How to Choose a MaaS Platform in Vietnam and APAC?

Businesses in Vietnam and across APAC should choose a MaaS (Model-as-a-Service) platform based on seven criteria: data processing location, legal entity and payment, model catalog, API latency, usage cost, API rate limits and technical support. Model count and price per million tokens are only two of these factors, and they are not enough on their own to identify the right platform for production AI.

This article groups Model API providers into four categories by how they deliver models: model developers, cloud platforms, inference / MaaS providers and aggregator gateways. It then compares GreenNode MaaS in detail with providers in the same category, and closes with a four-step POC process to help businesses evaluate and choose a provider using real data.

What Is MaaS? Common Types of Model API Providers

MaaS (Model-as-a-Service) is a way for businesses to call foundation models through an API and pay for what they use, usually per token. Instead of investing in and operating GPU infrastructure, businesses can integrate a Model API into their existing applications. The MaaS provider deploys the models, supplies the compute and runs AI inference.

By how they deliver models, Model API providers fall into four categories:

• Model developers offering direct APIs. Examples: OpenAI, Anthropic, Mistral AI. Businesses call the API of the company that built the model and get the newest versions first. Each developer offers only its own models.
• Cloud platforms offering model access. Examples: Amazon Bedrock, Google Vertex AI, Azure AI Foundry. Models from many developers are offered inside the cloud ecosystem, with IAM, private networking and monitoring. Best suited to businesses already running their systems on that cloud.
• Inference / Model-as-a-Service providers. Examples: Together AI, Fireworks AI, Groq, GreenNode MaaS. They run their own GPU infrastructure to serve models, mostly open-source, and sell access via API by the token. Providers differ in model catalog, infrastructure location, speed and pricing.
• Aggregator gateways. Example: OpenRouter. They do not run models themselves; they route requests to providers in the three categories above. OpenRouter lists more than 600 models from over 80 providers. Where data is processed depends on the provider selected for each request.

No category is better in every case, and many businesses combine several. The seven criteria below help identify which priorities matter most for your business.

7 Criteria for Choosing a MaaS Platform

1. Data processing location and how the provider labels models

This is the first criterion to check, because it decides whether a provider makes the shortlist at all. Most APAC markets now have personal data protection laws with specific rules on cross-border transfers: Vietnam’s Personal Data Protection Law (effective January 1, 2026), the PDPA in Singapore and Thailand, Indonesia’s PDP Law and Malaysia’s amended PDPA. For each model, you need to know where requests are processed: on the MaaS provider’s infrastructure, in which region, or forwarded to the model developer in another country.

A good platform labels this clearly. For example, the GreenNode MaaS model list distinguishes GreenNode self-host models (running on GreenNode infrastructure) from third-party models (forwarded to the model developer under contract). On hyperscalers, check whether you are using a regional or global endpoint, and whether cross-region inference can send requests outside the source region. Learn more: Why data processing location matters in the AI era.

2. Legal entity, contract, invoicing and payment method

Beyond technical factors, the legal entity and payment method directly affect whether a business can adopt MaaS.

The practical questions are: which legal entity signs the contract, whether invoices are valid for local accounting, whether you pay in local currency or USD, and whether bank transfer is accepted or an international card is required. Global gateways usually take prepaid credits by card or cryptocurrency and charge a platform fee on top-ups. Hyperscalers invoice through the cloud account, sometimes via local resellers. For businesses in Vietnam, a provider with a local legal entity makes contracting, invoicing and paying in VND simpler. Finance and legal teams should be involved from this step, not after the POC.

3. Model catalog and local language performance

A large model count does not mean a MaaS platform meets your needs. What matters is whether the catalog includes the right options for your actual workloads.

Before choosing, define:

• The model types you need: chat, embedding, rerank or image generation.
• The specific models your engineering team uses today or plans to integrate.
• Performance in your users’ languages, such as Vietnamese, Thai, Indonesian and English.
• Whether a model is already available on the platform or still on the roadmap.

Test output quality on your own data rather than relying only on benchmarks or provider claims.

4. Latency from where your users are

API latency directly affects user experience, especially for chatbots, internal AI assistants and real-time applications.

A key metric is TTFT (Time to First Token): the time from sending a request to receiving the first token.

Latency depends on several factors, including the network distance between users, the application and the region where the model runs. If requests pass through an intermediary gateway, that extra hop can add response time.

To evaluate it accurately, measure:

• TTFT: time to the first token.
• Total response time: time to complete the full response.
• p50 and p95: latency percentiles that reflect typical performance and slower cases.

Measure from your own infrastructure and in the markets where your users are, not from generic benchmarks.

5. Pricing and payment terms

Compare prices per million input, output and cached tokens, but also check: top-up fees, whether credits expire, whether prepaid and postpaid are both offered, and cost at ten times your current volume. The lowest list price is not always the lowest total cost once extra fees and operating effort are included.

6. API rate limits and scalability

Default limits (requests per minute, tokens per minute, requests per day) are enough for testing but rarely for production. Ask whether limits apply per account or per model, how long a limit increase takes and what commitment it requires.

