Every AI platform vendor will tell you they want to be your partner. What they mean is they want to be your only partner.

This is not conspiracy. It is structural. The business model of AI platform vendors creates incentives that push you toward deeper dependency. Understanding those incentives is the first step toward building an architecture that serves your interests, not theirs.

We have built AI systems on every major platform, OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, and open-source models running on custom infrastructure. We have migrated clients between platforms when the economics shifted or capabilities changed. That experience has given us a clear-eyed view of how vendor dependency develops and what you can do about it.

The Lock-In Playbook

The mechanisms of AI vendor lock-in are not new. They follow the same patterns that database vendors, cloud providers, and enterprise software companies have used for decades. But they are accelerated by the pace of AI development and disguised by the novelty of the technology.

Proprietary tooling. Every major AI platform offers tools that only work with their models. Fine-tuning interfaces, prompt management dashboards, evaluation tools, deployment pipelines. Each tool you adopt adds switching cost. The fine-tuned model on OpenAI cannot move to Anthropic. The prompt templates in Azure OpenAI’s Prompt Flow do not export to a portable format. The evaluation datasets you built in a vendor’s interface are trapped there.

The tools are genuinely useful. That is what makes them effective lock-in mechanisms. You adopt them because they save time. By the time you realize you are locked in, the migration cost is prohibitive.

Opaque pricing that discourages comparison. AI vendors price their services in tokens, but token definitions vary between providers. A token on OpenAI is not the same length as a token on Anthropic. Context windows differ. Rate limits differ. The pricing for cached tokens, batched requests, and fine-tuned models adds complexity that makes direct comparison difficult.

This opacity is not accidental. When customers cannot easily compare prices, they stay with the current vendor even when a cheaper option exists. The friction of comparison is itself a form of lock-in.

Data gravity. Once your data is embedded, indexed, and stored in a vendor’s vector database, moving it requires re-embedding everything with the new provider’s embedding model. Embeddings are not portable between models. A vector that represents a document in OpenAI’s embedding space is meaningless in Anthropic’s embedding space.

For a company with a million documents in a vendor’s vector store, migration means re-processing every document. That is weeks of compute time, significant cost, and a period where your system is degraded or offline. Most companies decide the migration is not worth it, which is exactly the outcome the vendor designed for.

Model-specific behavior tuning. Every team that builds on a specific model develops implicit knowledge about how that model behaves. What prompt structures work best. What failure modes to watch for. What workarounds address specific limitations. This knowledge is a sunk cost that does not transfer to a different model.

When you have spent six months learning the quirks of GPT-4 and building prompts that account for its specific behavior, switching to Claude means starting that learning process over. The switching cost is not just technical. It is cognitive.

Why Multi-Model Architecture Matters

The argument for multi-model architecture is not about avoiding vendors. It is about maintaining optionality in a market that changes quarterly.

Models improve at different rates. Six months ago, the best model for code generation was different from the best model today. Six months from now, it will likely be different again. If your architecture is coupled to a single model, you cannot take advantage of improvements from other providers without a significant migration effort.

Different models are better at different tasks. We see this every day in our own work. One model is better at structured data extraction. Another is better at nuanced analysis. A third is faster and cheaper for simple classification tasks. A multi-model architecture lets you use the right model for each task instead of using one model for everything and accepting mediocre performance on tasks where it is not the best option.

We built CalliopeAI specifically to solve this problem. It provides a unified interface across multiple model providers, handles routing between models based on task requirements, and manages the prompt variations that different models need. When a better model appears, you switch the routing rule. Your application code does not change.

Pricing changes without warning. AI vendors adjust pricing frequently. Sometimes prices decrease, which is great. Sometimes prices increase for specific capabilities, or rate limits change, or free tiers disappear. If your entire system depends on one provider, a pricing change is a business risk. If you can route traffic between providers, a pricing change is just a routing decision.

Availability is not guaranteed. Every major AI provider has had significant outages. If your product depends on a single provider and that provider goes down, your product goes down. Multi-model architecture gives you automatic failover. When OpenAI is slow, route to Anthropic. When Anthropic is down, route to Google. Your users never know.

Building Provider-Agnostic Systems

Provider-agnostic architecture adds complexity. The question is whether that complexity is worth the optionality it provides. In our experience, for any system that will run for more than six months, the answer is yes. Here is how to build it.

