Over the past two days, Anthropic’s Claude service has experienced multiple service disruptions. By the time your read this article, I am confident those outages will be resolved, just as similar incidents at OpenAI, Google, Microsoft Azure, AWS and virtually every major cloud provider eventually are. That isn’t really the story. The story is that many enterprises are building their AI strategy as though choosing a language model is the same thing as building an AI platform. Those are very different decisions.
When your business depends on AI to process invoices, capture trades, dispatch fuel, monitor compliance, support customers, or operate critical workflows, your architecture cannot assume that one provider will always be available. Recent outages are simply reminders of something infrastructure engineers have understood for decades: every component eventually fails. The question is not whether it will fail, but whether your business fails with it.
That is why, at NewTide, we made a deliberate architectural decision from the beginning. RisingTide was never designed around a single model provider. It was designed around the idea that the underlying models will continuously evolve, improve, compete, and occasionally become unavailable. Our platform was built to treat large language models as interchangeable components within a broader enterprise architecture rather than as the architecture itself. This distinction matters more today than it did a year ago.
Every frontier model has strengths. Claude has become exceptionally strong at long-form reasoning and software development. GPT models excel across a wide range of enterprise tasks. Gemini offers advantages for organizations deeply invested in Google’s ecosystem. Open-weight models like Llama, Mistral, and DeepSeek continue improving at remarkable speed while enabling entirely different deployment options. Tomorrow another provider may produce the industry’s best reasoning model, coding model, multilingual model, or lowest-cost inference engine. History suggests they all will trade places, more than once.
If your enterprise has hardwired itself to one provider, every breakthrough elsewhere becomes expensive to adopt. Every pricing change becomes your pricing change. Every outage becomes your outage. Every strategic decision made by someone else’s executive team becomes your operational constraint. That is vendor lock-in wearing modern clothes.
Enterprise technology leaders solved this problem years ago in other domains. We build redundant network paths. We replicate databases. We distribute workloads across availability zones. We deploy backup power. We avoid single points of failure because resilience matters more than theoretical perfection. AI infrastructure deserves exactly the same engineering discipline. The role of an enterprise AI platform is not to choose one model forever. Its role is to route the right workload to the right model at the right time while preserving governance, security, auditability, and operational consistency.
- Sometimes the best answer is the fastest model.
- Sometimes it is the least expensive.
- Sometimes it is the model with the strongest reasoning.
- Sometimes it is the one that is actually available.
Those decisions should become operational policies, not architectural rewrites. This is one of the design principles behind RisingTide. Our platform provides a secure enterprise foundation where organizations retain control of their proprietary data, govern how AI agents operate, and maintain the flexibility to leverage hundreds of models from leading providers as business needs evolve. Data Helm, Agent Harbor, and Shipyard provide the enterprise infrastructure that remains stable even as the model ecosystem changes beneath it. Models improve. Providers compete. Your platform should not have to be rebuilt every time that happens.
There is another benefit that becomes increasingly important as AI economics mature, cost optimization. Some model pricing is falling rapidly, while others are more expensive than anything we have seen before. Performance is improving almost monthly. Specialized models are emerging for reasoning, coding, vision, scientific analysis, and domain-specific tasks. Organizations that preserve model optionality will continuously optimize both capability and cost. Organizations locked into a single provider will optimize neither.
The companies that built cloud-native applications twenty years ago understood they were investing in abstraction rather than hardware. Today’s AI leaders should think the same way. Your competitive advantage should come from your data, your workflows, your institutional knowledge, and your business processes—not from whichever model happened to be leading the benchmark leaderboard this quarter.
Claude’s recent outages are not an indictment of Anthropic. They are a reminder that even exceptional technology providers operate complex distributed systems that occasionally fail. The same is true for every major cloud platform. The real lesson is much broader. Don’t build your enterprise around an LLM. Build it around a platform that can intelligently use them all.
