-end distributed tracing across every engineering team.
Multiple engineering teams consumed shared Azure-hosted AI services independently, with no unified view of request volumes, token usage, latency, or cost. Leadership had no reliable way to assess platform adoption or spending efficiency, making budget planning reactive and imprecise.
Model upgrades and version migrations were handled manually, driven by subjective judgment and inconsistent validation criteria. Without a shared benchmark, teams couldn't compare models objectively, making deprecation responses slow and high-risk. Every migration carried unnecessary uncertainty.
Existing Datadog coverage did not extend to AI-specific request tracing. Teams were unable to follow a user request through APIs, LLM interactions, and downstream database calls. Root cause analysis during incidents was time-consuming, often requiring engineers to stitch together logs from multiple systems manually.
Azure API Management (APIM) was deployed as the control plane for all LLM traffic. Every call to Azure OpenAI Service now routes through APIM, where subscription keys identify the consuming application and custom policies capture request metadata, token counts, and cost signals. This data flows into Azure Monitor Workbooks via KQL queries, giving operations teams a real-time view of usage across the enterprise.
In parallel, Datadog LLM Observability was integrated using the Datadog SDK. This layer captures prompt execution details, model responses, latency distributions, and token consumption at the individual request level. Together, APIM and Datadog provide both an executive cost view and a granular engineering view from a single unified data pipeline.
A structured evaluation framework replaced ad hoc model comparison. Before any model change, candidate models are tested under identical conditions and scored using an LLM-as-a-Judge methodology. Every model is assessed across quality, relevance, and correctness dimensions, with native Datadog evaluators adding automated signals for sentiment, hallucination detection, toxicity, and topic relevance.
Results are published through Datadog Experiments, giving stakeholders a clear, side-by-side view of model performance before any production change is approved. This turns model governance from a subjective conversation into an evidence-based decision process.
Applications were instrumented using the Datadog SDK to produce a continuous trace for every user request, from system entry through application logic, AI interactions, outbound API calls, and database operations. Every hop in the request lifecycle is visible in one trace view.
This capability allows teams to isolate slow transactions, pinpoint high-latency dependencies, and reduce mean time to investigate production incidents dramatically. What once required manual log correlation across multiple systems is now a single query in Datadog APM.
Microsoft Azure
Azure AI Foundry
Azure OpenAI Service
Azure API Management (APIM)
Azure Monitor
KQL
Azure Monitor Workbooks
Datadog LLM Observability
Datadog Experiments
LLM-as-a-Judge
Datadog APM
Datadog SDKs
Each team tracked AI usage in isolation. Leadership had no shared view across the organization.
This engagement moved a leading FinTech enterprise from fragmented, team-level AI operations to a governed, scalable enterprise platform. By combining Azure API Management, Azure Monitor, Datadog LLM Observability, Datadog Experiments, and distributed tracing, the organization now has the infrastructure to monitor, evaluate, and optimize a growing portfolio of LLM-powered applications with confidence.
The solution addressed immediate operational pain around token tracking, model evaluation, and request tracing, while also establishing a long-term operating model for responsible AI adoption. New AI workloads onboard into the governance framework with minimal rework, giving both engineering teams and business leadership the visibility and control they need as AI adoption grows.
The architecture is not just a monitoring layer. It is a foundation for enterprise AI operations that will scale with the organization's ambitions.
