The Best AI Gateways for Azure OpenAI in 2026

[This guide compares the top AI gateways for routing and managing Azure OpenAI traffic in production. For enterprise teams that require high performance, reliability, and comprehensive governance, the open-source Bifrost gateway is the strongest choice.]

Routing traffic to Azure OpenAI services requires more than a simple API call in production environments. As organizations scale their use of models like GPT-4, they face challenges with reliability, cost control, security, and observability. An AI gateway acts as a centralized control plane to solve these problems, providing essential features like automatic failover, load balancing, and granular access control. For teams building on Azure, selecting the right gateway is a critical infrastructure decision.

This article evaluates the best AI gateways for Azure OpenAI, comparing them on key criteria for enterprise use cases. We will examine features, performance, and deployment models to help teams choose the optimal solution for managing their AI workloads. The options range from dedicated, open-source gateways to native cloud services, each with distinct trade-offs.

Key Criteria for Evaluating Azure OpenAI Gateways

When assessing an AI gateway, engineering and platform teams should look beyond basic proxying. The following capabilities are essential for production-grade AI applications using Azure OpenAI:

• Reliability and Failover: The gateway must handle API errors and regional outages gracefully. It should support automatic fallbacks to other deployments, regions, or even different providers (like Anthropic or Google Gemini) to ensure zero downtime.
• Performance: Gateway latency is critical. The added overhead should be minimal to avoid impacting the user experience. Look for gateways with published benchmarks that demonstrate low single-digit millisecond or microsecond overhead under load.
• Governance and Cost Control: The ability to issue virtual keys with specific budgets, rate limits, and model access policies is fundamental. This prevents runaway costs and enforces organizational policies at a granular, per-application or per-user level.
• Security and Compliance: A gateway must secure credentials and enforce data policies. For regulated industries, features like integration with secrets managers, role-based access control (RBAC), and immutable audit logs are non-negotiable.
• Observability: Detailed, real-time logging and monitoring are necessary for debugging and performance tuning. Look for native support for tools like Prometheus, OpenTelemetry, and Datadog.
• Deployment Flexibility: Teams need options. The ideal gateway supports deployment in any environment, including in a private VPC, on-premises data centers, or as a managed service.

1. Bifrost

Bifrost is a high-performance, open-source AI gateway written in Go. It is designed for enterprise teams that need a scalable, reliable, and governable layer for their AI traffic, including sophisticated Azure OpenAI deployments.

Bifrost unifies access to over 1000 models from dozens of providers, including a first-class integration for Azure OpenAI. It acts as a drop-in replacement for the OpenAI SDK, meaning teams can integrate it by changing only the base URL in their existing code.

Best for: Enterprise teams building mission-critical AI applications on Azure that require the highest levels of performance, reliability, and security. Its deployment flexibility makes it ideal for hybrid-cloud strategies and regulated industries.

Key Features:

• High Performance: Adds only 11 microseconds of overhead per request at 5,000 RPS, making it one of the fastest gateways available.
• Advanced Reliability: Provides automatic, configurable failover and fallback chains. If an Azure OpenAI endpoint in one region fails, Bifrost can automatically route traffic to another region, a different deployment ID, or another provider entirely.
• Enterprise-Grade Governance: Its virtual key system allows for precise control over budgets, rate limits, and model access. The Bifrost Enterprise version adds RBAC, OIDC integration with Microsoft Entra ID, and access profiles for at-scale user provisioning.
• Comprehensive Security: Bifrost supports in-VPC deployments for maximum data privacy. It integrates with secrets managers like Azure Key Vault and provides detailed audit logs for compliance with standards like SOC 2 and HIPAA.
• Unified Observability: Native integrations with Prometheus, OpenTelemetry, and a Datadog connector provide deep visibility into request patterns, latency, and costs.
• Endpoint Governance: Beyond the gateway, Bifrost Edge extends the same governance and security policies to AI traffic on employee machines, providing a complete solution for managing both cloud and endpoint AI usage.

2. Azure API Management (APIM)

Microsoft's native solution, Azure API Management (APIM), can be configured to function as a gateway for Azure OpenAI services. As an existing Azure service, it offers deep integration with the Azure ecosystem, including Azure Active Directory (now Entra ID) for authentication and Azure Monitor for logging.

Using APIM involves creating an API proxy that sits in front of one or more Azure OpenAI endpoints. Policies can be applied to handle tasks like authentication, rate limiting, and request transformation.

