Conclusion

Let’s get straight to the point. “AI that doesn’t send data outside” is now readily usable.

I want to ask small and medium-sized enterprises that are paying 100,000 or 200,000 yen a month for cloud AI:

Is it really safe to send that data outside?

And is that cost really justified?

From late 2024 to 2025, local AI—meaning AI that operates entirely on your own PC—has dramatically reached a “usable level.” You can open the box, install it, and start using it right away. This is what’s known as plug-and-play. And it costs less than 50,000 yen a month.

In this article, I will help you determine whether you should ditch cloud AI and switch to local AI using specific costs and figures.

—

Offline Voice-to-Text Conversion: What Has Changed?

The most noticeable change is in voice-to-text conversion.

Traditionally, services for creating meeting minutes or transcribing business discussions were predominantly cloud-based. Monthly fees ranged from several thousand to tens of thousands of yen. Audio data was sent to a server, processed, and returned. While convenient, there was always the risk of sensitive information from client meetings or internal confidential data passing through the cloud.

Now, fully offline voice-to-text conversion apps have reached practical levels. A representative example is applications equipped with local models from Whisper. Here are their features:

• Completely offline. Audio data is not sent outside at all.
• Real-time transcription while recording. Words are highlighted during playback.
• No time limits. It can handle a two-hour meeting or a three-hour training session in its entirety.
• Apps that support integration from Apple Watch or smartphones to PCs have also emerged.

What about the cost? For a one-time purchase, it’s around 10,000 to 30,000 yen. Even for subscription models, it’s about 50,000 yen per year. If you were paying 5,000 yen a month for a cloud transcription service, that’s 60,000 yen a year. It’s almost the same amount, or even less if you buy it outright.

Moreover, your data doesn’t leave your premises. If you’re recording meetings where customer personal information or transaction conditions are discussed in a local small or medium-sized enterprise, this difference is significant. You gain not only “affordability” but also “security” at the same time.

—

AI Sandbox: Your PC Becomes an ‘AI Development Environment’

Another major change is the rapid development of desktop applications that can run AI models locally.

Notable examples include open-source tools like “Ollama,” “LM Studio,” and sandbox-type “Kaiden.” Using these, you can download open-source AI models like Llama 3 or Mistral onto your PC and execute chat, text generation, summarization, and translation locally.

Let’s summarize what’s happening:

What’s noteworthy is the comparison for “5 users over a year.” Cloud AI costs continue to balloon with the number of users multiplied by the monthly fee. Local AI, once the initial investment is made, incurs almost zero additional costs regardless of how many people use it. As more employees start using it—10, 20—the gap widens exponentially.

Of course, there are limitations. The size of the models that can run locally depends on the PC’s specifications (especially GPU memory). Fully replicating the performance of a GPT-4 class model locally is still challenging at this time. However, tasks that are frequently performed in small and medium-sized enterprises, such as “summarizing internal documents,” “drafting standard emails,” “automating FAQ responses,” and “organizing meeting minutes,” can be sufficiently handled by models with 7B or 13B parameters.

—

Breaking Down the Cost-Benefit Analysis: The Breakdown of ‘50,000 Yen’

Let’s stop with the abstract discussions and calculate specifically.

Assumptions

• A small business with 5 employees using AI in their daily operations.
• Use cases: creating meeting minutes, drafting emails, summarizing internal documents, simple translations.

If You Continue Using Cloud AI (Annual Cost)

• ChatGPT Plus × 5 users: 3,000 yen × 5 users × 12 months = 180,000 yen
• Cloud transcription service: 5,000 yen × 12 months = 60,000 yen
• Total: 240,000 yen per year (and it continues to incur every year)

If You Switch to Local AI (Annual Cost)

• GPU-equipped PC (RTX 4060 or higher): about 150,000 to 200,000 yen (only in the first year)
• Local transcription app (one-time purchase): about 20,000 yen (only in the first year)
• Ollama or LM Studio: Free
• Additional electricity costs: about 500 yen per month × 12 months = approximately 6,000 yen
• Total for the first year: approximately 170,000 to 220,000 yen
• From the second year onward: approximately 6,000 yen/year

In the first year, the costs are almost break-even. From the second year, the difference exceeds 230,000 yen annually. Over three years, that’s a difference of about 500,000 yen.

Additionally, consider the time savings in work hours.

If creating meeting minutes took 30 minutes each time and now, with local transcription and AI summarization, it takes just 5 minutes, then for a company with three meetings a week, that’s a reduction of 75 minutes per week. Annually, that’s about 65 hours. At an hourly wage of 3,000 yen, that’s a savings of 195,000 yen in labor costs annually.

In other words, the economic effect of implementing local AI exceeds 400,000 yen annually from the second year onward. This is the figure for a small business with 5 employees.

—

“So, should we switch?” — Three Criteria for Decision-Making

I’m not saying every small and medium-sized enterprise should switch to local AI. Here are three criteria to consider:

1. Is there sensitive data being handled?

Customer personal information, contract terms with business partners, employee personnel information—if you’re routinely sending these to AI, local AI is the only option. “We should be fine” is the phrase you’ll regret the most after a data leak occurs.

2. Are three or more people using AI?

Cloud AI costs increase in proportion to the number of users. If three or more people are using it regularly, local AI will definitely be more cost-effective. If only one person is using it a few times a month, it’s fine to stick with cloud.

3. Is the focus on “streamlining routine tasks”?

If you need cutting-edge image generation or advanced reasoning at the GPT-4 level, cloud may still have the advantage. However, for tasks like meeting minutes, emails, summarization, and translation—those “mundane but daily” tasks—local models are more than sufficient. Most of what is needed in small and medium-sized enterprises falls into this category.

If you meet two or more of these criteria, you should seriously consider making the switch.

—

The Real Change is Not in the ‘Cost Structure’ but in the ‘Power Dynamics’

Now, let’s get to the crux of the matter.

The fundamental impact of local AI is not cost reduction. It’s that the power dynamics between large and small businesses are changing.

Until now, only large companies that could afford to pay hundreds of thousands to millions of yen a month for cloud services could fully integrate AI into their operations. Small and medium-sized enterprises believed that “AI is for large companies.”

But what if you could achieve the same level of operational efficiency that large companies enjoy with a 150,000 yen PC and free open-source models?

Moreover, small and medium-sized enterprises have an advantage. Decision-making is faster. “Let’s all start using this next week” can be realized with a single word from the president. In a large company, it might take three months for approval. This speed difference is the greatest weapon of small and medium-sized enterprises.

Local AI is a tool that can maximize that weapon. There’s no need for contract negotiations with cloud vendors or internal security review processes. You can install it on your PC and start using it from tomorrow.

—

What to Do First

There’s no need for grand plans.

1. What to do this week: Try installing “Ollama” on your company’s PC (free, takes 10 minutes).
2. What to do next week: Test actual business emails or meeting minutes with local AI and check the quality.
3. Decide in a month: Compare it with cloud AI and determine if it meets your company’s needs.

The cost of “just trying it out” is zero. What you need for your decision is not information but experience.

Local AI is no longer just a “toy for engineers.” It has quietly and reliably become a tool that can be used in the field of small and medium-sized enterprises. Companies that realize this first will change their cost structures.