Conclusion: Don’t Bet on One Company

The U.S. Department of Defense (Pentagon) has declared that it will “never again depend on a single AI provider.” The world’s largest organization has explicitly labeled reliance on a specific vendor as a “risk.”

This issue is not just about national defense; it is something that local small and medium-sized enterprises (SMEs) should consider right now.

Why? Because SMEs are more vulnerable to the damages of vendor lock-in.

What Happens to Companies That Choose One Vendor Because “It’s Cheap”

What is AI vendor lock-in? It’s not a complicated concept. It means, “Once you base your operations on a single AI service, you can no longer escape it.”

What specifically happens?

• You can’t resist price hikes. OpenAI has repeatedly revised prices for some models from 2023 to 2024. Companies that rely on a single vendor have no choice but to pay when prices go up. If a monthly fee of 50,000 yen becomes 150,000 yen, they will pay it rather than halt operations. This results in an annual “invisible cost increase” of 1.2 million yen.
• You can’t escape even if performance declines. If the output quality of GPT-4o no longer meets the company’s needs, and if prompts and workflows are built around that model, it could cost hundreds of thousands to millions to rebuild.
• You lose the flexibility to try new options. New models like Claude, Gemini, and Mistral are emerging that may be cheaper and more accurate than GPT for certain applications. However, if everything is tied to one vendor, companies won’t even conduct comparative tests.

The Pentagon is concerned about this very structure. SMEs are more likely to fall into this trap than large corporations because they tend to stick with the “first tool they used”.

What Changes When You Switch to Multi-AI?

Let’s move beyond abstract discussions and look at the numbers.

Consider a local SME (20 employees, manufacturing) using AI for three tasks.

In the case of a single vendor (only GPT-4o):

• Automatic meeting minutes generation: Approximately 20,000 yen/month (API usage fee)
• Customer inquiry response chatbot: Approximately 50,000 yen/month
• Internal manual search and summarization: Approximately 30,000 yen/month
• Total: Approximately 100,000 yen/month, 1.2 million yen/year

In the case of Multi-AI (selecting the optimal model for each application):

• Automatic meeting minutes generation: Gemini Flash → Approximately 5,000 yen/month (strong in lightweight tasks and low cost)
• Customer inquiry response: Claude 3.5 Sonnet → Approximately 30,000 yen/month (high naturalness in Japanese and adherence to instructions)
• Internal manual search and summarization: Local LLM (Ollama + Mistral 7B) → Almost 0 yen/month (operates on the company’s server, no API charges)
• Total: Approximately 35,000 yen/month, 420,000 yen/year

Difference: Approximately 780,000 yen/year.

This means “you save 780,000 yen while doing the same tasks.” For SMEs, 780,000 yen is close to the annual salary of one part-time employee. Moreover, the performance is either equivalent or even better for certain applications.

Addressing the Concern: “But Isn’t It a Hassle to Use Multiple Models?”

This is the biggest misconception.

From the latter half of 2024, the cost of switching AI models has dramatically decreased. Specifically:

• By using API gateways like OpenRouter or LiteLLM, you can switch the model you call with just one line of code.
• By using no-code/low-code tools like Dify or n8n, you can set up in the workflow, “This task is for Claude, this task is for Gemini” via a GUI.
• Local LLMs have become easier to install and start with the advent of Ollama, which allows for installation and startup with just one command.

In other words, the premise that “multi-AI equals complicated management” has already collapsed. With switching costs approaching zero, there is almost no rational reason to remain fixed to one vendor.

Three Realistic Steps for SMEs to Switch to Multi-AI

You don’t need a five-step consulting process. Here’s how it works on the ground.

Step 1: Check the “Invoices” for Your Current AI (Time Required: 30 minutes)

First, accurately assess how much you are paying this month. List out API usage fees, subscription costs, and everything else. Surprisingly, there are cases where “I thought it was 20,000 yen/month, but it turned out to be 50,000 yen due to usage-based billing.” This is the starting point.

Step 2: Try Alternatives Starting with the Most Expensive Task (Time Required: 1-2 days)

You don’t need to change everything at once. Choose one task that costs the most money and try doing the same thing with a different model.

Example: If you’re using GPT-4o for a chatbot and it’s costing you 50,000 yen/month, try running the same prompt with Claude 3.5 Sonnet. If the quality is equal or better, you could save 20,000 yen/month just like that.

Many services offer free tiers or trials. Testing usually costs almost nothing.

Step 3: Implement an API Gateway to Be in a “Switchable State” (Time Required: Half a day to 1 day)

Introduce OpenRouter or LiteLLM to abstract the model calls. This way, if a new model comes out next month, you can test it by changing just one line in the configuration file.

This “always being able to switch” structure is the essence of a multi-AI strategy. The goal is not to use a specific model; the goal is to create a state where you are not tied to any model.

The Agility of SMEs Becomes a Weapon That Large Corporations Can’t Match

For the Pentagon to pivot to multi-AI, it required a massive procurement process and approval flow. The same goes for large corporations. Changing AI tools for company-wide implementation involves passing internal approvals, undergoing security reviews, and retraining staff. This can take six months to a year.

SMEs are different. If the CEO says, “Let’s try this next week,” they can start testing it next week. The speed of decision-making directly translates to the speed of AI utilization.

The evolution of AI models is happening on a monthly basis. A model that was the strongest at the beginning of 2024 could drop to third place in cost-performance just six months later. The ones who can respond to these changes the fastest are not large corporations but SMEs.

The Real Risk Is Not Switching to Multi-AI

Finally, I want to pose one question.

What will you do if the AI service your company is currently using doubles in price next month? What if that service is discontinued next year?

If you face that situation while dependent on a single vendor, your operations will come to a halt. You will have to search for alternatives, migrate data, and rebuild workflows. The opportunity loss and transition costs during that time will easily exceed your usual cost savings.

The Pentagon has acted with this “worst-case scenario” in mind. Even the organization with the largest budget in the world has determined that dependence on a single vendor is an “unbearable risk.”

For SMEs with limited budgets, this is even more critical.

The first step is simple. Open this month’s AI invoice. From there, everything begins.