WLStack Network Pioneers Dual-Domain MaaS

WLStack Network’s brand-new Model-as-a-Service (MaaS) platform is about to launch — a dual-site architecture spanning routed.cn and routed.hk that lets enterprises “integrate once, cover both domains, schedule many models,” redefining how the world accesses model services from the network layer up.

Over the past decade, the global large-model industry has moved briskly from laboratory exploration to industrial-scale deployment, and enterprise demand for AI has long since shifted from single-point trials to full-scenario, large-scale rollout. Yet one problem has stood before every practitioner and remained unsolved: if a company wants to deploy compliant domestic AI capabilities and global model services at the same time, how much integration cost must it pay, and how many compliance and network barriers must it cross?

This is the AI industry’s “dual-domain access problem.” Simple to state, it has nonetheless trapped countless going-global enterprises, multi-scenario application developers and AI vendors: the domestic market carries strict algorithm-filing and data-compliance requirements, while overseas the model ecosystem is fragmented and the network links are complex. Under the traditional model, an enterprise often has to build two independent access systems, integrate the APIs of dozens of different vendors, and repeatedly tune cross-border networks — with most of the cost consumed in stitching infrastructure together rather than on business innovation itself. The industry has long assumed that local compliance and global expansion cannot be reconciled, and that network experience and model services cannot be natively fused — an almost insurmountable “default premise” on the road to AI adoption.

Today, we are formally announcing that WLStack Network’s brand-new Model-as-a-Service (MaaS) platform is about to go live. With a dual-site collaborative architecture spanning routed.cn and routed.hk, it decisively breaks this long-standing industry consensus. The former provides full-chain compliant model services for the Chinese mainland market; the latter faces the global market, aggregating the outbound capabilities of high-quality domestic models alongside a curated selection of overseas model services. This is not only a milestone in WLStack Network’s extension from backbone network infrastructure into the AI service layer — it is a fresh reconstruction of the access paradigm for global model services.

The platform’s core advantage likewise stems from cross-disciplinary innovation: it is not a traditional MaaS product that merely aggregates third-party model APIs, but an integrated model-service system built upward from the network layer, rooted in the global BGP backbone capabilities WLStack Network has honed for many years. Just as tools from algebraic number theory unexpectedly cracked a classic problem in discrete geometry, deep accumulation in network engineering lets this platform step outside the homogeneous “race for model count” and redefine the stability, flexibility and access efficiency of model services from the infrastructure layer up.

For the AI industry and for enterprise digitalization, this is an important milestone. It marks the entry of model services into a new stage of native “network + model” fusion, and it demonstrates the new value infrastructure providers can bring amid the wave of AI adoption. Industrial deployment offers the truest proving ground for AI capabilities: demand is clear and measurable, service stability is verifiable, and a complete solution can truly enter production systems only when compliance, performance, cost and scalability all interlock.

The Industrial Proposition of Dual-Domain Access

Let the total access cost of an enterprise’s AI deployment be C(n), where n represents the regions covered, the categories of models and the complexity of scenarios. Industry practice has shown that with every new class of model and every new regional market, access and operating costs grow almost super-linearly: integrating each vendor’s API separately, rebuilding compliance links repeatedly, tuning cross-border networks one by one, and adapting to different interface standards ultimately leave many enterprises’ large-scale AI deployment stuck at single-point pilots.

The prevailing solutions in the industry have always been confined to a single scenario: domestic MaaS platforms only aggregate compliant models, overseas MaaS platforms only provide local model services, and going-global enterprises can only stitch the two systems together themselves, bearing double the integration cost and operational burden. The industry has broadly assumed that compliance and globalization cannot both be had, and that network optimization can only be a bolt-on afterthought rather than a native capability of model services.

