When bringing AI into a product, businesses usually face three options: call a direct model API from providers like OpenAI or Anthropic, use a managed multi-model inference service (similar to Model as a Service – MaaS), or self-host the AI model on your own GPU infrastructure.

Each deployment approach differs in cost, latency, data control, and operational resources. There is no single model that fits every business. The right choice depends on your workload scale, how sensitive your data is, and your team's infrastructure capability.

This article compares consuming a model as a service (MaaS), calling a direct model API (e.g. the OpenAI API, Anthropic API), and running self-hosted model serving, across the criteria that matter most – cost, latency, data, scalability, and operational effort – and suggests when each approach fits.

Three ways to run AI: direct model API, model-as-a-service, and self-hosted GPU

Fundamentally, all three let your application use a model's inference. The difference lies in where the model is deployed, who runs the inference infrastructure, and where the data is processed.

Direct model API: call an inference endpoint run by the model provider

Overview: You register an account with a model provider such as OpenAI or Anthropic, get an API key, and integrate the provider-hosted inference endpoint into your product over the internet. The provider runs the inference infrastructure, while you pay based on token usage.

Strengths:

• Very fast to start, with almost no setup cost.
• No inference infrastructure to manage; you always get that provider's newest models.
• Ideal for validating an idea (proof of concept) before investing deeply.

Weaknesses:

• Data is sent to the provider's servers (usually located overseas by default; a data-residency option in Vietnam is not always available).
• You're "locked in" to one model provider; switching models means changing the API integration.
• Latency depends on their server region, and is often higher for domestic users.

Hidden costs to note: API costs can rise quickly as traffic grows and are hard to forecast when the workload fluctuates. If data or compliance requirements arise later, you may have to change an already-deployed architecture, adding cost and technical debt.

Best for: startups and small teams that need to get to market fast, especially when the data isn't sensitive and there are no strict compliance requirements yet.

Model-as-a-Service (MaaS): many models through one unified inference layer

Overview: Model as a Service (MaaS) also lets you consume model inference through an API, but a MaaS platform can offer many models on one unified inference API. You don't manage GPUs directly, you pay per usage, and you gain more choice over model and deployment region.

Strengths:

• As fast as calling a direct API, but with better control over data region and latency.
• Switching or combining models is easy thanks to a shared API – avoiding lock-in to one model provider.
• No inference infrastructure team needed; usage-based cost with real-time token tracking.

Weaknesses:

• It's still a managed service, so deep customization (modifying the model architecture, a self-managed serving stack) usually isn't on par with full self-hosting.
• It's still billed per token, so at very large, stable volumes the long-term cost can be higher than self-hosting.

Hidden costs to note: MaaS doesn't require you to invest in and run GPUs, but token costs still need monitoring as the workload grows to avoid unexpected budget overruns.

Best for: mid-size and large businesses that want to deploy AI fast but still need better control over data, models, and deployment region.

Self-hosted model serving: deploy and run the model on your own GPUs

Overview: You download an open-weight model (or a licensed model) and deploy and run model serving on your own GPU infrastructure – on-premises or a private cloud.

Strengths:

• The highest level of data and model control; data never leaves your infrastructure.
• Freedom to customize and fine-tune for specialized tasks.
• No per-token fees – you can optimize cost at very high volumes.

Weaknesses:

• High GPU capital investment (CapEx) and long deployment time.
• You need an MLOps/infrastructure team to operate, scale, update, and monitor it.
• An infrastructure incident (power outage, network failure, GPU fault) can interrupt the service if there's no failover plan.

Hidden costs to note: beyond the GPUs, you must account for power, cooling, operations staff, under-utilized GPUs, security, and infrastructure upgrade costs.

Best for: organizations with a strong enough ML or infrastructure team, large and stable workloads, and high requirements for data and infrastructure control.

Comparison table: cost, latency, data, and operational effort

The table below compares the three AI deployment approaches across the criteria businesses care about most – from upfront cost, latency, and data to scalability and operational effort. Legend: ✓ clear advantage · – case-by-case / neutral · ✗ limitation.