Abstraction layer for model interaction. Do not call model APIs directly from your application code. Build or use an abstraction layer that translates your application’s requests into provider-specific API calls. The abstraction should handle authentication, request formatting, response parsing, error handling, and retry logic for each provider.

This is not over-engineering. It is the same principle as using a database abstraction layer instead of writing raw SQL throughout your application. The abstraction costs a few days of development time. The flexibility it provides saves weeks when you need to switch or add a provider.

Portable prompt templates. Write prompts in a provider-agnostic format and translate them to provider-specific formats at the abstraction layer. Each model has preferences for how instructions are structured, how examples are formatted, and how system messages are distinguished from user messages. Your prompt templates should capture the intent, and the abstraction layer should handle the formatting.

This also enables A/B testing between models. Send the same logical prompt to two different models and compare the results. You cannot do this if your prompts are written for a specific model’s idiosyncrasies.

Embedding portability strategy. This is the hardest part. Embeddings are inherently model-specific. The practical approaches are:

Store documents alongside their embeddings and maintain the ability to re-embed on demand. This means keeping your raw documents accessible, not just the vectors. When you switch embedding models, you re-process your corpus during a migration window.

Alternatively, use an embedding model that you control. Open-source embedding models running on your own infrastructure give you complete portability. The quality is competitive with commercial offerings for most use cases, and you eliminate the dependency entirely.

Evaluation framework that is model-independent. Your evaluation metrics should measure the quality of the output, not the specifics of the model that produced it. This lets you evaluate any model against the same criteria and make switching decisions based on data.

If your evaluation framework tests for GPT-4-specific output formats, you cannot use it to evaluate Claude or Gemini. Build evaluations that test for the properties you care about, accuracy, completeness, tone, format, regardless of which model produced the output.

The Counter-Arguments

There are legitimate reasons to commit to a single vendor. Here are the strongest ones and our responses.

“Single vendor reduces complexity.” True. Multi-model architecture is more complex than single-model architecture. The question is whether the complexity of managing multiple providers is greater than the risk of dependency on one. For small projects with a six-month horizon, single vendor is fine. For production systems that will run for years, the complexity is worth it.

“We get better support and pricing with commitment.” Also true. Vendors offer volume discounts and dedicated support for committed customers. But these incentives are designed to increase switching costs. Calculate the discount against the potential cost of being locked in when a better option appears or when the vendor changes terms.

“Our team knows this model well.” Model-specific expertise is valuable, but it is also a form of lock-in. Invest in general AI engineering skills, prompt engineering principles, and evaluation methodology, which transfer between models, rather than model-specific tricks that become worthless when you switch.

“The switching cost is too high.” If the switching cost is already too high, that is evidence that you should have built a provider-agnostic architecture from the start. The cost of building the abstraction layer is a fraction of the cost of a forced migration later.

Practical Migration Path

If you are already locked into a single vendor, here is a realistic path toward provider independence.

Phase 1: Introduce the abstraction layer. Wrap your existing model calls in an abstraction that currently only supports your current provider. This is a refactoring exercise that does not change functionality. It just reorganizes the code to prepare for future flexibility.

Phase 2: Add a second provider for non-critical tasks. Pick a low-risk use case, like summarization or classification, and implement it on a second provider through your abstraction layer. Run both in parallel and compare results. This validates your abstraction and builds team familiarity with a second provider.

Phase 3: Implement routing logic. Add the ability to route requests between providers based on cost, latency, capability, or availability. Start with simple rules: use Provider A for task X, Provider B for task Y. Evolve toward dynamic routing based on real-time performance data.

Phase 4: Address data portability. If you have data locked in a vendor’s vector store, plan the migration to a portable solution. This is the most expensive phase but also the most important for long-term independence.

Phase 5: Continuous evaluation. Once you can route between providers, maintain a continuous evaluation pipeline that tests each provider’s performance on your specific use cases. This gives you the data to make informed routing decisions and to negotiate with vendors from a position of knowledge.

The Strategic Principle

The vendors building AI platforms are building excellent technology. We use their models every day. The problem is not the technology. It is the business model that incentivizes dependency.

Your architecture should reflect your interests, not your vendor’s. That means maintaining the ability to switch providers, use multiple providers simultaneously, and evaluate new options as they appear. The cost of building this flexibility is real but bounded. The cost of being locked into a vendor whose incentives do not align with yours is unbounded and unpredictable.

Build for optionality. Use the best tool for each job. And remember that the vendor telling you to commit is the same vendor who benefits from your commitment. Their advice is not impartial. Your architecture should be.