Why Are AI Services So Expensive? A Deep Dive into the Cost Structure of GPU Inference — Copainter Available from ¥680

In recent years, a wide range of AI services have emerged, including ChatGPT, Gemini, AI image generation, AI assistants, video generation, and voice synthesis. However, many users often ask:

• “Why are AI services so expensive?”
• “Why do some services cost more than ¥10,000 per month?”

The short answer:

AI services tend to be expensive because GPU-based inference is required.

That said, the AI creation support service Copainter offers plans starting at ¥680 per month (with usage limits), which is exceptionally affordable compared to typical AI services.

In this article, we’ll explain the real reasons AI services are expensive, and how Copainter manages to keep its pricing low.

The Main Reason AI Services Are Expensive: GPUs

1. High-end GPUs are extremely expensive

AI inference requires powerful GPUs such as A100, H100, or RTX 6000 to run efficiently.

Typical prices per GPU:

• A100: ¥1–3 million+
• H100 / H200: ¥3–5 million+

To handle hundreds or thousands of concurrent users, AI services must deploy many GPUs—sometimes hundreds at a time. This alone makes AI services costly.

24/7 GPU Operation Costs

2. AI services must always be running

Even a 1–2 second delay hurts user experience, so GPUs must stay on standby 24 hours a day.

This includes:

• Maintaining idle GPUs during low-traffic hours
• Keeping spare GPUs for sudden traffic spikes
• Redundant setups to prevent downtime

With cloud GPUs, idle time still costs money, significantly increasing operational expenses.

Massive Power and Cooling Costs

3. GPUs consume huge amounts of electricity

AI GPUs are power-hungry:

• A100: ~400W
• H100: 700W+

Running just 10 GPUs can consume 4–7 kW continuously, plus additional cooling infrastructure. Data center electricity and cooling costs are substantial.

Advanced Engineering Is Required

4. GPUs alone aren’t enough

Running AI efficiently requires deep optimization, including:

• Model quantization (FP16 / INT8)
• Memory optimization
• Batch inference tuning
• CUDA kernel optimization
• Multi-GPU distributed processing

These tasks require highly specialized AI engineers, and engineering costs directly affect service pricing.

AI Costs Scale with Users

5. More users = more GPUs

AI services scale poorly compared to CPU-based systems:

• Double the traffic → nearly double the GPUs
• GPUs must be provisioned for peak hours
• Overload causes latency or failures

This linear cost structure makes AI services inherently expensive as they grow.

High Prices Are Structurally Inevitable for Most AI Services

Using cloud providers like AWS, Azure, or GCP:

• A100 GPU: ~¥1,000 per hour
• Monthly cost: ¥500,000+ per GPU

In short, high AI service pricing is structurally unavoidable for most providers.

Why Copainter Can Start at ¥680 per Month

While many AI services charge ¥5,000–¥15,000 per month, Copainter offers a ¥680 entry plan thanks to three key strategies:

1. Efficient GPU sharing and optimization

Copainter uses a custom infrastructure designed to maximize inference efficiency, allowing many users to share GPU resources effectively.

2. Ticket-based usage limits

By introducing a ticket system, Copainter can precisely control inference volume, enabling optimal GPU allocation.

3. Minimal dependence on expensive cloud GPUs

Copainter leverages its own optimized GPU environment, reducing reliance on high-cost cloud infrastructure.

Conclusion

AI services are expensive because of:

• High GPU costs
• 24/7 operation requirements
• Electricity and cooling expenses
• Advanced engineering needs
• Linear cost scaling with users

Despite this, Copainter achieves industry-leading affordability starting at ¥680 per month through smart infrastructure design and optimization.

For creators working on manga, anime, games, or webtoons, Copainter stands out as one of the most cost-effective AI creation tools available.