Microsoft and AWS launch Azure Multicloud Interconnect for 100 Gbps networking

The launch of Azure Multicloud Interconnect for AWS marks a significant shift toward interoperability between the two largest cloud providers, directly addressing the friction of managing split-cloud architectures. For streaming organizations, this reduces the latency and complexity of moving massive video libraries or metadata between disparate storage and compute environments. By abstracting the physical networking layer, engineers can focus on application performance rather than manual cross-cloud provisioning. As the industry moves toward an open ecosystem, watch for other hyperscalers to adopt these Open API specifications to create a unified multicloud fabric for global content delivery.

Microsoft and AWS are not the only hyperscalers racing to simplify cross-cloud connectivity for data-intensive workloads. In June 2026, Nokia combined with AWS and Databricks to build a unified data and control layer for autonomous networks, demonstrating how cloud-native orchestration fabrics are becoming the connective tissue between previously siloed infrastructure domains. Nokia's Autonomous Network Fabric, which runs on AWS and integrates with Databricks for unified data processing, represents the same architectural philosophy behind Azure Multicloud Interconnect: abstracting the networking and data layers so that operators and enterprises can move workloads across environments without rewriting code. The parallel is instructive for streaming teams evaluating whether to consolidate on a single cloud or maintain split architectures for resilience and cost optimization.

The competitive dynamics between Microsoft and AWS in multicloud networking are intensifying as both companies court AI and media workloads simultaneously. Ericsson launched its AI in RAN commercial software subscription on June 11, claiming up to 20% higher downlink throughput across more than 15 live deployments, illustrating how telecom vendors are also building proprietary acceleration layers that depend on high-bandwidth cloud interconnects for model training and inference. For streaming platforms that rely on both Azure and AWS for different stages of their pipeline, from transcoding to CDN distribution, the availability of 100 Gbps private links removes a key bottleneck in training recommendation models on viewing data stored across both clouds. The Open API specification approach signals that Microsoft intends to extend the interconnect pattern beyond AWS, potentially creating a standardized fabric that spans three or more hyperscalers.

On the technical side, the performance claims around Azure Multicloud Interconnect align with broader industry benchmarks for GPU-accelerated workloads that demand low-latency cross-cloud data movement. Ericsson and Nokia are diverging sharply on AI-RAN architecture, with Nokia running all Layer 1 functions on Nvidia GPUs while Ericsson reserves the GPU only for forward error correction, a split that mirrors the streaming industry's own debate over whether to centralize compute in one cloud or distribute it across providers for proximity to end users. Nokia's GPU-first approach, adopted by T-Mobile US, SoftBank, and Vodafone, requires massive data pipelines between training clusters and inference nodes, exactly the kind of workload that benefits from dedicated high-bandwidth interconnects. For streaming engineers, the practical implication is that Azure Multicloud Interconnect could enable real-time model synchronization between Azure-hosted personalization engines and without the latency penalties of public internet transit.