WLStack Network’s dual-domain architecture offers a fundamentally different answer. More precisely, this platform, through a dual-site collaborative design, delivers a new experience of “integrate once, cover both domains, schedule many models”:

• routed.cn — the Chinese mainland compliance site: carefully selects mainstream domestic large models that have all passed national algorithm filing, covering general conversation, code generation, multimodal understanding, vector retrieval, industry fine-tuning and more. All data flow and model inference are completed entirely within the domestic border, strictly meeting data-security and algorithm-regulation requirements. An enterprise needs only one integration, one interface and one bill to flexibly invoke multiple compliant models on demand — with no repeated integration across vendors and no separate compliance audits.
• routed.hk — the international site for the world: on one hand it carries the outbound service of high-quality domestic large models, giving global customers a convenient, stable channel to access Chinese model capabilities; on the other it connects a curated selection of mainstream overseas models, giving Chinese going-global enterprises low-latency, highly reliable access to overseas model services. Backed by WLStack Network’s global backbone nodes and cross-border dedicated lines, the platform deeply optimizes cross-region call paths, greatly reducing the latency and jitter of cross-border access and resolving the network pain points of invoking overseas models.

Unlike the industry’s conventional “single-site, single-region” design, the dual-domain architecture breaks the “local vs. overseas” divide at the level of underlying product logic, so enterprises no longer have to trade off compliance against expansion — achieving truly unified, global management of AI capabilities.

Architectural Innovation Born from Network DNA

At the level of underlying logic, this platform’s core breakthrough is to push the familiar capability of network scheduling into an entirely new application scenario, making network infrastructure a native component of model services.

Traditional MaaS platforms mostly center on “API forwarding + billing statistics” — essentially an intermediary layer between model vendors and customers, ill-equipped to solve deeper problems of network latency, link disaster recovery and cross-region scheduling. WLStack Network’s answer is to use more than a decade of hard-won global network scheduling capability to build a carrier-grade foundation for model services:

• Intelligent link routing: relying on WLStack’s global BGP network and cross-border dedicated lines, it automatically selects the optimal access and inference node for each model request; average cross-border call latency is reduced by more than 40% versus ordinary public-internet access, and peak-period link stability rises to 99.99%;
• Unified protocol adaptation layer: fully compatible with mainstream model API protocols, it offers standardized invocation interfaces and multi-language SDKs, so enterprises can switch between models smoothly and combine multiple models to build complex business flows — without modifying core business code;
• Two-tier disaster recovery: with built-in monitoring and failover at both the network and application layers, when a single link or model service fails, it automatically switches to a backup link and alternative model within seconds, keeping the business continuously running;
• Flexible compute scheduling: supporting pay-as-you-go, reserved resources, dedicated instances and more, it dynamically schedules compute according to business peaks and troughs, optimizing cost while safeguarding service experience.

These capabilities have long been familiar to network engineers, but strikingly, once combined with model services they fundamentally resolve the thorniest stability and experience problems in industrial deployment. This cross-disciplinary fusion is the platform’s most essential differentiated value.

Cross-Border Data, Model Competition and Compliance: The Deeper Value of the Dual-Domain Architecture

If network scheduling answers the question of “how well the service works,” then cross-border data, model-technology competition and China–US regulatory policy together decide “whether it can be used at all, and for how long.” The true, deeper value of the dual-domain architecture lies precisely here: amid the tangled interplay of these three variables, it offers enterprises a clear, robust and sustainable path.

Cross-Border Data Transfer: From “Disorderly Flow” to “Domain-Based Governance”

Every model invocation is inherently accompanied by a flow of data — prompts, business documents, user information and inference results; each request is a potential cross-border data transfer. As global data regulation tightens, “where data flows, where it resides, and who processes it” is no longer a technical detail but a precondition for whether a business can operate in compliance.

WLStack’s dual-domain architecture is, in essence, an engineering realization of “domain-based data governance”:

• Domestic closed loop: routed.cn keeps the full chain of data flow and model inference for Chinese mainland users within the border — from access and transmission to inference and storage, none of it leaves the country — naturally aligning with the data-localization and outbound-assessment requirements of the Data Security Law and the Personal Information Protection Law;
• Hong Kong hub: routed.hk uses Hong Kong as a transit hub for cross-border data and model services; leveraging Hong Kong’s mature international networks and relatively independent data-jurisdiction environment, it provides a “buffer zone” for domestic models going global and overseas models coming in, preventing sensitive data from flowing disorderly between jurisdictions;
• Controlled cross-border: for business that genuinely must cross borders, the platform uses standardized data minimization, field de-identification and full-link encryption to keep cross-border data strictly within an explainable, auditable scope, rather than sending entire business datasets out as-is.

