- 23
- February
In an era where AI has become an essential tool for organizations — whether for data analysis, document summarization, or building internal chatbots — the question Thai organizations must face is: should you use Commercial AI (Closed Source) that's ready to use immediately, or Open Source AI that offers full customization? This article compares both approaches head-on, along with a recommended Hybrid Approach tailored for the Thai organizational context.
Commercial AI (Closed Source) — Ready to Use Immediately
Commercial AI refers to AI services developed by major technology companies, accessed by users through APIs or proprietary platforms, without access to source code or model weights — the main providers today include:
Major Commercial AI Providers
- OpenAI (GPT-4o, ChatGPT API) — the global LLM market pioneer with an easy-to-use API, decent Thai language support, and the broadest developer ecosystem
- Anthropic (Claude API) — focused on AI Safety with an exceptionally long Context Window (up to 200K tokens), ideal for analyzing lengthy documents and coding tasks
- Google (Gemini API, Vertex AI) — integrates well with the Google Cloud Ecosystem, offers Multimodal capabilities (image + audio + text) and strong Search Grounding
- Microsoft (Azure OpenAI Service) — offers GPT-4o through Azure Cloud with Data Residency Options and Compliance Certifications that large enterprises require
Advantages of Commercial AI:
- Very high performance — models are trained with massive data and large-scale Data Center compute
- Easy to use, no ML team required — call via API instantly without managing infrastructure
- Support and SLA included — dedicated support team and Service Level Agreement for enterprises
- Continuous updates — providers improve models regularly without requiring any action from you
Disadvantages of Commercial AI:
- Data leaves the organization — every Prompt and Response is sent to the provider's cloud for processing, which may pose PDPA and trade secret concerns
- Ongoing costs — pay per token used; the more you use, the more expensive it gets, and prices can increase at any time
- Vendor Lock-in — once you build Prompt Templates, Fine-tuning, or Workflows on a specific platform, switching to another provider is costly
- Limited customization — you cannot modify Model Weights or tune model behavior as deeply as Open Source
Open-Source AI — Full Control
Open-Source AI refers to AI models that release model weights, source code, and documentation for users to download, customize, and deploy on their own infrastructure freely — notable options today include:
Notable Open-Source AI Models
- Meta Llama 3 / 3.1 — free-to-use models from Meta (Facebook), available in multiple sizes from 8B to 405B parameters, with GPT-4 class performance in certain tasks and a very large community
- Mistral / Mixtral — French models with high performance relative to their size, using Mixture of Experts (MoE) Architecture for speed and resource efficiency
- Typhoon (SCB 10X) — a Thai-language specific AI model developed by SCB 10X (under SCB), trained on extensive Thai language data, enabling superior understanding of Thai context, idioms, and cultural nuances compared to foreign models
- OpenThaiGPT — an Open Source project from the Thai developer community focused on building a Thai-language LLM, suitable for research and learning
Advantages of Open-Source AI:
- 100% data stays within the organization — deploy on your own server; not a single byte of data leaves the organization
- Unlimited customization — fine-tune with organization-specific data and adjust Model Behavior as needed
- No API costs — once setup is complete, use it without limits and no token fees
- No Vendor Lock-in — you can switch models or infrastructure providers at any time
- Easy PDPA Compliance — maintain 100% control over personal data since everything stays in-house
Disadvantages of Open-Source AI:
- Requires GPU/Infrastructure — running large models requires GPUs with high memory (e.g., NVIDIA A100, H100), which are expensive to procure
- Requires ML/DevOps team — skilled personnel are needed for deployment, fine-tuning, and maintenance
- Performance may be lower — small to medium Open Source models often underperform Frontier-level Commercial AI
- Self-managed updates — you must track new versions, test, and deploy them yourself
Comparison Table: Open-Source AI vs Commercial AI
| Topic | Commercial AI | Open-Source AI |
|---|---|---|
| Organization Data | Sent to the provider's cloud | Stays 100% within the organization |
| Cost | Continuous pay-per-token/usage | Infrastructure cost + personnel cost |
| Performance | Very High (Frontier Level) | Good to Very Good (depends on model size) |
| Customization | Limited (Fine-tuning only as the provider allows) | Unlimited (modify everything) |
| Thai Language | Good (GPT-4o, Claude perform well) | Typhoon: Excellent (specialized for the Thai language) |
| PDPA Compliance | Depends on the DPA (Data Processing Agreement) with the provider | 100% control |
| Time to Start | Ready immediately (minutes) | Requires setup (days to weeks) |
| Team thatRequired | Developer can use it | Must have ML Engineer / DevOps |
| Vendor Lock-in | High | None |
Hybrid Approach — The Best Option for Thai Organizations
In practice, most organizations don't need to choose just one path — a Hybrid Approach using both Commercial and Open Source together is often the most suitable option, divided by data sensitivity and nature of the work:
1. General Tasks (Non-Sensitive Data) — Use Commercial API
- Summarize news, draft external emails, write marketing content
- Help write code, review general documents
- Answer customer FAQs via Chatbot
- Why: This data is not confidential — using Commercial API is convenient and high-performance
2. Tasks with Sensitive Data — Use Open Source On-Premise
- Analyze financial data, budgets, and confidential contracts
- Process employee data (salaries, personal records)
- Analyze customer data subject to PDPA
- Why: This data must stay within the organization — sending it to external cloud carries legal and trade secret risks
3. RAG System (Retrieval-Augmented Generation) — Use Both
- Build a Knowledge Base from internal documents (Open Source)
- Use Commercial API as the LLM for answering questions from the Knowledge Base
- Or use Open Source LLM for the entire system if the Knowledge Base contains confidential data
- Why: RAG helps AI answer questions accurately based on organizational data, reducing hallucination without requiring model fine-tuning
Recommended Hybrid Approach:
Start with Commercial API for general tasks to learn how to use AI in your organization — then gradually move sensitive data workloads to Open Source On-Premise when the team is ready. There's no need to rush everything at once.
