Introduction
The rise of AI-driven workloads—spanning machine learning, generative models, and high-performance computing (HPC)—has fueled demand for specialized AI clouds optimized for GPU-intensive tasks. However, integrating these platforms into existing hybrid multi-cloud architectures poses significant challenges for enterprises. From on-premises systems to public clouds, organizations need a networking solution that ensures seamless connectivity to AI clouds without sacrificing performance or security. Alkira’s Network Infrastructure-as-a-Service (NIaaS) platform addresses this @need, enabling businesses to extend their hybrid multi-cloud networks into AI clouds with agility, security, simplicity, and scale. This white paper explores the key benefits of using Alkira to unlock the full potential of AI cloud adoption.
The Challenge of Hybrid Multicloud Networking with AI Clouds
AI clouds offer unparalleled compute power, high-speed networking, and managed services tailored for advanced workloads. Yet, enterprises face hurdles when connecting these environments to their broader infrastructure:
- Complexity: Traditional networking requires manual setup of VPNs, private interconnects, or SD-WAN overlays, delaying deployment and increasing operational overhead.
- Security: Maintaining consistent policies across on-premises, public clouds, and AI clouds while protecting sensitive data is critical yet challenging.
- Scalability: AI workloads often require rapid scaling, demanding networks that adapt without bottlenecks or latency.
- Management: Disparate tools across environments hinder visibility and control, complicating operations and troubleshooting.
Alkira’s Network Infrastructure as a Service platform overcomes these obstacles, delivering a unified, on-demand networking solution for AI cloud integration.
Benefits of Using Alkira for AI Cloud Integration
1. Agility: Rapid Deployment and Adaptability
Alkira enables organizations to connect their hybrid multicloud networks to AI clouds in minutes. Its intuitive, point-and-click interface allows network architects to design and provision connectivity on a digital canvas, linking on-premises data centers, public clouds (e.g., AWS, Azure, GCP), and AI cloud providers via Alkira’s global Cloud Exchange Points (CXPs). This eliminates hardware dependencies, manual configurations, and lengthy setup times. For AI-driven businesses, this agility accelerates deployment of compute-intensive workloads, keeping pace with innovation cycles.
2. Security: Built-In Protection Across Environments
Security is a top priority when extending networks to AI clouds, particularly for applications handling proprietary models or sensitive data. Alkira offers end-to-end encryption, advanced segmentation, and Zero Trust Network Access (ZTNA) natively within its platform. Integration with leading security vendors—such as Check Point, Cisco, Fortinet, and Palo Alto Networks—enables customers to deploy next-generation firewalls that auto-scale with demand. This ensures uniform security policies across hybrid multicloud environments and AI clouds, protecting workloads without sacrificing performance.
3. Simplicity: Unified Management and Visibility
Alkira simplifies hybrid multicloud networking complexity, segmentation, resource sharing, policies and routing controls with a single pane of glass for design, provisioning, and management. Customers can onboard AI clouds alongside existing infrastructure, using standardized protocols and intent-based policies to streamline connectivity. Comprehensive visibility into network performance, traffic flows, and security posture reduces troubleshooting time and eliminates operational silos. For IT teams, this simplicity frees resources from infrastructure management to focus on leveraging AI clouds for strategic outcomes.
4. Scale: Elastic, On-Demand Networking
AI workloads—such as model training or real-time inference—demand networks that scale seamlessly with compute requirements. Alkira’s consumption-based Network Infrastructure-as-a-Service (NIaaS) model provides elastic scalability, enabling customers to expand connectivity as workloads grow, without capacity limits or upfront hardware costs. Leveraging Alkira’s global CXPs and high-performance backbone, organizations can connect multiple regions, clouds, and AI providers, ensuring low-latency, high-throughput networking that matches the dynamic nature of AI clouds.
5. Cost Efficiency: Optimized Resource Utilization
Traditional networking approaches like MPLS or custom interconnects are costly and slow to deploy. Alkira’s pay-as-you-go model eliminates CapEx, allowing customers to consume networking resources on demand. By optimizing data paths and reducing redundant infrastructure, Alkira lowers operational costs while maximizing the efficiency of AI cloud investments. This cost-effective approach supports enterprises scaling AI initiatives without financial strain.
Use Cases for AI Clouds
Use Case 1: AI-Driven Enterprise Adoption
Imagine a global enterprise developing AI models while maintaining data lakes in a public cloud and regulated workloads on-premises. Alkira connects these environments via private, secure links, integrating an AI cloud’s GPU resources with the public cloud’s storage and on-premises systems. The IT team provisions connectivity in under an hour through Alkira’s portal, applies consistent security policies, and scales bandwidth as AI demands surge—all without hardware or complex setups. This enables the enterprise to accelerate AI innovation while preserving its hybrid multicloud networking strategy.
Use Case 2: Healthcare AI for Drug Discovery
A pharmaceutical company leverages an AI cloud to run molecular simulations for drug discovery while storing proprietary research data on-premises and using a public cloud for collaboration tools. Alkira integrates these environments, providing low-latency connectivity to the AI cloud’s compute resources and enforcing strict segmentation to protect sensitive intellectual property. Researchers gain rapid access to GPU clusters for simulations, and Alkira’s visibility tools help IT monitor compliance, speeding up the path to market for new treatments.
Use Case 3: Media and Entertainment Rendering
A media studio uses an AI cloud for real-time rendering of high-definition animations, with assets stored in a public cloud and editing suites on-premises. Alkira connects these systems, delivering high-throughput networking to handle large data transfers between the AI cloud and other environments. The studio scales connectivity during peak production phases, and Alkira’s cost-efficient model ensures budget predictability, enabling faster content delivery to audiences.
Use Case 4: Financial Services Risk Modeling
A financial institution employs an AI cloud for real-time risk modeling and fraud detection while keeping transactional data in a private cloud and customer records on-premises. Alkira unifies these environments with secure, low-latency connections, integrating the AI cloud’s processing power into the institution’s hybrid multi-cloud setup. Intent-based policies simplify compliance with regulatory requirements, and elastic scaling supports sudden increases in modeling demand during market volatility.
Conclusion
Alkira’s Network Infrastructure-as-a-Service (NIaaS) transforms how enterprises extend hybrid multicloud networks into AI clouds. By delivering agility, security, simplicity, and scale, Alkira eliminates adoption barriers, empowering organizations to harness AI cloud capabilities efficiently and cost-effectively. As AI continues to redefine business landscapes, Alkira provides the networking foundation to connect, secure, and scale these workloads across any environment. For enterprises aiming to lead in the AI era, Alkira offers a powerful, future-ready solution.
