Local LLM vs ChatGPT: an honest comparison of cost, privacy, and quality (which is the better deal)

Let me state the conclusion first. "A local LLM is the better deal because it's free" is only half right. Even if the software and model are free, when you amortize monthly the cost of hardware that runs it comfortably plus electricity, for light usage ChatGPT Plus (about $20/month) is often cheaper. A local LLM truly becomes the better deal when one of these applies: "privacy is required," "offline is required," "you use it so heavily that API billing grows heavy," or "you already have a good enough PC." This article examines this break-even with an honest estimate that includes hardware cost.

This article is the comparison installment of the getting-started guide for local LLMs. For the basics of getting started, see that one.

First, the big picture: comparing on four axes

A local LLM and ChatGPT (cloud) differ in character along four axes.

It's decided not by "which is superior" but by "which axis matters for your use." Let's look at them in order.

Cost: estimating the true nature of "free"

A local LLM's cost comes into view correctly when you amortize the hardware's upfront investment "monthly" and add electricity. The common misconception is to ignore this upfront investment and think "0 yen."

Let's do an honest comparison with an estimate that states its premises.

/**
 * ローカルLLMの実質月額と、サブスク（ChatGPT Plus等）を比較する純粋関数。
 * 「ハードを月割り＋電気代」をローカルの実コストとして扱う（隠れ初期費を可視化）。
 * 金額・前提はすべて引数で受け取り、誰でも自分の数字で再計算できる。
 */
interface LocalCostInputs {
  readonly hardwareCostJpy: number;     // GPU等の追加投資（既存PCで足りるなら0）
  readonly amortizeMonths: number;      // 何か月で償却するか（例: 36）
  readonly gpuWatts: number;            // 稼働時の消費電力（例: 350W）
  readonly hoursPerDay: number;         // 1日あたりの稼働時間
  readonly electricityJpyPerKwh: number;// 電気料金（例: 31円/kWh）
}

interface CostComparison {
  readonly localMonthlyJpy: number;     // ローカルの実質月額（償却＋電気）
  readonly subscriptionMonthlyJpy: number;
  readonly cheaper: "local" | "subscription";
}

const DAYS_PER_MONTH = 30;

export function compareLlmCost(
  local: LocalCostInputs,
  subscriptionMonthlyJpy: number,
): CostComparison {
  const amortizedHardware = local.hardwareCostJpy / local.amortizeMonths;
  const monthlyKwh = (local.gpuWatts / 1000) * local.hoursPerDay * DAYS_PER_MONTH;
  const electricity = monthlyKwh * local.electricityJpyPerKwh;
  const localMonthlyJpy = amortizedHardware + electricity;

  return {
    localMonthlyJpy,
    subscriptionMonthlyJpy,
    cheaper: localMonthlyJpy <= subscriptionMonthlyJpy ? "local" : "subscription",
  };
}

The reality this estimate shows is roughly as follows (figures are examples; recompute for your environment).

• If you buy a new GPU: buying a high-performance GPU PC for several hundred thousand yen and amortizing it over three years comes to around 10,000 yen/month for hardware alone. Adding electricity, it's often more expensive than ChatGPT Plus (about 3,000 yen/month). In other words, if you "just want to use AI," for light-to-moderate usage the cloud is cheaper.
• If you already have a good enough PC: with zero additional hardware investment, the cost is almost just electricity. In this case local is overwhelmingly cheaper and becomes all-you-can-use.
• If you use the API (metered) heavily: the more your API bill swells with automation or bulk processing, the more a break-even appears where fixed-cost local (or self-hosting) wins (this business-scale judgment is detailed in the API vs self-hosting break-even).

The point: not "local = free" but "local = pay the upfront investment, then it's all-you-can-use." Whether you already have a PC, or use it heavily decides whether it's the better deal. I myself, on projects that assume bulk processing, run open models on my own GPU for cost — this is exactly the break-even judgment of "fixed cost is the better deal because I use it to the hilt."

Privacy: this is a clear win for local

On cost there are pros and cons, but on privacy a local LLM is a clear win.

• Local LLM: the data you input is processed only inside your own PC and not sent externally (except when downloading the model). You can handle confidential information, personal data, unpublished material, source code, and the like with peace of mind.
• Cloud (ChatGPT, etc.): input is sent to external servers. Providers take care with data handling and offer settings to not use it for training and zero-data-retention options, but if there's a regulation, contract, or internal rule that "data can't go outside," it's not an option in the first place.

