What is NaaS and Why Does AI Need It?

Network-as-a-Service (NaaS), as its name implies, aims to make the procurement and use of connectivity and networks comparable to using any cloud service. To that point, NaaS brings a type of on-demand service subscription model to the market.

Typically, providers that embrace NaaS also bundle in additional capabilities. For example, most offer enhanced service-based security features such as Secure Access Service Edge (SASE), which is a cloud-delivered framework that combines wide area networking (WAN) capabilities with comprehensive network security functions such as secure web gateways, zero trust network access (ZTNA), firewall-as-a-service (FWaaS), and cloud access security broker (CASB).

In essence, NaaS provides the connectivity, while SASE ensures that connectivity is secure, creating a unified service for modern digital and AI-driven enterprises.

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What are the key benefits of NaaS?

Network-as-a-Service (NaaS) provides a modern, cloud-based approach to networking, enabling enterprises to operate with greater agility, efficiency, and focus. By shifting networking from a hardware-intensive model to a service-based one, NaaS removes the burden of purchasing, maintaining, and upgrading internetworking equipment and physical infrastructure. That allows organizations to allocate more time and resources to their core business goals, while still benefiting from enterprise-grade connectivity and security.

One of the most significant benefits of NaaS is cost efficiency. Instead of large, upfront investments in networking equipment, businesses pay predictable monthly fees or usage-based rates. This subscription model makes budgeting easier and reduces capital expenditures.

NaaS also provides scalability, allowing companies to quickly adjust bandwidth, capacity, and services to meet changing needs. All of this can be accomplished without the delays or expenses of purchasing new hardware.

Beyond cost and flexibility, NaaS simplifies network management. Providers typically offer centralized, cloud-based portals that give IT teams real-time visibility and control over network performance, configuration, and security. NaaS also provides access to the latest technologies, including software-defined networking (SDN), automation, and AI-driven optimization.

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NaaS considerations

Network-as-Service is internet-delivered, offering global reach and faster deployment. That allows organizations to extend their operations anywhere, enable remote workforces, and bring new services to market faster.

However, there are several issues to consider when seeking NaaS solutions. To start, supporting such global connectivity often requires the involvement of multiple providers using equipment from different technology vendors. That makes the automation of business and operational functions critical.

These are areas that the global association Mplify (formerly MEF) is working on. Mplify has developed lifecycle service orchestration (LSO) APIs and frameworks that enable service providers to expose network services (such as Ethernet, IP, SD-WAN, SASE, etc.) in a standardized manner. The LSOs streamline and automate the coordination, management, and control of services across all entities responsible for delivering an end-to-end connectivity service.

Some LSOs facilitate the conveyance, sharing, and exchange of billing information, service conditions, and more. Additionally, Mplify LSO APIs provide the needed business and operational automation between the multiple parties required to provide a cloud service provider or enterprise with connectivity.

The bottom line is that with Mplify’s standards, enterprises can order, activate, monitor, and assure network services across multiple operators as if they were dealing with a single cloud provider.

See also: AI Workloads Need Purpose-built Infrastructure

Why is NaaS important for AI and edge intelligence?

Increasingly, AI, edge computing, and GPU acceleration applications are highly distributed and resource-intensive. As such, they have special connectivity requirements. And in many ways, NaaS can help.

For example, AI workloads may burst between data centers, clouds, and edge sites. NaaS lets organizations spin up high-bandwidth links only when needed to connect such sites.

Applications like inference at the edge (e.g., in manufacturing or autonomous systems) need deterministic, reliable networking. Network-as-a-Service allows policy-based performance guarantees.

With distributed GPUs and edge nodes, the attack surface expands. NaaS integrates security (SASE, ZTNA) as part of the service fabric.

If GPUs are offered as on-demand cloud services, in a so-called GPUs-as-a-Service model, they need equally elastic, high-bandwidth interconnects to be viable. NaaS ensures that moving large AI datasets and model parameters doesn’t become the bottleneck.

A final word

NaaS is the networking equivalent of cloud computing. Standards and APIs from industry groups like Mplify and others make NaaS globally usable.

For AI, edge, and GPU services, NaaS is the connective tissue that ensures these high-performance resources can be consumed elastically and securely.