Every conversation about AI budget in a Malaysian SME lands in the same place: the per-token bill from OpenAI or Anthropic adds up faster than expected once real workloads move on. With that, there are many teams that don’t start with a paid model, but rather an open source LLM like DeepSeek hosted on their own infrastructure precisely for this reason.
If you’re in a similar dilemma in choosing a model for your business, here’s why the open model is the fastest way in, where it stops being enough, and how Malaysia cloud solutions for businesses can carry a business through the whole journey.
Why Businesses Start With Open Models
Open-weight models changed the entry cost of AI. A team that would have paid for API calls to test a use case can now run the model inside its own environment, on hardware it already has or can rent by the hour. The workflow is the same, but the bill is not.
Three things push businesses to start here:
- Cost predictability: GPU-hours are easier to plan than token spend.
- Data control: prompts and responses never leave the environment.
- Speed of iteration: internal experiments do not need vendor approval or procurement sign-off.
For a first working pilot, it is the fastest path from idea to putting together something running.
What DeepSeek and Its Peers Actually Deliver
DeepSeek is one of several free AI models a business can download and run for itself, alongside Meta’s Llama and Alibaba’s Qwen. None of the three charge a licence fee, and all three can be deployed on a business’s own computers, a private cloud, or rented computing power.
In practice, a smaller version of these models (a distilled 32-billion-parameter model) runs on a single high-end GPU, which handles most everyday tasks like summarising documents, pulling out key details, drafting text, sorting information into categories. The biggest versions can match the reasoning power of paid AI tools like ChatGPT or Claude, but they need far more computing power to run smoothly at production scale.
For a business testing whether AI adds value to a workflow, that entry-level configuration is enough. Many never need anything bigger once it demonstrates its potential.
The Ceiling: Where Open Models Run Out of Room
Capable as they are, open models have a ceiling to what they can do, which shows up in three places.
- Peak concurrency. One GPU serves a handful of parallel requests. If fifty users hit the model at 10am on a Monday at once, requests will queue.
- Frontier reasoning. Open models are strong, but the top hosted models are still superior for the hardest tasks such as complex legal analysis, deep code work, and multi-step planning across long contexts.
- Operational load. Someone has to keep the model up, patch the runtime, monitor for drift, and swap in a new version when it lands. That work is unglamorous and constant.
None of these mean the open model was the wrong start, but when a business reaches this stage, it now needs to work out what should run where.
When to Move to Paid, and What “Move” Really Means
Fortunately, moving to a paid model is rarely a full swap. It looks like this: keep the open model for the high-volume tasks it already handles, add a public API for the specific workloads that hit the ceiling. With this setup, complex reasoning goes to OpenAI or Anthropic while bulk work stays on the local LLM. One routing layer decides which model a request goes to.
Three signals usually trigger the shift:
- Concurrency needs have outgrown the GPU budget.
- A specific workflow needs reasoning the open model does not deliver.
- The maintenance burden starts to compete for the team’s attention with the value the model is producing.
That said, costs actually get easier to model at this stage. The high-volume work stays on the open model, so that part of the bill keeps its predictable per-hour rate, while the public API only carries a narrow set of high-value tasks, which keeps the token spend contained.
Most Malaysian businesses end up running this hybrid setup, being an ideal route for a company outgrowing the capabilities of an open model.
The Malaysian Data Angle
Running an open model locally keeps data inside the business boundary. That makes PDPA obligations easier to manage: no cross-border transfer questions, no third-party processor contracts for the workloads on the local stack. Bank Negara RMiT expectations on outsourcing and data residency are easier to satisfy too.
When a public API enters the picture, those obligations reappear for the specific workloads that use it. That is why the hybrid works: the sensitive, high-volume traffic stays inside the boundary, and the narrow set of tasks going through an API can be scoped, contracted, and reported on properly.
AWS research from late 2025 found 27 per cent of Malaysian businesses had adopted AI by that point. The ones getting value are the ones that matched the model to the workload, rather than picking a vendor first.
Hosting the Open Model, Managing the Transition
Starting with an open model is a legitimate approach for businesses exploring its first AI avenues, but the value is only real if the setup can grow with the business. A pilot that has to be rebuilt to add a public API, or a stack that no one knows how to maintain past month three, loses the head start.
This is where Net Onboard is here to help. With our AmplifyChampion pillar, we host the open model on the right hybrid or multi-cloud setup, size the GPU capacity to the actual workload, and manage the transition to paid or hybrid as your business grows into it. Through our setup, the routing, monitoring, and governance all stay in one stack.
Have a chat with our team or WhatsApp us today to learn more about our Malaysia cloud solutions for businesses. We can help you start on an open model and scale from there.