Optimizing the cost of AI training: A step-by-step guide for the early, mid, and long term

After the ChatGPT craze, every developer wants to build an AI model. But the reality? It ends up costing way too much money.

Especially for individual developers or startups:

Cloud: Unpredictable billing bombs 💸
On-premises: Heavy initial investment burden 💰
Just giving up: Falling behind in AI innovation 📉

But is this really the only way? So I've put together a summary.

2025: A New Turning Point in AI Development

1. HuggingFace + AWS combo

I fine-tuned one sentiment analysis model, then nearly had a heart attack seeing the AWS bill the next day

You might set a monthly budget of around 1 million won, but when billing day rolls around, you could get hit with an unexpectedly huge charge.

2. On-Premises vs. Cloud: Reality Check

Is on-premises really the answer? Dell EMC server racks + a knowledge industry center (with low electricity rates) could be far more efficient.

Dell EMC server rack configuration:

4 GPU servers (RTX 4090 x 4 per server)
Total Purchase Cost: 80 million KRW (one-time)
Knowledge Industry Center electricity cost: 500,000 KRW/month

Equivalent performance on AWS p3.8xlarge:

$14.688 per hour (approx. 20,000 KRW)
Assuming 720 hours per month: 14.4 million KRW
Over 170 million won per year 💸

Conclusion: Running it for just 6 months shows that on-premises can be more profitable when viewed as a long-term investment.

3. But the hidden costs of on-premises

bash# 예상 vs 현실
초기구매비: 8,000만원 → 1억 2천만원 (UPS, 쿨링시스템 추가)
전기세: 월 50만원 → 월 120만원 (에어컨 24시간 가동)
관리비: 0원 → 월 200만원 (시스템 관리자 필요)

4. Ultimately, the developer's dilemma

Cloud: Flexible but a cost bomb
On-premises: High upfront costs but profitable long-term?

But the real problem is… both cost a lot of money 😭

5. So the real solution we found: NPU

Neural Processing Unit = AI-dedicated chip

Over 10x more power-efficient than GPUs
High initial cost but long-term benefits
Predictable fixed costs

NPU + Knowledge Industry Center Combination:

Initial: 30-80 million KRW
Monthly operation: 500,000–1,500,000 KRW (electricity + management)
After 6 months: Becomes cheaper than AWS

6/ But the real game changer is this

Pre-trained model + Fine-tuning

Training from scratch ❌ Utilizing existing models ⭕
Reduces development time by 1 year
Saves hundreds of thousands of dollars
Only 100,000-500,000 KRW per month

🧠 AI Training Cost Strategy at a Glance

Strategy	Recommended For	Key Benefits	Emotion-Based Criteria	Budget	Risk
🔹 Pre-trained model + Fine-tuning	Short-term results, MVP launchers	Time + Cost Savings, Flexibility	Suitable for MVP implementation	💸 100,000~500,000 KRW/month	Limited customization
🔹 NPU + On-Premises	Companies building their own AI OS	Lower power costs, reduced long-term expenses, increased independence	Capable of building large-scale architectures	💸 Initial investment: 30–80 million	Initial capital burden
🔹 Small Language Models (sLM)	Personal creators, prototypes	Laptop-compatible, lightweight	Optimal for UX experimentation	💸 0~100,000 KRW	Difficulty with complex logic processing
🔹 Cloud NPU (KT ATOM)	Startups seeking GPU alternatives	Stability↑, Operational Ease	Backend for server processing	💸 300,000~700,000 KRW/month	Dependencies, complex setup

1. Pre-trained models + Fine-tuning (Highly recommended)

Leveraging pre-trained AI models can reduce AI application development time by up to one year and save hundreds of thousands of dollars.

Reference: What Are Pre-trained AI Models? : NVIDIA Blog

Cost: 100,000–500,000 KRW/month

HuggingFace models + AWS/Google Cloud Spot Instances
Fine-tune existing models for specific use cases

2. NPU + On-Premises Combination (Long-Term Optimal)

NPUs offer higher efficiency compared to GPUs, excel at achieving price competitiveness through mass production, and deliver low-power, high-performance AI computations

Reference: Aitimes Techm

Initial Cost: 30-80 million KRW Monthly Operating Cost: 5-15 million KRW (Electricity + Maintenance)

3. Utilizing Small Language Models (sLM)

Small models are gaining prominence starting in 2025. They can deliver meaningful performance even with billions of parameters, making them easily executable on personal laptops or high-performance smartphones.

Reference: Where is AI Headed in 2025? 7 Essential Trends You Must Know Now

4. Cloud NPU Services

KT Cloud offers Rebellion's ATOM chip NPU on its cloud platform. Compared to traditional GPUs, it offers the advantages of low power consumption and high performance, enabling cost savings.

Helpful Resource: Serving sLM with NPU: Exploring New Possibilities — kt cloud [Tech blog]

💡 Conclusion: Why NPU + Knowledge Industry Centers Are the Answer

NPUs are intelligent semiconductors optimized for specific AI computations, delivering superior power efficiency and performance compared to general-purpose GPUs in their respective domains.

Reference: Server and Edge-Oriented NPU Technology Development Trends

Why NPU + On-Premises is Optimal:

Power Efficiency: NPUs are gaining attention as an alternative to overcome the limitations of high power consumption and high costs, enhancing efficiency through low-power, high-speed processing
Predictable Costs: No cloud billing surprises
Data Security: Eliminates the need for external data transmission
Long-Term Cost-Effectiveness: Investment payback within 6 months to 1 year

Reference: Why NPUs are gaining prominence over GPUs in the AI era… "The key is power and cost savings"

🚀 Final Recommendations

However, due to the large initial investment:

For short-term projects → Utilize pre-trained models
If AI is a core business long-term → NPU + server rack on-premises + knowledge industry center (low electricity costs) is the most efficient choice.

Share your experiences saving on AI development costs or tales of billing hell in the comments! However, due to the large initial investment cost, for short-term projects → utilize pre-trained models, and if AI is to be a core business long-term → NPU + server rack on-premises + knowledge industry center (low electricity rates) is the most efficient choice.