Southeast Asia Needs the Right AI Model for the Right Job
Southeast Asian companies have access to both US and Chinese AI, but lower model prices only translate into savings when performance, retries, deployment and compliance costs are factored in
Chinese open-weight AI models can cost far less than premium US systems, but the savings only matter if they can complete the same work reliably. For Southeast Asian companies, that makes the real question less about which country produces the better model and more about where cheaper Chinese AI actually lowers the cost of getting work done.
Open-weight models can be downloaded, customized, and deployed on infrastructure chosen by the user, giving companies more control over how and where they run AI. While US developers such as OpenAI have moved toward more closed systems, Chinese developers including Moonshot AI, DeepSeek, and Alibaba’s Qwen have leaned heavily into open weights.
Hugging Face CEO Clément Delangue has gone so far as to argue that China could gain an advantage in the AI race through open-weight models and potentially catch up with US model makers.
Southeast Asia Is Already Mixing Models
Southeast Asian companies are already accustomed to buying technology from both US and Chinese suppliers, rather than committing to one ecosystem.
Gartner projects that Chinese AI model adoption among global companies could rise from 5% in 2025 to 50% by 2027, as enterprises increasingly use different models for different tasks and look for cheaper options.
While Gartner did not break out Southeast Asia, companies in the region are already experimenting with how open models fit specific workloads rather than treating them as a complete replacement for US systems. Indonesia’s GoTo, for example, has adopted open models in its AI voice assistant, with Sahabat AI built for Bahasa Indonesia and regional languages.
OCBC offers a clearer example of that stacking approach. The Singapore bank has rolled out more than 30 internal tools using open-source models, with different models assigned to different jobs. Alibaba’s Qwen has been used for coding, while other models support separate internal tasks.
AlonOS has also partnered with Indosat Ooredoo Hutchison to develop DeepSeek-powered applications in Indonesia, adding another Chinese model to an enterprise environment that already relies on a broader mix of technology providers.
That pattern matters. Southeast Asian companies have rarely had to choose exclusively between US and Chinese tech, and AI is beginning to look similar. The opportunity is about adding lower-cost open-weight models for specific jobs where they make sense.
Where Cheaper Models Actually Save Money
The price gap can be substantial. Alex Colville, an analyst with the Australian Strategic Policy Institute’s Cyber, Technology, and Security Program, recently pointed to testing by Artificial Analysis in which leading models were asked to complete 657 office and administrative tasks. Anthropic’s Opus 4.8 cost nearly US$1000 across the tasks, compared with US$270 for Z.ai and just US$14 for DeepSeek-V4 Flash.
But the cheapest model is not necessarily the best performer. DeepSeek used around one billion tokens during the test and completed fewer tasks correctly than several competitors. Colville also noted that some Chinese models also use more tokens on complex tasks, meaning a low token price can overstate the saving on a finished task.
For Southeast Asian companies, language can change the calculation quickly. A model that looks inexpensive on English-language benchmarks still needs to be tested on Indonesian, Thai, Vietnamese, and other local or mixed-language workloads. If those tasks require more correction, retries, or human review, the apparent savings can quickly narrow.
How a model is run also affects the cost. Self-hosting may be cheaper at scale, but companies have to pay for and manage the infrastructure themselves. Using an open-weight model through a cloud provider is simple, but comes with the provider’s own charges.
A better measure is the total cost of getting a usable result, including any retries or extra review. For smaller companies, Chinese open-weight models can be attractive because they can often be tested through existing cloud platforms before committing to a larger deployment.
When Deployment Changes The Risk
How a model is deployed also affects security and regulation. If a company runs an open-weight model in its own environment, it has more control over where the data stays, but it also takes on more responsibility for securing and managing the system. Using a hosted service is easier, but the company must be comfortable with how the provider stores and processes its data.
That matters in Southeast Asia because regulators generally focus on how companies handle data and manage AI risks, rather than whether the model itself is American or Chinese.
Take Singapore, for example. IMDA’s latest framework for agentic AI focuses on who’s responsible for an agent, how its actions are monitored, and what safeguards companies need. Separate privacy guidance also sets rules around the use of personal data without prescribing which country’s models companies should use.
For operators, that means the model itself is only part of the decision. The same open-weight system can carry different cost and risk profiles depending on where it runs, what data it touches, and how much oversight is required.
The Operator Takeaway
The practical opportunity is in workloads where the performance bar is clear, and mistakes are easy to catch. High-volume tasks such as document processing, routine coding, and lower-risk customer support may be natural places to test open-weight models. More complex or sensitive work can remain with frontier systems where the performance premium is worth paying for.
Recent research on enterprise coding assistants points toward the same approach: route each task to the cheapest model that can meet the required quality and latency, while factoring in failures and escalation rather than optimizing for token price alone.
Across Southeast Asia, the economics can vary widely depending on language performance, deployment costs, and local rules. The operator decision, then, is not Chinese AI versus US AI for the whole enterprise. It is choosing the cheapest model that can reliably meet the requirements of each workload.
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