Data is no longer a scattered source of risk, but is clearly assigned to different “domains” and governed accordingly — the firmest foundation for cross-border AI adoption.

Model-Technology Competition: Letting Enterprises Avoid “Picking a Side”

Over the past two years, the global large-model landscape has taken on a distinct “bipolar” character: on one side, local forces led by domestic models have risen rapidly, entering the world’s first tier in Chinese comprehension, code generation, multimodality and inference cost; on the other, leading overseas models continue to lead in general capability, ecosystem tooling and long-tail languages. For enterprises, betting on any single camp means bearing the twin risks of technology roadmap and supply chain at once.

The value of dual-domain MaaS lies precisely in freeing enterprises from being forced to “pick a side” in this competition:

• Through routed.cn, enterprises can stably invoke mainstream domestic models that have passed algorithm filing, fully enjoying local models’ advantages in Chinese scenarios, industry fine-tuning and cost;
• Through routed.hk, enterprises can simultaneously connect curated overseas models to fill gaps in general capability and global scenarios, and can also export domestic models’ capabilities to global customers;
• When a camp changes due to technology iteration, supply strategy or the external environment, the unified interface layer lets enterprises switch or combine models with minimal rework — turning “the uncertainty of technology competition” into “the certainty of multiple available paths.”

At a moment when model capabilities iterate rapidly and the competitive landscape has yet to settle, this ability to “connect both domains and switch on demand” is itself a scarce, risk-resistant asset.

China–US Compliance Policy: Building Native Capability for a “Dual-Track” Regime

Regulation of artificial intelligence is advancing rapidly in both China and the United States, each with its own emphasis, forming a “dual-track compliance” reality that cross-border AI business must confront directly.

• The domestic track: the Interim Measures for the Management of Generative AI Services establish the basic framework of algorithm filing, security assessment and content compliance, while the Data Security Law and the Personal Information Protection Law set out paths for cross-border data transfer such as security assessment, standard contracts and personal-information-protection certification. That routed.cn carefully selects only algorithm-filed models and keeps data within the border is a direct response to this system.
• The overseas track: export controls and technology reviews led by the United States continue to tighten — from restrictions on exporting high-end AI chips to China, to a series of controls around advanced models, compute and related investment, all reshaping the global AI supply chain. Situated in Hong Kong, routed.hk conducts model aggregation and outbound services under the local legal framework, offering a clear path for compliant outbound of domestic models and compliant inbound of overseas models.

It must be emphasized that compliance is never a one-off “check-the-box” act, but a “dynamic process” that evolves with policy. The significance of the dual-domain architecture is precisely that it distills “compliance capability” into a native attribute of the platform — through site-level domain separation, data governance by domain and unified interfaces, it lets enterprises always retain a clear, auditable and adjustable compliance path between two rapidly changing regulatory systems, without having to rebuild their technical architecture at every policy update.

The Convergence of Three Advantages

In summary, the advantages of dual-domain MaaS across cross-border data, model competition and compliance can be distilled into four points:

These three variables were originally three barriers standing before cross-border AI; under the dual-domain architecture, they are gathered into one coherent product logic — which is precisely the distinctive answer WLStack Network offers, starting from its network DNA.

Reactions from the Industry

Almost every technical team doing cross-border business has been troubled by the “two separate AI systems, at home and abroad” problem. Compliance done separately, interfaces integrated separately, operations managed separately — enormous effort wasted reinventing the wheel. WLStack’s dual-domain MaaS hits this pain point precisely; integrating once to cover both domains not only saves integration cost, it takes the iteration speed of our AI business to a new level.

— CTO of a cross-border technology company

Many think MaaS is just piling up model APIs, but anyone who has actually run production-grade business knows that network latency, jitter and disaster recovery are the bottom line that decides whether a service is usable. WLStack has run backbone networks for so many years; distilling network-scheduling capability into MaaS is like giving model services a carrier-grade foundation. This is not a simple feature add-on — it is a dimensional advantage at the architectural level.

