Enterprise AI: Build or Buy?
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If you’re building AI into your enterprise right now, there’s a good chance you’re making a decision that will haunt you in eighteen months. Not because you chose the wrong model. Because you chose a model. Singular. And built everything around it.
The AI landscape is moving faster than any technology cycle most of us have lived through. The model that is state of the art today will be outperformed by something new in six months. The vendor you bet on might change their pricing, their terms, or their entire strategic direction. The capabilities you need might emerge from a provider you have never heard of.
LLM agnostic architecture is not about hedging your bets. It is about building AI systems that can evolve as fast as the technology underneath them. Here is why it matters and what it actually looks like in practice.
Let’s start with the basics. LLM agnostic means your AI system is not hardwired to a specific large language model or provider. Instead of building directly on top of OpenAI's API or Anthropic's Claude or Google's Gemini, you build with an abstraction layer that lets you swap models without rebuilding your application.
Think of it like building a house with standard electrical outlets instead of wiring everything directly to one specific appliance brand. When a better appliance comes along, you just plug it in. You do not have to rewire the walls.
In practical terms, this means your prompts, your data pipelines, your integration logic, and your user interfaces are not dependent on the specific quirks and capabilities of one model. The model becomes a component you can change, not a foundation you’re stuck with.
This sounds obvious when you say it out loud. But look at most enterprise AI deployments happening right now and you will find tight coupling everywhere. Custom prompts tuned to GPT 4's specific behavior. Workflows designed around Claude's context window. Integrations built on one vendor's embedding model. Every one of those decisions creates technical debt that will slow you down later.
Vendor lock in is not a new concept. Anyone who has been through an enterprise software migration knows the pain. But AI lock in is different in ways that make it more dangerous. Let’s check out a few reasons why.
The switching costs are architectural, not just contractual. With traditional SaaS, switching vendors means migrating data and retraining users. Painful, but straightforward. With AI, switching models can mean rewriting prompts, retuning outputs, rebuilding evaluation pipelines, and revalidating that the new model actually works for your use cases. If you have fine tuned a model on your data, that work may not transfer at all.
Pricing is unpredictable and changing constantly. OpenAI has changed their pricing structure multiple times. Anthropic and Google are still figuring out their models. The economics that made sense when you started your project might not make sense at scale. Organizations locked into a single provider have no leverage to negotiate and no alternative to switch to.
Terms of service can shift underneath you. How your data is used, what gets logged, what training rights the provider reserves. These terms change. If you’re locked in, you accept the changes or you start over. Neither option is good.
Single points of failure are real. Every major AI provider has had outages. If your entire operation depends on one API and that API goes down, so do you. An LLM agnostic architecture gives you the option to fail over to alternative models when your primary provider has issues.
Here is an uncomfortable truth. Nobody knows which model will be leading in two years. Including the people building the models.
Think about the pace of change we have seen. GPT 3 was impressive. GPT 4 was dramatically better. Claude went from interesting to enterprise competitive in about eighteen months. Open source models like Llama and Mistral have closed the gap faster than anyone predicted. New architectures, new training approaches, and new capabilities are emerging constantly.
The model you choose today is not the model you will want in 2026. Maybe a new entrant will offer better performance at lower cost. Maybe an open source model will become good enough that running it yourself makes economic sense. Maybe a specialized model will emerge that is dramatically better for your specific use case than any general purpose model.
Organizations that build for a single model are betting that the current landscape will not change. That is not a bet I would take.
The smart approach is to build systems that can take advantage of improvements as they happen. When a better model emerges, you want to be able to adopt it in days, not months. That only happens if you have built for flexibility from the start.
Here is something that gets lost in the "which model is best" debates. Different models are better at different things. And the optimal AI system often uses multiple models, each chosen for specific tasks.
A large, expensive model might be overkill for simple classification tasks but essential for nuanced reasoning. A fast, cheap model might be perfect for high volume, straightforward queries but fall short on complex analysis. A specialized model trained for code might outperform a general model on development tasks even if it is worse at everything else.
An LLM agnostic architecture lets you match models to tasks. Use the heavyweight model where you need its capabilities. Use lighter models where speed and cost matter more than maximum intelligence. Route different types of queries to different models based on what each one does best.
This is not theoretical optimization. Organizations running AI at scale are already doing this. They are using cheaper models for embeddings and retrieval, reserving expensive models for generation. They are routing simple questions to fast models and complex questions to capable ones. They are using specialized models for specific domains where those models have an edge.
If your architecture only supports one model, you cannot do any of this. You’re paying premium prices for every query, whether it needs premium capabilities or not.
So what does it actually take to build AI systems that are model flexible? The key is separating the layers of your stack so that the model is a pluggable component, not a load bearing wall. Check out how much this entails below:
Abstracted model interfaces. Your application code should not call OpenAI or Anthropic directly. It should call a model interface that can route to any provider. This abstraction layer handles the differences in API formats, authentication, and response structures so your application does not have to know which model it is talking to.
Portable prompts. Prompts should be designed to work across models, not tuned to one model's specific behaviors. This means avoiding tricks that only work with certain models and building prompts that express intent clearly enough that any capable model can follow them. It also means having evaluation frameworks that test prompts against multiple models before deployment.
Model agnostic data pipelines. The way you connect to your enterprise data, the way you chunk and embed documents, the way you build retrieval systems. All of this should work regardless of which model you use for generation. Your data layer is an investment that should outlast any individual model choice.
Unified governance. Access controls, audit logging, and compliance frameworks should work consistently regardless of which model handles a query. You should not have to rebuild your governance and security controls every time you change models.
Building this from scratch is significant work. Which is why most organizations should not try to do it themselves.
We built Unframe to be LLM agnostic from day one because we saw this problem coming. Locking customers into one model would have been easier to build. It also would have been the wrong choice for everyone involved.
Multiple models, one platform. Unframe is fully model-agnostic. You can use any foundation model - open-source or proprietary - such as Anthropic or Gemini, or bring your own fine-tuned and internal models, including customer-owned domain-specific language models (DSLMs) and specialized models for legal, finance, operations, IT, and other regulated functions.
Deploys wherever you need it. Because Unframe deploys in your environment, you’re not dependent on our infrastructure choices. If you want to run open source models on your own hardware, you can. If you want to use a specific provider for compliance reasons, you can. The deployment model is as flexible as the models themselves.
Pre-built building blocks that work across models. Our AI capabilities for search, extraction, workflows, and agents are designed to work with any supported model. You get the benefits of pre-built functionality without being locked into one provider's ecosystem.
Pricing that does not penalize flexibility. Our outcome based pricing means you pay for results, not for which model generates them. Using a more efficient model for appropriate tasks saves you money. Using a more capable model when you need it does not break your budget. The economics work regardless of which models you choose.
The goal is simple. Give you all the benefits of AI without forcing you to bet your architecture on decisions that might not age well. The models will keep getting better. Your AI system should be able to take advantage of that without starting over.
The AI landscape will look different in two years. Different models. Different capabilities. Different economics. The only thing we can say with confidence is that the status quo will not hold.
Building LLM agnostic systems is how you prepare for that uncertainty. It is not about being indecisive or avoiding commitment. It is about making smart architectural choices that preserve your options as the technology evolves.
If you’re thinking through your AI architecture and want to avoid the lock in trap, let us talk. We have helped organizations across industries build AI systems that are flexible enough to evolve with the technology, not fight against it.
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