Best for: Organizations already heavily invested in the Azure ecosystem that need a basic gateway and prefer to use native Azure services. It is a good starting point for teams whose primary requirement is centralized endpoint management within Azure.

Key Features:

• Native Azure Integration: Seamlessly connects with other Azure services.
• Policy-Based Control: Uses a flexible policy engine to enforce rate limits, quotas, and JWT validation.
• Security: Integrates with Microsoft Entra ID for authentication and provides standard Azure security features.
• Developer Portal: Offers a built-in developer portal for API discovery and documentation.

However, APIM is a general-purpose API gateway, not a specialized AI gateway. Features like automatic provider failover to non-Azure services, semantic caching, and a unified API for multiple LLM providers are not built-in and require significant custom development to implement.

3. Kong AI Gateway

Kong AI Gateway is a product from the popular API gateway provider Kong. It extends their core gateway with AI-specific capabilities and can be deployed in front of Azure OpenAI services. Kong runs in your own infrastructure, offering control over the data path.

The AI Gateway includes features like multi-LLM support, prompt engineering plugins, and AI-specific analytics. It allows teams to manage access to different models, including Azure OpenAI, through a unified interface.

Best for: Companies that have already standardized on Kong for their existing microservices and API management. Its AI features provide a convenient add-on for managing LLM access within a familiar platform.

Key Features:

• Multi-LLM Support: Provides a single endpoint to access various models.
• AI Plugins: Offers plugins for prompt templating, validation, and engineering directly within the gateway.
• Performance: Built on Kong's high-performance core.
• Full Lifecycle Management: Integrates with Kong's broader API management platform.

While powerful, Kong's AI capabilities are an extension of its core product. Teams not already using Kong may find its complexity and feature set to be more than what is required for a dedicated AI gateway role.

4. LiteLLM

LiteLLM is a popular open-source library that provides a unified interface for calling over 100 LLM providers, including Azure OpenAI. While primarily a library, it can be deployed as a self-hosted proxy server, allowing it to function as a lightweight AI gateway.

It standardizes inputs and outputs into the OpenAI format, simplifying the process of switching between models and providers. Its proxy feature adds capabilities like logging, key management, and a simple UI for viewing requests.

Best for: Development teams, startups, and projects that need a simple, open-source way to standardize LLM API calls and add basic logging and routing. It is an excellent tool for prototyping and applications with less stringent enterprise requirements.

Key Features:

• Unified API Format: Simplifies multi-provider development by standardizing on the OpenAI format.
• Lightweight and Simple: Easy to set up and deploy as a standalone proxy.
• Callback Functions: Supports logging requests and responses to various destinations.
• Basic Routing and Budgeting: Offers simple routing logic and cost tracking.

LiteLLM is a developer-focused tool and lacks the advanced governance, security, and reliability features of enterprise-grade gateways like Bifrost. For a detailed comparison, see the Bifrost LiteLLM alternatives page.

How the Gateways Compare for Azure OpenAI

Feature	Bifrost	Azure API Management	Kong AI Gateway	LiteLLM
Primary Use Case	Enterprise AI Gateway	General API Management	General API Management	LLM API Abstraction
Performance	11µs overhead	Variable	High	Moderate
Automatic Failover	Yes (cross-provider)	No (requires custom logic)	Yes (within Kong)	Basic
Virtual Keys/Budgets	Yes (advanced)	No (uses quotas)	Yes (via consumers)	Yes (basic)
Observability	OTel, Prometheus, Datadog	Azure Monitor	Yes (via plugins)	Basic (callbacks)
Open Source	Yes (Apache 2.0)	No	Yes (core is OS)	Yes (MIT)
Deployment	Any (VPC, on-prem)	Azure-only	Any (self-hosted)	Any (self-hosted)

Recommendation and Next Steps

Choosing an AI gateway for Azure OpenAI depends on your organization's scale, security posture, and performance needs.

For large enterprises, regulated industries, or any team building mission-critical AI products, Bifrost offers the most complete and performant solution. Its combination of enterprise-grade governance, best-in-class reliability, and deployment flexibility provides a robust foundation for scaling AI applications securely. The ability to govern both cloud and endpoint AI traffic from a single control plane makes it a strategically sound choice.

For teams deeply embedded in the Azure ecosystem with basic needs, Azure APIM is a viable starting point. Kong is a strong contender for organizations that already use it for API management, and LiteLLM is an excellent open-source tool for development and smaller-scale projects.

As AI workloads become more complex and central to business operations, the need for a specialized, high-performance gateway becomes clear. Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository to explore its capabilities further.