What is AI vendor dependence and why is it a risk?
Single-provider dependence is when a business relies on one AI provider for its core operations — making it vulnerable to price increases, service changes, and decisions it cannot control. When one provider controls your AI, they also control your costs, your data access, and your ability to switch. Businesses that tied their operations to one cloud provider in the 2010s learned this lesson the hard way. AI is following the same pattern. The risk is not theoretical: providers change pricing, deprecate models, and alter terms. A business that cannot switch is a business that cannot negotiate. This dependence is no longer a fringe concern: enterprise AI spending has moved out of experimental budgets and into permanent ones — the share funded from experimental "innovation" budgets fell from about a quarter to just 7% in a single year, with the rest now sitting in core IT and business-unit lines (Andreessen Horowitz enterprise survey, 2025). In other words, the AI a business runs on has become core infrastructure — and infrastructure you cannot change is infrastructure that controls you. As Klaara co-founder and CEO Van Anh Do put it: “AI independence is not about models. It is about ensuring that your business future is not determined by someone else’s roadmap.” (Van Anh Do, 2026).
How can a business use AI without its data leaving the company?
Your data stays under your control when you choose where the AI runs — and what is ever shared. With Klaara you decide the setup: your own servers, your own cloud, or a private deployment, so sensitive work stays inside your environment. When a task uses one of the big AI models, you choose what is shared with it — and see exactly what was sent. That is the principle Klaara is built on.
What is a multi-model AI platform?
A multi-model AI platform lets a business use the best AI for every task, from any provider, without being locked into one. Instead of committing to a single AI system, you can use different AI capabilities for different jobs — and switch whenever a better option becomes available. Think of it like having a team of specialists rather than one generalist: you choose the right tool for each task. A multi-model platform also means that if one provider raises prices or changes its terms, you can move to another without rebuilding your entire operation. This is already how sophisticated organizations operate: in a 2025 survey of enterprise AI leaders by the technology investment firm Andreessen Horowitz, 37% of companies reported using five or more models in production, up from 29% a year earlier — driven both by avoiding vendor lock-in and by the fact that different models are genuinely better at different tasks (Andreessen Horowitz enterprise survey, 2025).
How do I keep control of AI decisions — and prove how each one was made?
You keep control of AI decisions when you can see how every one was made — what the AI decided, why, and who approved it. This means you can answer the question "how was this decision made?" for any AI output in your business. This matters for compliance, for customer trust, and for your own confidence. A platform with proper oversight lets you set rules for when AI acts automatically and when a person must review the decision first. People stay in charge; AI handles the work.
How is Klaara different from building AI on top of the big AI models myself?
Klaara is the difference between a proof of concept and a system you can run a serious, regulated business on. You can start any one of these pieces yourself — but a basic version of one is a proof of concept, not a business you can depend on. Klaara already gives you each one well past that baseline, as one platform you own: model freedom — cloud or your own hardware, even spread across your own machines, switchable without a migration; AI that works from your own knowledge and shows you where every fact came from; multi-step work that runs automatically with a person in the loop where you want one; and the controls your industry's rules demand, from day one. Each of those is hard on its own. Making them work together — safely, and still running months later — is harder still. It is in production today. That is what Klaara is.