Picture this: your customer service platform is running perfectly. Servers are online, the database responds instantly, and your network is stable. Yet your agents can't help customers. The reason? The AI language model powering your smart chatbot is unreachable. No error in your own monitoring, no alert from your hosting provider — but your application isn't functioning.
This isn't a hypothetical scenario. On February 11, 2026, Anthropic's status dashboard — makers of Claude — registered yet another incident with elevated error rates. Claude.ai had an uptime of 99.32% over the past 30 days. That sounds high, but translates to nearly 5 hours of downtime per month. For business-critical applications, that's significant.
At Universal.cloud, we see organisations integrating AI into their core processes every day. From automatic document processing to intelligent customer interactions, from compliance checks to code analysis. The dependency is growing exponentially, but availability policies lag behind. In this blog, we share our perspective on a challenge every company with AI ambitions must address.
AI models as infrastructure components
Traditionally, your application infrastructure consisted of familiar components: compute, storage, networking, and databases. Each has a mature ecosystem of monitoring, redundancy, and SLA agreements. When you run a virtual machine on Azure, AWS, or Google Cloud, you get an SLA of 99.9% or higher. Your database has failover, your storage has replication, and your network has multiple paths.
With AI language models entering the application landscape, a fundamentally new dependency has emerged. An LLM API (Large Language Model — the type of AI behind tools like ChatGPT and Claude) is no longer a nice-to-have — it's an essential component in the chain. If the model isn't available, your application doesn't work, regardless of how robust the rest of your infrastructure is.
Yet many organisations treat their LLM integration differently from other infrastructure components. No failover is configured, no SLA demanded from the model provider, and basic availability monitoring is often missing entirely. That's comparable to running your production database without backups — it works until it doesn't.
The uptime reality of major model providers
Let's be honest: no cloud provider or AI vendor delivers 100% uptime. But the differences are significant, and transparency varies enormously.
| Provider / Service | Measured uptime | Published SLA | Note |
|---|---|---|---|
| Claude API (Anthropic) | 99.42% | Not published | No formal SLA available |
| Claude.ai | 99.32% | Not published | Consumer product |
| OpenAI API | ~99.5% | 99.9% (Enterprise) | SLA only for Enterprise tier |
| Azure OpenAI | ~99.9% | 99.9% | Microsoft SLA framework |
| Google Vertex AI | ~99.9% | 99.9% | Google Cloud SLA |
| Amazon Bedrock | ~99.9% | 99.9% | AWS SLA framework |
What stands out: the measured uptime figures for AI model providers are consistently lower than what we're used to from traditional cloud infrastructure. An SLA of 99.9% for an Azure VM means a maximum of 43 minutes of downtime per month. A measured uptime of 99.3% for an LLM API means over 5 hours. That's a factor 7 difference that many organisations don't account for in their risk analysis.
Moreover, the nature of the downtime is different. Traditional infrastructure issues are often local and predictable — a region has an outage, a specific service is temporarily unreachable. AI model downtime can be global, without geographic failover capabilities, affecting all users simultaneously.
The strategic choice: three models
At Universal.cloud, we regularly discuss the right strategy with our clients. The conversation centres around three fundamental approaches, each with its own pros and cons.
1. Multi-provider with automatic failover
This is the approach we at Universal.cloud most frequently recommend for business-critical applications. The principle is simple: you integrate multiple LLM providers into your application and configure automatic failover when the primary provider is unavailable.
Concretely, this means your application automatically switches to OpenAI's GPT-4, Google's Gemini, or another model during a Claude API outage. The technical implementation requires an abstraction layer that translates model-specific API calls, but the investment pays off in availability.
- Pros — Highest availability by eliminating single point of failure. No own hardware required. Access to the latest models from each provider. Pay only for actual usage.
- Cons — Higher complexity in the application layer. Subtle differences in model behaviour may affect user experience. You depend on multiple external parties. Potentially higher costs from maintaining multiple integrations.
- Best for — Organisations requiring maximum availability without investing in their own infrastructure. Applications where slight variations in model output are acceptable.
2. Self-hosting with a major cloud provider
A growing number of organisations are considering running open-source models like Llama, Mistral, or Qwen on GPU infrastructure at Amazon (AWS), Google Cloud, or Microsoft Azure. This provides more control over availability but introduces new challenges.
