What Exactly is an AI Gateway?

An AI Gateway serves as a controlled entry point for all LLM interactions. Rather than connecting directly to model endpoints, applications and agents direct their calls to this gateway which handles:

• ▸Authentication: It validates the caller—preferably employing Microsoft Entra ID instead of a shared key.
• ▸Policy Enforcement: This includes token budgets, rate limits, content safety checks, and logging mechanisms.
• ▸Routing: The gateway directs requests to the appropriate backend—be it a PTU deployment, pay-as-you-go service, or a different provider.
• ▸Performance Measurement: It tracks every token used to facilitate effective cost management and detect irregularities.

Within Azure, the AI Gateway is supported by Azure API Management (APIM), which now provides a tailored set of policies specifically designed for LLMs. This goes beyond merely placing an API gateway in front; traditional API gateways focus on counting requests, but an AI Gateway counts tokens—an essential aspect in the LLM domain, where tokens equate to costs.

Key InsightAn AI Gateway does not replace your application or agent runtime; rather, it governs how AI is consumed without dictating where your applications are operated.

Seven Reasons to Implement an AI Gateway (Before Your CFO Raises Concerns)

The Effective Structure: Hub, Spoke, and Clear Boundaries

The most effective design in enterprise landing zones typically comprises three layers:

Key design considerations include:

• ▸Deploy the AI Gateway as a standalone APIM instance within a dedicated AI platform subscription. Avoid merging it with the enterprise’s main APIM which serves business APIs, as their scaling needs and operational contexts differ.
• ▸Maintain separate non-production and production AI Hubs. Since gateway policies and model upgrades reflect changes, they require an established promotion process.
• ▸Centralise LLM deployments (especially PTUs) for better capacity planning instead of fragmenting across multiple subscriptions.
• ▸Route traffic from spokes to the hub through the hub firewall. This ensures that any cross-subscription traffic is treated with the necessary security measures.

What Not to Route Through the Gateway

While it’s tempting to route all traffic through the gateway, this is not advisable.

Guidance Direct traffic should align with value-added governance (tokens, trust boundaries, external parties), while latency concerns should guide direct access.

Implementing the Gateway: Practical Insights

Here’s a concise inbound policy that incorporates essentials like Entra ID authentication, application-specific token budgets, token metrics, a managed backend identity, and a load-balanced backend pool.

  
    
    
    
      
        api://ai-gateway
      
    

    
    

    
    
      
      
    

    
    

    
    
  

Enhance your gateway with llm-content-safety alongside policies for semantic caching as necessary, and you’ll establish a robust production environment.

APIM Alternatives: Open-Source AI Gateways to Explore

Though APIM is the obvious choice for Azure environments, the AI gateway design principle isn’t restricted to one product. Two prominent open-source alternatives frequently arise in discussions with engineering teams that address distinct issues.

LiteLLM: The Open-Source Enterprise Gateway

LiteLLM ranks among the most widely utilised open-source AI gateways. It can function as a Python SDK or operate as a proxy Server (gateway mode), showcasing over 100 providers, such as Azure OpenAI, Foundry, Bedrock, Vertex AI, Anthropic, vLLM, and NVIDIA NIM, through a single OpenAI-compatible API. Gateway mode encapsulates the essential features discussed:

• ▸Team-Specific Virtual Keys prevent any single individual from holding a provider’s key.
• ▸Budgeting and Spending Control is available per key, user, or team.
• ▸Load Balancing and Fallbacks manage deployments and providers effectively.
• ▸Implementation of Guardrails, Caching, and Logging hooks alongside an administer-friendly UI.
• ▸MCP and A2A functionalities to facilitate tool and agent communication.

Suitable Azure Context: This gateway excels in multi-cloud or multi-provider scenarios where the platform team requires a code-friendly, self-managed gateway, or for workloads lacking support from native APIM. Consider deploying it on AKS or Azure Container Apps within a spoke setup featuring private networking, leveraging Azure Database for PostgreSQL and Azure Managed Redis, and integrate it behind your Landing Zone ingress like any other workload.

My Preferred Structure For larger enterprises: Utilise APIM at the forefront, with LiteLLM as a subsequent layer. This way, APIM manages enterprise authentication, WAF integration, and security policies, whereas LiteLLM addresses provider-specific needs for translation and routing. It’s advisable to limit the structure to two layers, as each additional layer can increase latency and add complexity to ongoing operations.

