How to Build Redundancy for AI Models: A Practical Guide

The SLA problem nobody is talking about

Before we get into solutions, it's worth understanding just how exposed most organisations are right now. Anthropic's uptime commitments vary dramatically by plan:

The good news: building AI redundancy is entirely achievable and does not require a massive engineering effort. The key is understanding the architectural patterns available and choosing the right approach for your organisation.

What is an AI proxy server?

Before diving into the tools, it helps to see the problem visually. Here's the difference between a non-redundant and redundant AI architecture:

An AI proxy server (also called an AI gateway) sits between your application and the AI providers it calls. Instead of your application talking directly to Claude's API, it talks to the proxy — which then routes the request to the appropriate AI provider based on availability, cost, or performance.

Think of it like a load balancer, but for AI models. When your primary provider goes down, the proxy automatically routes requests to your fallback provider — transparently, without your application code needing to change.

A proxy typically handles:

• Routing — directing requests to the right provider based on rules you define
• Failover — automatically switching to a backup provider when the primary fails
• Load balancing — distributing requests across providers to avoid rate limits
• Caching — storing responses for repeated queries to reduce cost and latency
• Logging and monitoring — tracking usage, costs, and performance across all providers
• Authentication — managing API keys centrally rather than scattered across applications

Open source and commercial AI proxy tools

Rather than building a proxy from scratch, the vast majority of organisations are better served by an existing open-source or commercial AI gateway. These tools are production-hardened, actively maintained, and can be deployed in days rather than months. A good proxy will handle the key capabilities you need:

• Automatic failover — switching to a backup provider when the primary fails, without your application noticing
• Health checks — proactively detecting provider issues before your users do
• Request queuing — holding requests during outages and processing them when service resumes
• Response caching — serving cached responses for repeated queries to reduce cost and latency
• Unified logging — tracking usage, cost, and performance across all providers in one place
• Circuit breaker pattern — stopping requests to a failed provider during a cool-down period rather than hammering it repeatedly
• API key management — centralising credentials rather than scattering them across applications

Here are the leading options available today:

Choosing the right redundancy strategy

The right approach depends on your organisation's size, technical capability, and risk tolerance. Here's a framework for choosing:

This isn't just an Anthropic problem — it's industry-wide

To be clear: AI provider outages are not unique to Anthropic. Every major LLM provider has experienced significant downtime in 2026. This is a structural characteristic of the AI industry at its current maturity level — not a failing of any single company.

This is precisely why the choice of which provider to use as your primary is less important than ensuring you have a tested fallback to a different provider on different infrastructure. When outages are this frequent across the entire industry, single-provider dependency is simply not a defensible architectural decision for any business-critical AI workload.

This is a question every architect should ask — and most don't. Routing all your AI traffic through AWS Bedrock, Azure AI Foundry, or Google Vertex AI solves the single-provider problem, but potentially introduces a new one: single-cloud platform dependency.

The good news is that AWS Bedrock is not simply a single-region service. AWS offers a feature called Cross-Region Inference — which automatically routes Bedrock requests to an available region if your primary region is degraded or unavailable. This provides meaningful resilience against the most common failure mode: a single AWS region going down.

However, Cross-Region Inference has limits. It routes across AWS regions — it does not protect against a broader AWS-wide incident. These are rare, but they do happen. In December 2021, a major AWS us-east-1 outage cascaded and affected services across multiple regions simultaneously. In those scenarios, all Bedrock endpoints would be affected regardless of cross-region routing.

Matching your redundancy to your risk appetite

The right level of redundancy depends on how critical AI is to your operations:

• Important but not critical — Bedrock with Cross-Region Inference is sufficient. The probability of an AWS-wide incident is very low.
• Critical — Bedrock with Cross-Region Inference as primary, plus a direct API fallback to a provider bypassing Bedrock, giving you a path that doesn't depend on AWS at all.
• Mission-critical — Full cross-cloud diversity across two or more cloud platforms, each with independent AI gateways.

True cross-cloud redundancy

For organisations where AI is genuinely mission-critical, the architecture that provides the highest level of protection looks like this:

Each layer sits on completely independent infrastructure, so a single cloud outage cannot cascade through the entire stack.

The pragmatic middle ground for most organisations

Full cross-cloud redundancy is complex and expensive to build and maintain — realistic only for large enterprises with significant engineering resource. For most organisations, the pragmatic approach that balances resilience with manageability is:

• Primary: AWS Bedrock with Cross-Region Inference enabled — provides strong resilience against regional failures, which are the most common cause of outages
• Secondary: Direct API calls to one or two providers, bypassing the cloud platform entirely — so a broader AWS incident doesn't take your fallback with it
• Abstraction layer: LiteLLM or similar sits in front of both, so switching between Bedrock and a direct API call is a configuration change, not a code change

Building a multi-provider failover strategy

Regardless of which tools you choose, a sound multi-provider failover strategy follows these principles:

Cost considerations

Multi-provider redundancy does add cost — but less than you might think, and far less than the cost of downtime.

• Dual API keys — maintaining accounts with two providers costs nothing if you're only paying per token used
• Proxy infrastructure — LiteLLM self-hosted runs comfortably on a small VM costing ~$20-50/month
• Managed gateways — Portkey and similar services typically cost $50-200/month for most usage levels
• Cloud platforms — AWS Bedrock, Azure AI Foundry, and Vertex AI add a small markup (typically 10-20%) on top of base model costs in exchange for the managed reliability

What about open-source models as a fallback?

For organisations that want the ultimate fallback — one that doesn't depend on any external provider — running an open-source model locally or on your own infrastructure is worth considering.

Models like Llama 3.1, Mistral, and Qwen can run on commodity GPU hardware and provide a genuine zero-dependency fallback. They won't match frontier models on complex tasks, but for many operational workflows they're entirely sufficient.

This approach makes particular sense for:

• Organisations with strict data residency requirements
• Financial services and healthcare where external API calls raise compliance concerns
• High-volume workloads where the economics of per-token pricing become significant
• Organisations that classify AI as genuinely mission-critical and need maximum resilience

The governance layer

Redundancy isn't just a technical problem — it's a governance one. Your proxy or gateway is also the right place to enforce organisational AI policies:

• Data classification — block requests containing PII or confidential data from being sent to external providers
• Content filtering — enforce acceptable use policies at the gateway layer rather than relying on each application to implement them
• Cost controls — set spend limits per team, per application, or per time period
• Audit logging — log every request and response for compliance and incident investigation
• Access control — manage which teams and applications can call which models

A well-configured AI gateway isn't just a reliability tool — it's a central point of control for your entire AI estate. This is what mature AI governance looks like in practice.

Getting started today

If you're currently calling an AI provider's API directly with no proxy or fallback in place, here's the minimum viable action plan:

1. This week — sign up for a second AI provider (OpenAI if you're using Claude, or vice versa). Cost: zero until you use it.
2. This month — implement a basic failover wrapper around your most critical AI calls. Even a simple try/except that switches providers is infinitely better than nothing.
3. This quarter — evaluate LiteLLM or a managed gateway for your use case and migrate your AI calls through it. Add independent monitoring.
4. Ongoing — run quarterly failover drills. Review your provider hierarchy as the model landscape evolves.

Also download our free guide — AI-First Without the Risk — for the complete 8-point framework for AI business continuity, including how to classify your AI dependencies and build your incident response plan.

Download the free guide →