Real Costs — Comparing Expenses of Both Approaches
Let's look at real numbers for a mid-sized organization (moderate AI usage ~500K tokens/day):
| Item | Commercial API | Open Source (On-Premise) |
|---|---|---|
| Initial cost | Low (~0 baht, just sign up API) | High (GPU Server 300K-1M+ baht) |
| Monthly costmonth (API/Infra) | ~15,000-50,000 baht/month | ~5,000-15,000 baht/month (electricity + maintenance) |
| Additional personnel cost) | Not necessary (existing developers can handle it) | ML Engineer 50K-120K+ baht/month |
| Total cost Year 1 | ~180,000-600,000 baht | ~960,000-2,640,000 baht |
| Total cost Year 2+ | ~180,000-600,000 baht (same or more) | ~660,000-1,620,000 baht (excluding server cost) |
| Break-even | Open Source More cost-effective in Year 2-3 if usage is heavy — but if usage is light, Commercial is more cost-effective | |
Important Note:
The figures above are estimates that depend on usage volume, model size, and current GPU prices — many organizations choose GPU Cloud (e.g., RunPod, Vast.ai) instead of purchasing their own servers, to reduce upfront costs and shift from CapEx to OpEx.
Thai Language — Typhoon's Strength
For Thai organizations seeking AI that truly understands the Thai language, Typhoon by SCB 10X is an excellent option because:
- Trained on extensive Thai language data, enabling strong understanding of idioms, royal vocabulary, and specialized terminology
- Supports Thai word segmentation more accurately than foreign models
- Has an open license allowing commercial use
- An active Thai developer community — with examples of Thai-language deployment and fine-tuning
While Commercial AI like GPT-4o or Claude supports Thai at a decent level, Typhoon has an advantage in tasks requiring deep understanding of Thai context — such as summarizing Thai government documents, analyzing Thai-language contracts, or building chatbots that respond in natural-sounding Thai.
PDPA — What You Need to Know When Using AI with Personal Data
The Personal Data Protection Act (PDPA) is now fully in effect — when organizations use AI with data containing personal information, they must consider:
| PDPA Consideration | Commercial AI | Open Source (On-Premise) |
|---|---|---|
| Cross-border data transfer | Must have DPA + Standard Contractual Clauses | Noneissues (data stays internal) |
| Right to delete data | Must confirm with the provider that data is actually deleted | Can control immediately |
| Purpose of data usage | Must audit provider terms | Definedby yourself 100% |
| Audit Trail | Depends on the provider | Can design own Logging |
Key Recommendation:
If your organization needs to process personal data of employees, customers, or partners through AI — you should primarily use Open Source On-Premise. If using Commercial API, ensure you have a comprehensive Data Processing Agreement (DPA) and verify that the provider does not use your data to train their models.
Saeree ERP — An Open Platform Ready to Connect with AI in the Future
Currently, Saeree ERP does not yet have built-in AI features — but with its Open Architecture designed from the ground up, Saeree ERP is ready to connect with AI in the future:
- RESTful API — standard APIs ready to connect with external AI services (both Commercial and Open Source)
- PostgreSQL database — supports the pgvector Extension for Vector Database, which is the foundation of RAG systems
- On-Premise Deployment — installed on the organization's server, enabling on-premise AI integration without data leaving the network
- Modular Architecture — AI modules can be added in the future without changing the system structure
AI Development Roadmap for Saeree ERP:
The Saeree ERP development team is studying and planning to integrate AI into the ERP system, with a focus on connecting with Open Source models that support the Thai language to keep organizational data within the system — stay tuned for updates soon.
Conclusion — How to Choose What's Right for Your Organization
There is no single right answer — choosing between Open Source and Commercial AI depends on 3 key factors:
- Data sensitivity: If data is confidential, On-Premise is required
- Budget and team: If you don't have an ML team yet, start with Commercial API
- Usage volume: If usage is heavy, Open Source is more cost-effective in the long run
For most Thai organizations, the Hybrid Approach is the most suitable answer — use Commercial API for general tasks requiring high performance, and Open Source On-Premise for tasks involving sensitive data.
AI is not about "choosing one side" — it's about crafting a strategy that fits your organization's context, the data you need to protect, and the team you have. A Hybrid Approach lets you benefit from both sides without sacrificing either.
— Saeree ERP Development Team
If your organization is interested in an ERP system designed as an Open Platform, ready for AI integration in the future, you can schedule a demo or contact our advisory team for further discussion.