"May we input confidential data into an external AI?" is actually a big point of contention at companies. If "the data physically not leaving" is a requirement, a local LLM (or self-hosting in your own environment) is the answer.

Offline: runs in environments without the internet

Unassuming but effective is offline operation. Once you download the model, a local LLM runs without an internet connection.

• Usable on planes, bullet trains, and places with poor reception.
• Runs even in internal environments isolated from the internet (closed networks).
• Unaffected by external services' outages, spec changes, price hikes, or shutdowns.

Cloud AI is convenient, but it has the dependency that "if the other party's service goes down, you stop too." A local LLM is free from that dependency.

Quality and speed: top-tier is cloud, everyday is fine on local

Honestly, if you seek the highest quality, the cloud's top-tier models still lead. The cutting-edge giant models won't fit on a personal PC. For complex reasoning and difficult tasks, the cloud has the advantage.

But — for everyday uses (summarization, drafting, classification, translation, coding assistance, chat), a mid-size local model (14B–32B class) is plenty practical. The point is "not everything needs the highest quality."

As for speed, as stated in the getting-started guide, the iron rule is to measure on your own PC since it's environment-dependent. Running a small model on a recent GPU gives speeds fine for conversation, but giant models get slow.

Conclusion: not either/or — "combining" is the realistic answer

Given all the above, here's the recommended split.

The optimal answer for many people is to combine. Use a local LLM for everyday use and confidential data, and rely on the cloud only for hard problems that absolutely need the highest quality. Not "all local because it's free" nor "all cloud because it's easy," but splitting by use gives the best balance of cost, privacy, and quality.

FAQ

Q. Which is cheaper, a local LLM or ChatGPT?

It's decided by how you use it and whether you have hardware. If you buy a new GPU, amortizing the hardware monthly is often more expensive than ChatGPT Plus (about 3,000 yen/month). If you already have a good enough PC, the cost is almost just electricity and local is overwhelmingly cheaper. Also, the more heavily you use the API, the more local (fixed cost) wins. Understand it as not "local = free" but "pay the upfront investment and it's all-you-can-use."

Q. I'm worried about privacy. Is local safe?

A local LLM is clearly advantageous on privacy since input data isn't sent externally. It's especially important for uses handling confidential information, personal data, and unpublished material. Cloud AI providers also take care with data protection, but if there's a regulation, contract, or internal rule that "data can't go outside," local (or self-hosting in your own environment) is the answer.

Q. Is the quality worse than ChatGPT?

For difficult tasks where you seek the highest quality, the cloud's top-tier models still lead. But for everyday uses like summarization, translation, drafting, classification, coding assistance, and chat, a mid-size local model (14B–32B) is often plenty practical. The perspective that not everything needs the highest quality matters.

Q. In the end, which should I choose?

Not either/or — combining is the realistic answer. Local for confidential data, offline use, and when you already have a PC. Cloud for hard problems needing the highest quality and light usage where you don't want to invest in hardware. Local for everyday use and confidential, cloud only for hard problems — this gives the best balance of cost, privacy, and quality.

Q. I want to handle my company's confidential data with AI. What should I do?

If data can't go outside, a local LLM or self-hosting in your own environment is the option. I can design everything from the stage of trying it on a personal PC to full operation on an internal server (own GPU, internal-document RAG, access control). For the optimal configuration given your usage, confidentiality, and regulatory requirements, also see the API vs self-hosting break-even.

Summary: whether it's the better deal is decided by "how you use it"

In choosing between a local LLM and ChatGPT, here's what to grasp.

1. "Local = free" is a misconception — amortizing hardware + electricity monthly, the cloud can be cheaper for light usage.
2. Local is the better deal when — privacy required, offline required, heavy usage, or you already have a PC, one of these holds.
3. Privacy and offline are clear wins for local — data doesn't go outside, and it runs without the internet.
4. The highest quality is cloud, everyday use is fine on local — not everything needs the highest quality.
5. Not either/or — combining is the realistic answer — local for confidential, cloud for hard problems.

"I want to use AI without sending my company's confidential data outside" / "I want to use AI heavily while keeping cost down" — I help design that break-even and the optimal configuration, from personal use to production operations.