With AI being one of the hottest and most discussed emerging technologies nowadays, countless businesses are looking to profit from it - some by developing it, while others by financing developers. However, while most companies think they are “investing in AI capabilities,” the truth is that they are building fragile, vendor-controlled cost bombs.
These machines end up becoming more expensive and less flexible with each new quarter, and it is a pattern that every enterprise veteran has seen time and time again, whenever a new technology emerges and goes through a hype cycle. Firms smell opportunities, and they rush in, buy whatever looks interesting, then discover the fine print a bit too late to back out without major costs.
The AI boom is similar, and it has fueled an entire ecosystem of so-called “AI wrappers” - tools that look sophisticated at first, but are essentially just modified front-ends acting as a face of someone else’s model that operates under the hood. They may look like real systems, but in reality, they represent a point of failure. So, while executives claim they are helping transform the industry, in reality, they are dooming their business by letting it sink into a trap that will take them a decade to recover from.
Why This Mistake Is So Expensive?
Despite this mistake seeming quite obvious on the surface, companies tend to repeat the same errors every time a cycle starts. The reason why they keep walking into the same trap is the pressure to look like an AI company, which is often enough to outweigh the discipline required to build an AI firm from scratch.
AI companies are hot nowadays, and when they see it, boards want to get in on the action quickly. They want flashy demos for investors who want AI narratives, while executives want to signal that they are innovative, and going after a major LLM vendor is the fastest route.
The real issue is the model-centric thinking rather than system-centric thinking. Company leaders tend to obsess over which model to use instead of designing a system that can switch between models seamlessly and without tearing the entire structure apart.
Beyond that, many overly rely on convenience APIs, such as OpenAI, Claude, etc, which all offer simple onboarding that hides long-term architectural issues.
Finally, organisations and companies often don’t understand the real infrastructure needs, underestimating the costs of retrieval systems, GPU interface, vector storage, and other similar needs that come when they limit themselves to a single model.
The Core Error: LLM Vendor Lock-In (and How it Happens)
Vendor lock-in doesn’t come with alarms and flashing lights, but rather, it happens when a company builds an AI stack around a single provider’s API. That API becomes a single point of failure, so any outage, pricing change, and alike, can severely affect the entire system.
The problems also tend to emerge because every major model has ties to cloud technology, such as ChatGPT depending on Azure, Claude on AWS, and Gemini on GCP, to name a few. Once integrated, it is next to impossible to switch, and doing so would require changing the entire architecture, rather than a minor tweak, which is both time-consuming and extremely expensive.
Beyond these major structural issues, there are more subtle lock-ins, such as data format lock-ins. Data formatted for one vendor can rarely, if ever, be applied cleanly to another. Custom fine-tunes also lock companies to closed weights, while wrappers - thin layers of UI and workflow logic - usually rely entirely on someone else’s model.
From a strategic standpoint, vendor lock-in brings severe damage to a business, making companies unable to adopt other, better models, they lose negotiation leverage and are forced to follow the vendor’s roadmap, whether or not it aligns with the company itself or its customers.
Hidden Cost Breakdown (The Real TCO, Not the Monthly API Bill)
Most companies make the mistake of thinking that the cost of implementing AI comes down to their monthly API bill, which is just not the case. API spending is just the tip of the iceberg, as the total costs include token usage volatility, latency, and rate-limit penalties, not to mention business disruptions that might arise from the API going down.
Then, there are the issues with the long-term architecture-related decisions that compound cost. For example, if a company builds single-model pipelines, they will have to fully rebuild them every 18-24 months as new models emerge, and they have to change everything to re-implement them. Closed-model fine-tunes also end up being sunk costs because they can’t be ported. Monolithic AI features also tend to result in weak systems that are unable to be updated easily.
Then, there are hidden infrastructure costs that present themselves much later, such as vector databases, data preprocessing pipelines, GPU inference, retrieval systems, and orchestration layers. None of this will be included in the API cost, but all of it will affect a firm’s balance sheet. When firms end up tied to a single cloud and unable to change it, they can also expect egress fees, proprietary SDKs, proprietary embeddings, and the need to rebuild the entire pipelines if they ever decide to migrate.
Depreciation games by hyperscalers also tend to negatively affect firms, as hyperscalers artificially extend depreciation cycles to hide the real cost of GPUs and infrastructure, and make it seem like they offer low-cost AI, thus distorting enterprise buyers’ expectations and understanding of long-term expenses. On top of that, there is the illusion that front-end wrappers are IP. which is why 73% of so-called “AI startups” are front-end wrappers over OpenAI, just pretending to be proprietary technology.
What this all amounts to is buyers walking away thinking they are investing in a new IP when really, they are just renting some existing model with a fresh face on it.
