How to design a reliable fallback system for LLM apps using an AI gateway

LLMs are powerful but not perfect. In production environments, they can fail for a range of reasons - rate limits, timeouts, quota issues, or even just returning unreliable outputs. For teams building LLM-powered applications, reliability is critical.

LLM outputs are unpredictable, and it’s important to design systems that don’t just assume success but actively plan for failure. This is where AI gateways can help with fallback mechanisms.

When and why LLMs fail

Even the most advanced language models aren’t immune to failure. When you're serving millions of requests or building mission-critical workflows, these failures are common and inevitable. Designing with this reality in mind is essential.

Some failures are straightforward. API calls to model providers may time out if the prompt is too long, the model is under heavy load, or latency spikes unexpectedly. In real-time applications, even a few seconds of delay can render a response useless, especially in chat interfaces or autocomplete use cases.

Other failures are caused by rate limits or quota constraints imposed by providers. If your app is experiencing a traffic spike and exceeds the tokens-per-minute limit, the model may reject requests outright. Similarly, if you’ve hit a usage cap or billing limit, requests will fail until the quota is reset or extended.

Language models can hallucinate, returning confident but completely incorrect answers. This isn’t a system crash or a timeout, but a breakdown in trust. If you rely on the model to generate reliable information, especially in user-facing contexts, this kind of failure can be just as damaging as a 500 error.

Sometimes, the failure is rooted in edge cases or bad inputs. Unexpected user prompts, malformed payloads, or untested logic paths can push the model into returning empty or unusable outputs. And finally, there’s always the risk of provider-side outages. Even the most reliable LLM APIs go down or degrade in performance, leaving your application without a working backend.

How to set up a fallback mechanism for your AI app?

Teams need to find a structured way to handle failures when their primary LLM call doesn’t go as planned. It’s not just a retry loop or a backup API key; it’s an intentional design pattern that ensures your application remains functional and trustworthy even when your preferred model or provider isn’t delivering.

What makes fallback systems powerful is that they’re not about avoiding failure. You define conditions under which the primary call is considered unsuccessful and preconfigure your system to make the next best move automatically.

Designing a robust fallback system for LLM applications requires more than just a backup model. It’s a layered strategy that assumes things will go wrong and ensures your system can recover without breaking the user experience. Here’s how to approach it:

First, adopt a multi-provider setup.

Relying on a single LLM provider makes your app vulnerable to outages, quota issues, and pricing changes. A multi-provider setup gives you flexibility. You can route around failures, reduce vendor lock-in, and take advantage of different models for different use cases, like using GPT-4 for complex reasoning and Claude for faster responses.

Then set up smart routing and switching. Not every request needs to go to your most powerful or expensive model. Build logic that routes requests based on task type, priority, or cost. For example, simple classification tasks might go to a smaller model, while complex summarization goes to a stronger one. You can also assign weights to providers and distribute traffic based on those weights to implement dynamic load balancing, ensuring no single provider becomes a bottleneck.

Next, implement automatic retries. Failures don’t always need fallbacks. Sometimes, just retrying a request solves the issue. Use an exponential backoff strategy to avoid overwhelming providers and infrastructure. Retry once after a short delay, then back off gradually. This helps deal with transient issues like rate limits or minor network hiccups without needing to fail over.

Retries failing? Time to fallback. As a general rule, you can consider triggering fallbacks on every 2xx response and not just hard failures. A model might return a response, but if it's malformed, empty, or fails validation, it still needs to be caught and handled. Your fallback logic should clearly define which conditions warrant switching, whether it's specific status codes, response timeouts, or content validation failures.

Set request timeouts. Don’t let your app hang waiting for a model response. Establish clear timeout thresholds for each provider. If a model takes too long, terminate the request and either retry, fallback, or return a degraded response. This is especially important for real-time use cases, where latency directly impacts user experience.

You can use canary testing when introducing new models. When you want to test a new model in production, don't switch all traffic at once. Assign a small percentage of requests to it using weighted routing. This allows you to observe real-world performance without putting your app at risk. Once you’re confident, gradually increase the traffic share.

Finally, make observability a core part of the system. Fallbacks shouldn't be invisible to you, even if they are to the user. Every retry, route switch, and fallback should be logged, along with its trigger condition and result. This lets you audit failures, measure provider performance, and refine your logic over time. Without good logging, you're flying blind.

In short, fallback logic needs to be a core part of your infrastructure. It must be intentional, observable, and as well-tested as your main model path. Done right, it makes failures feel like a non-event to your users, which is exactly the point.

Why use an AI gateway for fallback systems?

Building a reliable fallback system from scratch is operationally heavy. You need to manage multiple providers, implement routing logic, handle retries, track observability, enforce timeouts, and ensure everything works seamlessly across production traffic.

An AI gateway acts as a central control plane for all your LLM traffic. It sits between your application and your model providers, letting you orchestrate requests, implement fallbacks, and enforce policies — all without bloating your application logic or scattering responsibilities across services.

With an AI gateway, you can configure multi-provider support out of the box. Switching from one model to another no longer requires deep integration changes. You can add new providers, assign routing weights, and even A/B test models with a few configuration changes instead of code rewrites.

Fallback logic becomes easier to define and maintain. Instead of hardcoding logic into your app, you can declaratively define which error codes should trigger retries, which models to fallback to, how many attempts to make, and what thresholds to enforce for timeouts. It centralizes the rules, so you don’t have to replicate logic across different parts of your stack.

You also gain observability at the model level. The gateway can log every request, capture latency, error rates, and model-specific anomalies, and show exactly when and why a fallback was triggered. This visibility helps you continuously improve your system, without relying on scattered logs or guesswork.

Most importantly, an AI gateway helps you move fast without compromising on reliability. Whether you're onboarding a new model, testing a change in routing, or debugging a failure, the gateway gives you the tools to experiment and recover safely, without breaking your app or affecting end users.

In short, fallback logic is only as good as the infrastructure that supports it. An AI gateway turns a fragile patchwork of scripts and retries into a scalable, production-grade reliability layer for LLMs.

Best practices

Instead of writing your own retry logic, routing rules, logging layers, and provider integrations, use an AI gateway like Portkey that gives you all of this out of the box. You get built-in support for multi-provider routing, weighted load balancing, schema validation, timeouts, retries, and fallbacks, all configurable without bloating your core application code.

If reliability is a challenge in your AI apps, try it yourself on Portkey or book a demo, and we’ll walk you through it.