A Quiet Alliance: The Hidden Layer Powering AI Adoption in Asia
How regional AI service partners are becoming the quiet backbone of AI adoption — and the economics driving their rise.
AI adoption is often described in terms of chips, data centers, and model breakthroughs. But for most businesses in Asia, the decisive factor is something far less glamorous: the partners who translate global AI platforms into real deployments.
This layer is expanding quickly — largely unnoticed — and CloudMile’s recent US$20 million raise is one of the clearest signals.
CloudMile, a Taiwan-based AI and cloud services firm, doubled its revenue in Southeast Asia over the past two years, with plans to expand its AI offerings in the region following its fundraising. On its own, the announcement might look like another routine funding update in a crowded AI market. But beneath it lies a larger shift: it shows that global AI platforms are only as powerful as the partners who implement them — and Asia is becoming the proving ground for this model.
Companies like CloudMile are giving prominence to the AI-as-a-Service (AIaaS) model. Their growth tells us something important about where the economic value of AI is actually accruing — and why the real backbone of AI adoption isn’t where most people are looking.
CloudMile: A Poster Child of AI-as-a-Service
Founded in 2017, CloudMile has served more than 1,200 clients across Asia. Beyond Taiwan, it has offices in Singapore, Malaysia, the Philippines, Indonesia, Vietnam, and Hong Kong — with over 200 people employed across its sites.
Its business includes: helping enterprises migrate to the cloud, deploying machine learning on Google Cloud’s Vertex AI, and integrating Google’s Gemini models into their workflows. Its growth depends on the breadth of Google Cloud’s tools it can offer to its clients, essentially positioning CloudMile as a middleman for Google Cloud.
CloudMile is not the only company building on this model. A look through the Google Cloud Partners directory reveals dozens of similar firms across the region, each certified to provide specialized cloud and AI services. Together, they point to a missing link in how we think about global AI infrastructure. This layer, hiding in plain sight, has been growing steadily even as the public narrative swings between AI euphoria and AI anxiety.
CloudMile’s funding round is therefore less a standalone story and more a marker of a broader trend: AI adoption in Asia is increasingly mediated by regional partners who combine cloud expertise with localized insight.
The Localization Moat
Asia’s AI opportunity is large, fragmented, and full of friction — which makes it fertile ground for partner-driven adoption.
CloudMile serves clients across Taiwan, Singapore, Hong Kong, Malaysia, and Indonesia. These countries are diverse, with distinct regulations and talent pools. Challenges around adopting new technology range from local data residency laws to the availability of skilled talent. This is where CloudMile chimes in, doing the translation work on behalf of its clients.
Case in point: Dcard, a popular anonymous social platform in Taiwan, with a penetration rate of over 90% among people aged 18 to 24. The site sees 20 million unique visitors each month and counts 8 million members across Taiwan, Hong Kong, Macau, and Japan.
When Dcard’s forums began scaling rapidly, the company needed highly scalable IT infrastructure. It chose Google Cloud — the only cloud provider with a data center in Taiwan at the time.
“Google Cloud not only supports high scalability but also lower latency, which helps us ensure our service quality,” said Taco, Dcard’s engineering manager. Dcard relied on CloudMile for technical support and guidance to choose and operationalize Google Cloud services — help it considered essential, even with an engineering team already familiar with the tools.
This is far from a one-off relationship. Dcard plans to leverage Vertex AI to build new engagement features — something it will likely need CloudMile’s expertise for.
Localization isn’t an add-on in Asia — it’s the moat.
The Economics: Google’s Partner Advantage Program
If localization explains why partners matter, Google Cloud’s incentives explain why they’re multiplying.
In 2019, Google Cloud formalized a strategy that placed partners at the center of its go-to-market approach. The Partner Advantage program encourages firms to go beyond reselling and build deep specialization — in industries, geographies, and technical domains.
A select group of partners in each market can earn the designation of Managed Service Provider (MSP), a signal of advanced capability and trust. CloudMile achieved MSP status in Taiwan in 2020, added Singapore the following year, and was most recently recognized in Indonesia. It’s a coveted label, and across the region only a handful of companies hold it.
Partners are responding to the framework. In a post published in 2021, Google Cloud reported that its partner ecosystem grew by more than 400% in two years. The number of partners with specializations rose 70% through 2020, and its managed partners more than doubled their expertise designations compared to the previous year.
The incentives are clear. Partners are expected to generate US$5.32 in revenue for every dollar Google Cloud makes, according to one study. And value creation compounds over time.
Market analyst firm Canalys noted that “the most mature partners in the Google Cloud ecosystem can capture a PEM (Partner Ecosystem Multiplier) of up to US$7.05 for every dollar spent on Google Cloud,” with about half of that value captured in year one and the rest accruing over years two and three.
For Google, this broad network of partners means it can leverage localized knowledge while avoiding the cost of building dedicated teams in every market. It’s strategic and cost-effective — a win-win model in which partner growth and Google’s growth reinforce each other.
The Overlooked Infrastructure Layer
Much of the attention in AI today focuses on GPU supply chains, hyperscaler rivalry, and model breakthroughs. But the real bottleneck for most enterprises — especially in Asia — lies elsewhere: deploying AI safely, efficiently, and in compliance with local expectations.
That’s why the rise of AI-as-a-Service partners deserves more attention. They are turning potential demand for global AI platforms into actual adoption — by smoothing regulatory friction, filling talent gaps, and building institutional knowledge.
The companies doing this work rarely dominate headlines. But behind every AI deployment in Asia, there is a partner translating ambition into implementation. As models grow more capable and the infrastructure beneath them grows more complex, this quiet layer of specialists is becoming the real backbone of AI adoption — not a footnote.