OmniRoute: The Developer-Friendly AI Router

OmniRoute presents an alternative focus. It is a local-first, MIT-licensed gateway tailored for individual developers and coding platforms such as Claude Code, Codex, Cursor, Cline, and Copilot. With one endpoint, your tools can connect effortlessly while OmniRoute manages:

• ▸Quota-Aware Automatic Fallbacks across tiers (subscription → API key → budget-friendly → free), ensuring uninterrupted coding as quotas fluctuate.
• ▸Token Compression that the project claims reduces token usage significantly during coding tasks.
• ▸A Directory of Numerous Providers with many offering free tiers.
• ▸MCP and A2A connectivity, featuring a desktop/PWA dashboard.

Monitor Your Prompt Processing While it’s a fantastic productivity tool for personal projects, hackathons, and testing, it should never serve as the official governance layer for enterprises. The project openly acknowledges that it keeps a list of providers that may pose terms-of-service risks, and free tier offerings frequently change. When a request can revert to a free third-party offering, you lose control over where prompts (and associated data) are processed or stored. For corporate code or sensitive data, direct developer tools to your approved gateway instead.

Quick Feature Comparison

My Key Recommendation: For an enterprise-centric Azure deployment, start with APIM. Integrate LiteLLM as needs shift towards handling multiple providers or for a code-first environment. Employ OmniRoute to enhance individual productivity, but never let it substitute your enterprise’s governance structure. Lastly, ensure proper treatment of any self-hosted solution as a critical dependency: specify version requirements, verify container images, and assess your supply chain.

Common Pitfalls in Implementation

• ▸Relying solely on subscription keys for authentication. These keys merely identify users; they shouldn’t be your exclusive control mechanism. Instead, employ Entra ID and deactivate local (key) authentication for model resources using Azure Policy.
• ▸Inadequate scoping for semantic caching. A cache that doesn’t differentiate between users or tenants might inadvertently expose one user’s answers to another. Partition caches, set TTLs, and disable them for high-risk workloads.
• ▸Logging complete prompts without access restrictions. Prompts may contain sensitive information. Determine access rights to LLM logs and data retention guidelines prior to enabling logging.
• ▸Consideration of the gateway as a single point of failure. If all AI workloads rely on it, you must ensure it has availability zones, cluster scalability, and a disaster recovery strategy. Model-level failover is ineffective if the gateway fails.
• ▸Overlooking data residency requirements. A “Global” model can process prompts outside your designated region. For sovereign or regulated areas, document every model, its deployment methods, and processing locations. The gateway can route data wherever directed, so it won’t automatically enforce residency.
• ▸Neglecting streaming functionalities. Most chat interfaces rely on streaming. Test token accounting, content safety measures, and any masking logic with streaming enabled, not solely through Postman.

Adoption Guidelines

• ✅A dedicated AI platform subscription inclusive of its own APIM instance (selecting a tier that offers private networking and zone redundancy)
• ✅Separation of non-production and production AI Hubs, complete with a structured promotion pipeline for policies and model associations
• ✅Utilisation of Entra ID for all consumers, with a managed identity for backend connections, while deactivating local authentication
• ✅Establishment of token limits and quotas for each application or team
• ✅Dashboard feeds of token metrics into Azure Monitor, alongside budget alerts
• ✅Backend pools equipped with priority routing and circuit breaker functionalities
• ✅Enforcement of a content safety policy for models lacking built-in filters
• ✅A clear delineation of what is exempt from passing through the gateway, along with sufficient reasoning
• ✅A defined retention policy for LLM logs and a set access structure agreed upon with security and privacy teams.
• ✅An established onboarding procedure for MCP and A2A interactions concerning shared and external tools.

Final Thoughts

In 2023, the primary question was “which model to utilise?” By 2026, the focus shifts to “how can we allow multiple teams to effectively use various models, numerous tools, and an increasing count of agents securely, economically, and transparently?”

The AI Gateway is the answer to these queries without hindering productivity. Construct it from the forefront, concentrating on governance rather than making it a catch-all solution, enabling your AI platform to evolve in tandem with business objectives.

Next in this series: AI Networking on Azure, which will delve into private endpoints, DNS management, egress controls for agents, and common subnet sizing errors to avoid.