Better Alternatives (Modern, Model-Agnostic AI Architecture)
So, what can businesses that want to join the AI trend do? How can they approach it the right way and avoid the $1 billion vendor trap?
The solution is not to get a different model, but rather an architecture that doesn’t care which model is being used. Companies that get this right are the ones that shift from depending on a single provider to being model-agnostic and capable of using any model as long as it fits their needs. In other words, if their system can swap models, clouds, and infrastructure without having a complete do-over each time, they can efficiently switch between them as they please.
Simply put, with multi-model architecture, businesses don’t have to route every task to one LLM, but can use a model router that selects the best engine for each job. They can also stay flexible by blending open-source and closed-source models, where open source gives portability and local inference for predictable workloads.
Beyond that, businesses should consider using a multi-cloud or cloud-agnostic setup, which can help them avoid getting stuck with a single model, as mentioned. Using Terraform, containers, and Kubernetes means that computing, storage, and inference backends become interchangeable parts, rather than limitations.
Finally, there is modularity, meaning modular AI components such as swappable embeddings, pluggable inference backends, and portable fine-tuners, like LoRA and adapters.
Combined, all of this creates a strong architecture that includes:
- Multi-model gateway
- Routing engine
- Modular retrieval layer
- Portable embeddings with a vector store
- Containers/K8s
- An abstraction layer that separates model providers from a business’ app logic
- Observability and evaluation layer
How To Avoid Vendor Lock-In While Building an AI Stack?
The key is to design for flexibility from the start- Use a model-agnostic architecture, coupled with modular components, and multi-cloud strategies so you can easily switch between models, embeddings, or providers without having to tear down the stack.
Implementation Roadmap: Step-by-Step Escape from Vendor Lock-In
Escaping the vendor lock-in is not a quick and simple job, but rather a process that takes several steps. It was designed to gradually replace brittle components with portable ones, and it can be completed in three phases:
Phase 1 - Assessment
The first phase starts with a model usage audit. Businesses should list every model at their disposal, including who uses it, how often, which features depend on it, and the like.
Then, they should do latency and rate-limit analysis to track where the app might encounter issues and bottlenecks, since it is these choke points that lead to an overall failure. After that, they map out dependencies end-to-end and finally, calculate egress and storage costs of moving data between clouds or out of a vector.
Phase 2 - TCO Calculator
Next comes phase 2, which is where calculating the true cost comes. Most enterprises like the simplicity of monthly API bills, but as was discussed previously, these numbers do not represent the actual cost of ownership. Instead, businesses should rely on proper formulas for:
API cost growth:
Annual cost = token_usage_per_call × calls_per_day × cost_per_token × 365 × inflation_factor
GPU inference cost:
GPU cost = hourly_rate × utilisation_hours + overhead
Storage + vector DB:
Storage cost = data_volume × price_per_GB + query_costs
DevOps + engineering overhead:
Operational cost = hours_per_month × blended_engineering_rate
Switching cost amortisation:
Annualised switching cost = migration_cost / amortisation_period
This TCO model shows just how temporary the cheap API phase really is, and the actual long-term costs that they can expect to encounter later on.Phase
3 - Migration Plan
Finally, the third phase depends on developing an actual migration plan. This starts by introducing a multi-model gateway, even if only one model will be used at first. What matters is the ease of adding new ones or replacing the original one with something else.
Next, add open-source drop-in substitutes for classification, extraction, RAG, or summarisation tasks. After that, businesses should build a model-agnostic interface, where their app should ask for capabilities rather than brands. This is where it becomes irrelevant which model is being used, and all that matters is what it can do.
Finally, run a dual-run period, where you use both the old and new systems, and compare them to one another. Assess their output quality, latency, cost, resilience, and similar attributes, and see which one works better overall.
Is AI Your Differentiator, or an Enabler?
For most companies, AI acts as an enabler, rather than a competitive edge. The real value comes from the systems, workflows, and proprietary data wrapped around it, such as the retrieval layers, orchestration, and monitoring that competitors cannot copy just by adding the same model. To be truly different and unique, focus on building process to maximise the advantages rather than betting on the model to do all the work.
Conclusion
With AI being the hottest new tech out there, every business is looking for a way to integrate it or benefit from it in some way. Most teams make the mistake of taking the easy route and just integrating an existing model using a simple API, and call it a day. However, they only realise their mistake too late, when the costs start piling up, and every new update requires them to completely rebuild the entire structure.
This guide explains why this is a bad idea and why it is much better to develop a system that doesn’t care which model or cloud is being used, but allows you to easily switch between them, use multiple simultaneously, or remove the ones that prove inefficient or become outdated.
AI moats come from systems, workflows, data, and architecture - not buying API access. So, if your company wants an actual moat, you build it the old-fashioned way, where you own the system, and change models when you need to. This will lead to both performance and cost consistency, and let you stay in control.
