Integrating LLM Gateways with OpenTelemetry for Enhanced AI Observability

Jin-Ho Kwon — Thu, 02 Jul 2026 17:27:11 +0000

Observability in complex AI systems requires more than just metrics; it requires deep, contextual tracing. Integrating an LLM gateway like Bifrost with OpenTelemetry provides a standardized, vendor-neutral way to trace requests across your entire application stack, from your services to the AI models and back.

Modern AI applications are complex distributed systems. A single user query can trigger a cascade of internal service calls, database lookups, RAG pipeline executions, and multiple requests to different LLM providers. When latency spikes or responses are inaccurate, pinpointing the root cause is difficult without a clear view of the entire request lifecycle. This is the observability challenge that distributed tracing is designed to solve.

OpenTelemetry has emerged as the open standard for instrumenting, generating, collecting, and exporting telemetry data—traces, metrics, and logs. As a Cloud Native Computing Foundation (CNCF) project, it offers a vendor-neutral framework, allowing engineering teams to use a consistent set of APIs and SDKs to instrument their applications and send data to any compatible observability backend. For teams building with LLMs, this means you can trace a request from your user-facing application, through your backend services, into an AI gateway, and see the full context of the LLM provider call in one unified view.

An AI gateway is a natural place to generate this kind of telemetry. As the central hub for all LLM traffic, it has complete context on every request and response. Bifrost, an open-source AI gateway from Maxim AI, includes a native OpenTelemetry integration that exports detailed trace data automatically.

What is OpenTelemetry?

OpenTelemetry (often abbreviated as OTel) is an observability framework formed from the merger of two previous projects, OpenTracing and OpenCensus. It provides a single, standardized specification and a collection of tools, APIs, and SDKs to instrument applications for telemetry data collection. The core components include:

APIs: Language-specific interfaces for generating telemetry data within application code.
SDKs: Implementations of the APIs that process and export data.
Collector: A vendor-agnostic proxy that can receive, process, and export telemetry data to one or more backends.
Exporters: Components that send data to specific observability platforms like Jaeger, Prometheus, Datadog, or Grafana Cloud.

The key benefit of OTel is its vendor neutrality. Teams can instrument their code once and switch observability backends with a simple configuration change, avoiding vendor lock-in.

Why OpenTelemetry is Crucial for LLM Applications

Traditional application performance monitoring (APM) often focuses on metrics like HTTP error rates and p99 latency. These are necessary, but insufficient for AI applications. An LLM-powered feature can be slow, expensive, and functionally incorrect without ever returning an HTTP error code.

This is where OpenTelemetry's distributed tracing shines. Tracing allows you to follow a single request as it propagates through multiple services, showing the full chain of events and the time spent in each one. For an LLM application, this might look like:

A user submits a request to your web application (Span 1).
The web app calls a backend service to construct a prompt (Span 2).
The backend service retrieves data from a vector database (Span 3).
The service sends the final prompt to an AI gateway (Span 4).
The gateway forwards the request to an LLM provider like OpenAI or Anthropic (Span 5).

Each of these steps is a "span," and together they form a single trace. This gives engineers a complete picture of where latency is introduced. More importantly, with LLM-specific conventions, these traces can be enriched with metadata that is critical for debugging AI behavior.

The OpenTelemetry community has developed GenAI Semantic Conventions that standardize how to record this metadata. Attributes include:

gen_ai.request.model: The specific model name used (e.g., gpt-4o).
gen_ai.request.temperature: The temperature setting for the request.
gen_ai.usage.input_tokens: The number of tokens in the prompt.
gen_ai.usage.output_tokens: The number of tokens in the completion.
gen_ai.system: The AI provider system being called (e.g., openai, anthropic).

By adhering to these conventions, an AI gateway can provide standardized, actionable data that any compliant observability platform can understand and visualize.

How Bifrost Integrates with OpenTelemetry

An AI gateway is the ideal component to generate LLM-related trace data because it sits at the nexus of all AI traffic. Bifrost provides a native OTLP exporter that sends detailed traces for every request to a configured OpenTelemetry collector. This integration requires no changes to your application code.

The Bifrost OTel plugin captures a rich set of data for all request types, including chat completions, embeddings, and text-to-speech, and maps them to the appropriate GenAI semantic conventions.

Key features of the integration include:

Standard Compliance: All traces follow the official OpenTelemetry GenAI semantic conventions, ensuring compatibility with platforms like Grafana, Datadog, New Relic, and Honeycomb.
Protocol Support: The plugin supports both OTLP/HTTP and OTLP/gRPC protocols for exporting data to a collector.
Trace Propagation: Bifrost respects the standard traceparent W3C Trace Context header. If an incoming request from your application already includes this header, Bifrost continues the existing trace, creating its spans as children of the application's span. This provides a seamless, end-to-end view.
Rich Metadata: Spans are automatically enriched with request parameters (model, temperature, max tokens), response details (finish reason), provider information, and usage metrics (token counts).
Gateway-Specific Context: Traces also include valuable gateway-level context, such as which virtual key was used, whether a request was served from the semantic cache, and the state of provider fallbacks.

This gateway-level tracing means you get deep visibility into your LLM operations without needing to instrument every single application that makes an AI call. The gateway handles the instrumentation centrally.

Example Configuration

Configuring Bifrost to export traces is straightforward. In the gateway's configuration file, you enable the otel plugin and specify the collector endpoint.

plugins:
  - name: otel
    config:
      service_name: "bifrost-ai-gateway"
      collector_url: "http://otel-collector.observability:4318" # OTLP/HTTP endpoint
      protocol: "http"
      trace_type: "genai_extension"
      headers:
        # Optional headers for authentication
        Authorization: "Bearer ${OTEL_AUTH_TOKEN}"

With this configuration, every LLM request passing through the Bifrost AI gateway will generate a trace and export it to the collector, which can then forward it to your chosen observability backend. This centralizes telemetry generation and ensures consistent, high-quality data for monitoring and debugging. Furthermore, Bifrost's built-in governance and security controls can be extended to the endpoint with Bifrost Edge, which routes AI traffic from employee machines through the gateway, bringing even more traffic under a single observability plane with its endpoint enforcement capabilities.

Conclusion: A Unified View for Complex Systems

As AI applications become more integral to business operations, a reactive approach to monitoring is no longer sufficient. Teams need proactive observability to understand performance, control costs, and ensure reliability. Integrating a high-performance AI gateway with OpenTelemetry provides a powerful, standardized solution.

By centralizing the generation of LLM trace data at the gateway level, teams can achieve deep visibility with minimal instrumentation effort. This approach ensures that as your AI stack grows and evolves, your observability strategy can keep pace, providing a unified view that connects application performance directly to AI model behavior.