— Senior architect at a cloud-computing vendor

Domestic large models have reached the world’s first tier in capability, but going global has always faced shortfalls in service nodes, network links and localization support. routed.hk is like a standardized express lane for domestic models going global — it lets global developers use China’s AI capabilities more easily, and spares model vendors the cost of building an overseas service system from scratch. The long-term value of this is immense.

— Chief analyst at an AI industry research institute

Our team has long worked on multi-model applications, and our biggest headache has been non-uniform interfaces, high switching costs, and having to solve overseas models’ network problems on our own. This platform bundles standardized interfaces and network optimization together — it shoulders the dirty, heavy work at the bottom, so we can focus on the business logic itself.

— Technical lead of an AI application startup

What This Means for the Industry

The launch of this platform marks the entry of AI infrastructure services into a new stage of “native network-and-model fusion.” Before this, model services and network services were two separate tracks: model vendors focused on algorithm iteration, carriers focused on link transmission, and customers stitched them together in the middle, stumbling repeatedly. WLStack Network’s attempt makes network capability, for the first time, a built-in attribute of model services rather than a bolt-on afterthought.

The changes it brings will radiate across many links in the industry:

• For domestic enterprises, the barrier to AI adoption is greatly lowered. With no need to compare vendors’ compliance credentials one by one, no repeated integration and tuning, and no building of multiple operating systems, they obtain full-category domestic compliant model capabilities in one stop — freeing them to focus on business innovation rather than infrastructure assembly.
• For going-global enterprises, the complexity of global AI deployment is greatly reduced. Bidding farewell to “one system at home, another overseas,” they manage domestic and overseas model calls through a unified platform, interface and bill, achieving global unity in their business architecture.
• For domestic model vendors, a mature outbound service foundation becomes available. Without building overseas nodes, sales systems and technical service teams from scratch, they can reach the global market quickly by relying on the platform’s network and customer resources, and focus more on the technical iteration of the models themselves.
• For global developers, there is one more efficient gateway to China’s AI capabilities. At the same time, relying on optimized network links, they can stably invoke mainstream global models and enjoy a smoother development experience.

Just as the solution to a classic geometry problem opened the door to interdisciplinary research, the arrival of the routed series of platforms opens fresh room for imagination at the intersection of network engineering and AI services. In the future, more network-optimization-based innovations in AI services may well grow from this foundation.

A More Far-Reaching Significance

The value of this goes far beyond the launch of a new product.

As the AI industry gradually shifts from a “race of model parameters” to a “race of adoption value,” the importance of infrastructure becomes ever more apparent. Stable network links, clear compliance paths, flexible scheduling and controllable usage cost — these seemingly unglamorous engineering capabilities are exactly what decide how far AI technology can go, how many scenarios it can cover, and how many enterprises’ production systems it can truly enter.

WLStack Network, starting from the network domain it knows best and extending into the AI service layer, is filling in a crucial piece of the industrial puzzle. We believe the AI infrastructure of the future will be a deep, three-in-one fusion of compute, network and model. The launch of routed.cn and routed.hk is a solid step in that direction.

More powerful AI infrastructure can make AI a more capable partner for industry: it can lower the barrier to innovation so more enterprises are not tripped up by underlying infrastructure; it can connect scattered resources so the world’s AI capabilities flow smoothly to where they are needed; and it can support more complex business scenarios, helping enterprises make progress in directions that were once too costly or too architecturally complex to pursue.

This value transcends model services themselves. If a platform can break through the barrier between compliance and globalization, natively combine network capability with model services, and make AI adoption lower-cost, more efficient and more stable, then it will deliver enormous value across manufacturing, finance, retail, healthcare, education and every other industry. And that is an indispensable step in the long journey toward the full industrialization of AI.

The grand blueprint of artificial intelligence must ultimately be carried by layer upon layer of solid infrastructure. As model capabilities keep breaking through, there must equally be those who settle down to lay the access links more smoothly, build the service foundation more soundly, and make global connections more stable.

This road — we have only just set out.

2026 · WLStack Network