An analysis of the top AI gateways with native OpenTelemetry support for observability. This post compares the leading options for teams that need to integrate LLM infrastructure into their existing monitoring stacks. Bifrost emerges as a strong choice for enterprises requiring robust, low-latency observability.
As engineering teams deploy AI applications into production, observability becomes a critical, non-negotiable requirement. Without detailed telemetry, debugging performance issues, tracking costs, and ensuring reliability is nearly impossible. OpenTelemetry has become the industry standard for cloud-native observability, and integrating AI infrastructure with it is essential. An AI gateway is the natural control point for this integration, centralizing request traces, metrics, and logs.
This post evaluates seven of the best AI gateways that provide native support for OpenTelemetry, enabling teams to pipe detailed observability data into their existing platforms like Datadog, Honeycomb, or Grafana. The options are assessed based on their performance, feature set, and enterprise-readiness.
Key Criteria for Evaluating Gateways
When choosing an AI gateway with OpenTelemetry support, teams should consider several key factors:
- Performance Overhead: How much latency does the gateway add to each request? This should be minimal to avoid impacting user experience.
- OTLP Integration Depth: Does the gateway support traces, metrics, and logs? How configurable is the data export?
- Provider Support: How many LLM providers does the gateway support out of the box?
- Enterprise Features: Does it offer features like high-availability clustering, role-based access control (RBAC), and advanced security guardrails?
- Ease of Deployment: Can the gateway be deployed easily in various environments, including Kubernetes, on-premise, and in a VPC?
The Top 7 AI Gateways for OpenTelemetry
1. Bifrost
Bifrost is a high-performance, open-source AI gateway from Maxim AI, written in Go. It is designed for enterprise-grade performance, reporting only 11 microseconds of overhead per request at 5,000 RPS in published benchmarks.
Bifrost’s observability features are comprehensive, with native support for exporting detailed telemetry via the OpenTelemetry Protocol (OTLP). It allows teams to send traces, metrics, and logs to any OTLP-compatible backend. This makes it a strong fit for organizations that have standardized on OpenTelemetry for their observability stack.
Best for: Enterprises and teams running mission-critical AI workloads that require best-in-class performance, comprehensive observability, and robust governance. Bifrost's low latency and deep OTLP integration make it the top choice for production systems.
Key Features:
- Native OTLP Export: Provides first-class support for OpenTelemetry, exporting rich, contextualized trace data for every request.
- Prometheus Metrics: In addition to OTLP, it exposes a
/metricsendpoint for Prometheus scraping. - High Performance: Adds negligible latency, making it suitable for real-time applications.
- Unified API: Offers a single, OpenAI-compatible API for over 20 LLM providers.
- Advanced Governance: The Bifrost AI gateway provides granular control through virtual keys, budgets, and rate limits. Beyond the gateway, Bifrost Edge extends that same governance and security to AI traffic on employee machines.
- Enterprise-Ready: Offers high-availability clustering, audit logs, and security guardrails.
2. LiteLLM
LiteLLM is a popular open-source library that provides a unified interface to call over 100 LLM APIs. It can be deployed as a proxy server, functioning as a lightweight AI gateway. Its observability features include a callback mechanism that can be used to export data to various backends, including OTLP-compatible systems.
While flexible, setting up robust OpenTelemetry integration often requires more custom configuration compared to gateways with native OTLP exporters. Teams may need to write custom callback handlers to format and send the data correctly. For teams seeking a different approach, Bifrost offers a page on LiteLLM alternatives.
Best for: Development teams and smaller projects that need a simple way to manage multiple LLM providers and are willing to configure observability integrations manually.
Key Features:
- Broad Provider Support: Extensive support for a wide range of LLM providers.
- Flexible Callbacks: A customizable callback system allows for integration with many observability platforms.
- Cost Tracking: Provides utilities for tracking token usage and estimating costs.
3. Kong AI Gateway
The Kong AI Gateway is an extension of the widely-used Kong API Gateway, built to handle AI and LLM traffic specifically. Leveraging Kong's robust plugin architecture, it offers features like prompt engineering, caching, and rate-limiting. Its observability is handled through Kong's existing telemetry plugins, which include support for OpenTelemetry.
This makes it a natural choice for organizations already invested in the Kong ecosystem. However, it may be a heavier solution for teams that do not need a full-featured API management platform.
Best for: Organizations already using Kong Gateway for API management who want to extend its capabilities to their AI workloads.
Key Features:
- Plugin Architecture: Extensible with plugins for AI-specific tasks and observability.
- Multi-LLM Support: Can route requests to various LLM providers.
- Enterprise-Grade: Built on a proven, scalable API gateway platform.
4. Cloudflare AI Gateway
Cloudflare AI Gateway is a managed service that provides caching, rate limiting, and analytics for AI applications. As part of the Cloudflare ecosystem, it benefits from the company's global network, offering low-latency access for users worldwide. It provides logs and analytics through its dashboard, with integrations for exporting data to other platforms.