7. Technical support, SLA and portability

Check support channels, language, time zone and response time during incidents. On the technical side, an OpenAI-compatible API lets you switch providers by changing only the base URL, API key and model name, reducing lock-in. For a more detailed checklist, see How to evaluate Model-as-a-Service vendors.

Comparing Model API Providers by Criteria

To choose the right platform, look at the Model API market at two levels: compare provider categories to understand how their service models differ, then evaluate MaaS platforms within the same category in depth.

This section covers:

• A comparison of the four categories of Model API providers: characteristics, strengths and limitations of each.
• A comparison of GreenNode MaaS with providers in the same category: models, data processing location, API, cost and rate limits.

Comparing the four provider categories

Rather than choosing on model catalog or API price alone, businesses need to understand how the four common categories of Model API providers differ: model developers, cloud platforms, inference / MaaS providers and aggregator gateways.

Comparing inference / MaaS providers

If you have decided to use an inference / MaaS provider, the table below compares three representative platforms on model catalog, data processing location, API, cost and scalability.

Compiled from each provider’s public documentation, updated November 2026. Model catalogs, regions and rate limits change often; verify on official sites before deciding.

For a comparison of inference providers by performance and infrastructure, see Top AI Inference Platforms in 2026.

Who GreenNode MaaS Is (and Isn’t) For

A good fit when your business:

• Needs foundation models running on infrastructure in Vietnam, and wants to know clearly which models are self-hosted and which are forwarded to the developer.
• Serves users in Vietnam and Southeast Asia and needs low latency from the region.
• Wants to contract with a Vietnamese legal entity, pay in VND, and choose prepaid or postpaid.
• Already uses an OpenAI-compatible SDK and wants to switch by changing the base URL and API key.
• Wants to try and compare models in a no-code Playground before integrating.

Consider other options when your business:

• Needs access to hundreds of models, including niche ones. Global gateways have a broader catalog.
• Needs high throughput from day one before a limit increase is in place. GreenNode MaaS default limits suit testing; production requires working with GreenNode to raise them.
• Depends on a model that is still on the roadmap and not yet available.
• Has internal policies or local regulations requiring data to be processed in its own country (other than Vietnam). In that case, choose a region in that country if one is available.
• Runs its entire stack on one hyperscaler and needs deep integration with that ecosystem’s IAM and private networking.

A 4-Step Process for Choosing a MaaS Platform and Running a POC

Step 1. Define the workload and evaluation dataset

Write down the use case (customer service chatbot, document summarization, internal RAG, coding assistant), the type of data that will pass through the model, languages, expected volume and acceptable latency. Prepare 50 to 200 sample questions or tasks drawn from real data, with reference answers or scoring criteria.

Step 2. Shortlist 2–3 providers and review data policies

Rule out providers that fail criteria 1 and 2 first. For each remaining provider, read the data processing terms, the processing location of each model you plan to use, log retention periods and whether data is used for training. Anything the public documentation does not make clear, ask in writing.

Step 3. Test and measure on the same dataset

Start in the Playground to quickly rule out models that fall short, then call the API from your real infrastructure. If all platforms are OpenAI-compatible, you can use the same code and change only the base URL, API key and model name. Measure three groups of metrics:

• Quality: pass rate on the evaluation dataset and accuracy in local languages.
• Latency: TTFT and total response time at p50 and p95, measured in the markets you serve.
• Cost: actual tokens per task multiplied by unit price, projected to a monthly cost at expected volume.

Step 4. Confirm contract, rate limits and payment terms

Before going to production, confirm in writing: the rate limits granted for real traffic, prepaid or postpaid terms, invoicing and the support channel for incidents. Keep your code compatible with multiple providers so you have a fallback.

Frequently Asked Questions

How is MaaS different from renting GPUs to run models yourself?

With MaaS, you call models through an API and pay per token, with no infrastructure to operate. Renting GPUs gives you full control over the model, configuration and data, but your team must deploy, optimize and operate everything. Many businesses start with MaaS and move to dedicated GPUs once volume is high enough or they need deep fine-tuning.

Global gateways have more models. Should we just choose one?

A broad catalog helps when you need to try many models. In production, though, you need certainty about where each request is processed, latency from your region and suitable payment terms. Evaluate against all seven criteria rather than model count alone.

Does using MaaS guarantee compliance with data protection rules?

Not automatically. A platform with a clear processing location makes compliance easier, but your business remains responsible for what data goes into the model, how data subjects are notified and the procedures required by local law. Consult your legal team before sending personal data to a model.

Can businesses outside Vietnam use GreenNode MaaS?

Yes. The API is OpenAI-compatible, so it can be integrated from anywhere. Businesses should test latency from their own markets and check local cross-border data transfer rules, since GreenNode’s self-host infrastructure is located in Vietnam.

How long does it take to switch from one provider to another?

If your application uses an OpenAI-compatible SDK and the new platform is compatible too, the code change is usually just the base URL, API key and model name. The more time-consuming parts are re-evaluating output quality with the new model and finalizing the contract and rate limits.

Start Evaluating GreenNode MaaS

The fastest way to put GreenNode MaaS on your shortlist is to run your evaluation dataset in the Playground, then call the API from your real application. When you need higher rate limits or postpaid billing, the GreenNode team will work with you.

Try models in GreenNode MaaS Playground · Contact the GreenNode team