Build resilient Azure AI applications with an AI Gateway

Building apps that work reliably under real-world conditions is still a challenge. Whether it's handling sudden traffic spikes, model timeouts, or hallucinations, developers need more than just access to powerful AI models - they need infrastructure that ensures resilience.

Microsoft’s Azure AI platform offers a solid foundation, making it easier than ever to integrate advanced AI into enterprise applications. But while Azure gives you access to cutting-edge tools, building resilient, production-grade AI systems still requires additional layers of control and observability.

Understanding the Azure AI ecosystem

Azure AI offers a powerful suite of services designed to help developers build, deploy, and scale AI applications quickly.

Here are the core components of Azure AI:

• Azure OpenAI: Provides access to OpenAI’s models (like GPT-4 and DALL·E) through enterprise-grade APIs, hosted on Microsoft’s infrastructure. It's the go-to choice for companies that want the power of OpenAI’s models with the security, compliance, and reliability of Azure.
  
• AI Foundry: A new addition to the Azure AI stack, AI Foundry offers tools for model fine-tuning, evaluation, and deployment. It’s designed to streamline the lifecycle of custom model development and helps enterprises move from experimentation to production faster.
  
• Azure AI Content Safety: A service built to detect and filter harmful or inappropriate content in AI outputs. This is especially critical for applications that involve user interaction, such as chatbots, copilots, and content generation tools.
  

Apart from these, there are AI platforms and tools on the Azure Marketplace to enhance the AI apps.

The missing piece: how to build resilience

While Azure AI provides powerful building blocks, many teams run into operational hurdles once they begin scaling their applications. These issues usually don’t surface during prototyping, but they become painfully visible in production.

You might start noticing sudden spikes in latency with no visibility into why requests are slow. At times, the models might time out or return incomplete responses, especially under high load. When that happens, there’s no easy way to handle failures gracefully or reroute the request to an alternative provider. If a user hits a rate limit or if a region goes down, you’re left scrambling to patch things manually.

Another common issue is inefficiency - applications repeatedly sending the same prompt without any caching layer to reduce redundant requests. This drives up token usage and cost unnecessarily. And as your user base grows, you’ll likely want to route traffic differently based on usage patterns, geography, or performance, but you’ll find that Azure doesn’t offer any runtime-level controls to do that dynamically.

All of this means that while Azure AI gives you access to cutting-edge models and services, you’re still responsible for stitching together the resilience needed to run reliably at scale.

Why do you need an AI Gateway for resilience?

As your application scales, the chances of encountering transient errors, model-level issues, or traffic bursts only grow. You need infrastructure that can adapt in real time, without manual intervention.

An AI Gateway sits between your application and the model endpoints, acting as a control layer that can make intelligent decisions about how each request is handled. If a request fails, it can be retried automatically without exposing the user to errors. If the primary model is slow or unavailable, the gateway can route the request to a fallback model. It can even choose which model to use based on custom conditions, like cost, latency, or the type of user making the request.

Beyond resilience, an AI Gateway also helps optimize performance and cost. For instance, caching frequently asked questions can drastically reduce token consumption and speed up response times. And because it operates at the request level, it gives you granular visibility and control that model providers simply don’t offer out of the box.

How Portkey helps you build resilient Azure AI applications

Portkey acts as a drop-in AI Gateway designed to help developers build with resilience from day one, without needing to re-architect their entire stack. If you're using Azure to build GenAI apps, Portkey integrates with the complete Azure ecosystem, making it easier for teams to get started.

The AI gateway brings control, flexibility, and observability to every request. You can define custom retry policies and fallbacks that kick in when Azure’s models fail or underperform. Whether it's GPT-4 timing out or a region hitting a rate limit, Portkey can automatically route requests to alternative models or endpoints based on the rules you set. Also, you can set up load balancing to distributethe  workload across multiple LLMs.

Conditional routing is another key capability. With Portkey, you can decide which model to call based on metadata in the request, like user tier, latency requirements, or even prompt content. This means you can dynamically balance cost and performance, or experiment with multiple models without code changes.

Portkey also includes a built-in caching layer, simple and semantic. It stores and reuses prompt-response pairs intelligently, saving on token costs and improving latency, especially for repetitive queries in production workloads.

All of this is wrapped in real-time LLM observability. You get full visibility into each request: what model was used, how long it took, what fallback kicked in, and whether a cache was hit. This makes debugging faster, operations smoother, and optimization more data-driven.

Also, Portkey is deeply integrated into the Azure ecosystem:

• You can deploy Portkey directly from the Azure Marketplace with minimal setup
• Microsoft Entra SSO & SCIM support allows you to control access, manage users, and stay compliant with org-level policies
• You can connect 1800+ models via Azure OpenAI and Azure AI Foundry and manage them through a single Portkey interface
• Azure AI Content Safety guardrails can be enforced on every Portkey request, helping ensure responsible AI behavior by default
• Developers can keep using familiar tooling — Portkey is compatible with the official OpenAI C# SDK, as well as Microsoft’s Autogen and Semantic Kernel frameworks

In short, Portkey's Azure integration turns your AI stack into a resilient, production-grade system, without slowing down your development process.

Get started on the AI gateway

Azure AI gives you access to some of the most powerful models and tooling in the industry, but raw power isn’t enough to deliver production-ready applications. Real-world AI apps need resilience.

Portkey's AI Gateway adds the missing control layer to your Azure AI stack, handling fallbacks, retries, routing, and caching, so you don’t have to build it all yourself. With Portkey, you’re not just calling models; you’re building AI systems that are predictable, cost-efficient, and reliable.

If you’re developing on Azure AI and want to go from prototype to production without compromising on resilience, it’s time to try Portkey.