The major cloud providers now offer specialised GPU instances (Azure NC series, AWS P5, Google A3) and managed inference platforms (Azure AI Model Catalog, Amazon Bedrock, Google Vertex AI). You can choose between fully self-managing on bare-metal GPUs or using managed platforms that take away part of the operational burden.
- Pros — Full control over availability and scalability. Data doesn't leave your own cloud environment. Predictable costs with consistent usage. Ability to fine-tune on your own data. Cloud provider SLA applies to the underlying infrastructure.
- Cons — High initial costs for GPU infrastructure (often €10,000+ per month). Operational complexity of model deployment and management. You're responsible for updates, patching, and optimisation. Open-source models don't always perform at the level of frontier models like Claude or GPT-4o.
- Best for — Organisations with strict data residency requirements, high and predictable usage, and the technical capacity to manage AI infrastructure. Think financial institutions, healthcare organisations, and government agencies.
3. Self-hosting in your own data centre
The most far-reaching option is running models on your own hardware in a private or leased data centre. We see this primarily with organisations that have extreme compliance requirements or those that consider AI a strategic differentiator.
- Pros — Maximum control over data and privacy. No dependency on external parties. Potentially lower costs at very high volume long-term. Complete freedom in configuration and optimisation.
- Cons — Enormous capital investment in GPU hardware (NVIDIA H100/H200 cards cost €25,000–40,000 each). Power consumption and cooling are significant. You need specialised personnel for management. Hardware becomes outdated quickly at the current pace of AI development. Availability is entirely your own responsibility.
- Best for — Large enterprises with dedicated AI teams, organisations in regulated sectors with strict air-gapped requirements, and companies where AI is the core product.
Our recommendation: the layered approach
At Universal.cloud, we apply a layered strategy for our own applications and client advisory that we call the "AI Resilience Stack":
- Multi-provider integration — Integrate at least two LLM providers into your application architecture. Define a primary model and one or more fallback models. Regularly test whether failover actually works.
- Abstraction layer — Build an abstraction layer that hides model-specific details from the rest of your application. This makes switching between providers trivial and protects you against vendor lock-in.
- Monitoring and alerting — Monitor the availability of your LLM endpoints as seriously as your database or web server. Implement health checks, measure latency, and set up alerts for degradation.
- SLA calculation — Include AI availability in your SLA calculations. The total availability of your application is the product of all components. An LLM endpoint with 99.3% uptime drags down the availability of your entire chain.
- Graceful degradation — Design graceful degradation. What does your application do when no AI model is available? The best applications fall back to basic functionality instead of stopping entirely.
The SLA puzzle: do the maths
A common mistake is ignoring the cumulative impact of multiple dependencies on your total SLA. The maths is simple but unforgiving:
| Component | Uptime | Downtime/month |
|---|---|---|
| Azure App Service | 99.95% | ~22 min |
| Azure SQL Database | 99.99% | ~4 min |
| Azure Networking | 99.99% | ~4 min |
| LLM API (single provider) | 99.30% | ~5 hrs 6 min |
| Total (without AI) | 99.93% | ~30 min |
| Total (with AI) | 99.25% | ~5 hrs 28 min |
By adding an LLM provider to your chain without factoring in its availability, you're promising customers an SLA you can't deliver. In the example above, adding a single AI model drops your availability from 99.95% to 99.25% — a fourfold increase in expected downtime.
With a multi-provider failover strategy, you improve this dramatically. If you have two independent LLM providers each with 99.3% uptime, the probability of both being down simultaneously is only 0.0049%, raising your effective AI uptime to 99.995%.
Conclusion: AI uptime is no longer an afterthought
We're at the beginning of an era where AI becomes an essential part of the application landscape. Just as we learned to manage cloud infrastructure availability twenty years ago, we must now apply the same discipline to AI models.
The question isn't *whether* AI models will be unreachable — but *when*, and how well prepared you are. Organisations that invest now in a robust AI architecture with multi-provider failover, adequate monitoring, and realistic SLA calculations will have a significant competitive advantage over those that treat AI as a simple API call that "always works".
At Universal.cloud, we help organisations design and implement AI-resilient architectures. Whether you want to strengthen an existing application or build a new AI-driven platform — the availability of your AI components deserves the same attention as any other business-critical infrastructure.
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Want to discuss your AI strategy? Contact Universal.cloud for a no-obligation conversation about integrating AI availability into your existing SLA policy and architecture. Get in touch.