Overall: the direct model API wins on startup speed; self-hosted model serving wins on maximum control and cost at very high volume; and managed multi-model inference (MaaS) sits in the middle – as fast and lightweight as an API, yet keeping most of the data control and latency benefits of hosting infrastructure in-region.

Which option is optimal based on your operations team's size?

The practical choice depends on your scale, how sensitive your data is, and the technical resources you have.

Choose a direct model API if:

• You're a startup or small team that needs an MVP in a few days.
• Your data isn't very sensitive and there are no strict compliance constraints yet.
• You want to validate an idea before committing to invest.

Choose in-region managed multi-model inference if:

• You're a mid-size/large business that wants fast deployment but needs to control the data region.
• You have sensitive data or compliance requirements (finance, banking, insurance, healthcare).
• You want to try multiple models and keep the freedom to switch without rewriting the integration.
• You don't want (or don't yet have) a dedicated AI infrastructure team.

Choose self-hosted model serving if:

• You have a strong enough ML/infrastructure team to run GPUs reliably.
• Usage is very large and stable – enough for the investment to pay off long term.
• You have absolute security requirements; data must not leave internal infrastructure.

In practice, many businesses don't commit to one approach but combine them: using a direct API/MaaS for most use cases to move fast, and self-hosting only for a few especially sensitive tasks or very high volumes. A platform that lets you switch models through one unified inference API makes this combination far less costly.

(On choosing a model for each use case, see how to choose the right AI model; on reducing the operational burden, see how Businesses Can Shorten the Deployment Process.)

Why an in-region MaaS is the right fit for Vietnamese businesses

For many Vietnamese businesses, the problem is balancing three things at once: moving fast, reasonable cost, and keeping data sovereignty. That's exactly the gap an in-region managed multi-model inference service fills.

Take GreenNode MaaS as an example:

• In-region infrastructure. Deployment zones such as Hanoi, Ho Chi Minh City, and Bangkok reduce latency for domestic users and let you choose where data is processed – important for sensitive data and compliance (PDPL, finance – banking).
• Many models through one unified inference API. Try and switch between GPT, Claude, Qwen, GLM, MiniMax… without rewriting the integration – avoiding lock-in to a single provider.
• Transparency between self-hosted and third-party. You can see whether each model runs self-hosted on GreenNode's infrastructure or via a partner, and choose the option that matches your data sensitivity.
• Pay per token, no infrastructure team needed. Usage-based cost with real-time token tracking, while the platform handles GPUs and scaling.

In other words, an in-region MaaS keeps the speed and simplicity of a direct model API while delivering most of the data control and latency benefits of self-hosting – without requiring the business to invest in GPUs or build an MLOps team. That's why it's often a sensible starting point for Vietnamese businesses that want to bring AI into their products seriously.

If you'd like to understand this AI delivery model more deeply, see What Is Model as a Service (MaaS).

Contact GreenNode for a consultation on Model as a Service and choose the AI deployment approach that fits your company's scale.

Frequently Asked Questions

How is MaaS different from calling the ChatGPT API directly?
The ChatGPT API gives you one model from one provider, running on their infrastructure (usually overseas). MaaS gives you many models through one unified inference API, with in-region infrastructure and the ability to choose the data region and a self-hosted/third-party option – so you get better data control and latency.

Is self-hosting a model cheaper than using an API?
It can be cheaper at very large, stable volumes, since you don't pay per token. But you must add GPU investment, operations staff, and deployment time. At moderate or unstable volumes, a direct API/MaaS is usually more economical.

Which approach should a small business start with?
Usually a direct model API or MaaS, to move fast without infrastructure. If your data is sensitive or you have compliance requirements from the start, an in-region MaaS is the safer choice.

Is it hard to switch deployment approaches later?
It's much easier if you design your application to be decoupled from any specific model. Using a platform with one unified inference API for many models makes switching models – or combining API and self-hosted – mostly a configuration change rather than a rewrite.