While it offers visibility, its OpenTelemetry support is less direct than dedicated gateways, often relying on log forwarding and custom processing to generate traces and metrics.
Best for: Teams already using the Cloudflare stack who want a simple, managed solution for caching and basic analytics on their AI traffic.
Key Features:
- Global Network: Low-latency performance due to Cloudflare's edge network.
- Managed Service: Fully managed, reducing operational overhead.
- Caching and Analytics: Provides insights into request patterns and caches responses to reduce costs.
5. OpenRouter
OpenRouter is a hosted service that aggregates a wide variety of open-source and proprietary models, making them accessible through a unified, OpenAI-compatible API. It simplifies model discovery and routing, allowing users to direct requests to the best model for a given task, often based on cost or performance.
For observability, OpenRouter provides a detailed request log through its user interface. Programmatic access for integration with systems like OpenTelemetry is available but may require using the OpenRouter API to pull data rather than having it pushed via OTLP.
Best for: Developers and researchers who want easy access to a vast range of models for experimentation and are comfortable with a managed, hosted solution.
Key Features:
- Extensive Model Catalog: Access to hundreds of models from a single endpoint.
- Smart Routing: Finds the most cost-effective model for a given prompt.
- Usage Dashboard: Clear visibility into costs and request history.
6. NVIDIA NIM
NVIDIA NIM (NVIDIA Inference Microservices) is a set of optimized, pre-built containers designed to simplify the deployment of AI models. While not a gateway in the traditional multi-provider sense, NIM acts as a standardized entry point for serving models from NVIDIA's catalog or custom models. It integrates with the broader cloud-native ecosystem, including support for Prometheus and OpenTelemetry for monitoring GPU utilization and inference performance.
NIM is focused on the serving layer for self-hosted models, making it a powerful component of an AI stack that would typically sit behind a routing gateway like Bifrost.
Best for: Teams deploying and managing their own AI models on NVIDIA hardware who need standardized, high-performance inference endpoints with built-in observability.
Key Features:
- Optimized Performance: Pre-configured for high-throughput, low-latency inference on NVIDIA GPUs.
- Cloud-Native Integration: Includes support for Prometheus metrics and OpenTelemetry.
- Standardized API: Provides a consistent API for interacting with various models.
7. Azure AI Gateway
Microsoft's Azure platform offers AI gateway capabilities through services like Azure API Management and Azure AI Services. By combining these, teams can create a centralized entry point for managing access to Azure OpenAI, as well as other models. Azure Monitor provides extensive observability features, including integration with OpenTelemetry through its exporters.
This approach is powerful for teams deeply embedded in the Azure ecosystem but can involve configuring and connecting multiple services, increasing complexity compared to a single, dedicated gateway solution.
Best for: Enterprises heavily invested in the Microsoft Azure cloud that want to manage AI traffic using native Azure services and tooling.
Key Features:
- Deep Azure Integration: Seamlessly connects with other Azure services like Entra ID for authentication and Azure Monitor for observability.
- Managed Infrastructure: Leverages Azure's scalable and reliable infrastructure.
- Comprehensive Tooling: Access to the full suite of Azure's AI and cloud management tools.
How the Options Compare on Observability
| Gateway | Native OTLP Export | Primary Focus | Best For |
|---|---|---|---|
| Bifrost | Yes (Traces, Metrics, Logs) | Performance & Governance | Enterprise Production |
| LiteLLM | Via Callbacks | Unified LLM API | Development & Small Projects |
| Kong AI Gateway | Yes (Via Plugin) | API Management | Existing Kong Users |
| Cloudflare AI Gateway | Via Log Forwarding | Caching & Performance | Existing Cloudflare Users |
| OpenRouter | Via API Polling | Model Aggregation | Experimentation |
| NVIDIA NIM | Yes | Self-Hosted Inference | NVIDIA Hardware Users |
| Azure AI Gateway | Yes (Via Azure Monitor) | Azure Ecosystem | Azure-Centric Enterprises |
Recommendation
Choosing the right AI gateway depends on an organization's specific needs, existing infrastructure, and scalability requirements.
For enterprises and high-growth startups where performance, reliability, and deep observability are paramount, Bifrost stands out. Its minimal overhead, native and comprehensive OpenTelemetry support, and robust enterprise features make it the most suitable choice for demanding production environments. Its open-source nature also provides transparency and flexibility.
For smaller teams or those prioritizing simplicity and broad model access over deep integration, options like LiteLLM and OpenRouter offer a quick way to get started. For companies already committed to a specific cloud or API management platform, the native solutions from Azure, Cloudflare, and Kong provide a more integrated, albeit potentially less specialized, experience.
Ultimately, integrating AI traffic into a standard observability framework like OpenTelemetry is no longer optional. The gateways listed here provide a path to achieving that, with different trade-offs in performance, features, and complexity. Teams evaluating these options can request a Bifrost demo or review the open-source repository to explore its capabilities further.
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