Akamai Technologies (NASDAQ: AKAM), the cybersecurity and cloud computing company that powers and protects business online, today announced it has entered into an agreement with VAST Data, the AI data platform company, to make data-intensive AI inferencing faster and more efficient.
The collaboration will marry Akamai’s massive distributed platform with the VAST Data Platform’s breakthrough approach to data-intensive computing to integrate data for large, distributed customers that need better local response time and localization for distributed inference.
Latency, cost, and scalability are hurdles to deploying AI-powered applications at scale. Unlike training, which is resource-intensive but happens in controlled environments, inference must operate continuously, on-demand, and often with ultra-low latency. Adding VAST Data to the Akamai Cloud aims to lower costs and improve customer experience to help democratize edge AI computing.
While many cloud service providers support large-scale centralized AI training, they do not operate inference at the scale and distribution for planetary-scale service. As the world’s most distributed cloud, Akamai is uniquely positioned to deliver AI inference at the edge, helping enterprises meet local response time and localization requirements.  With VAST, Akamai Cloud users can benefit from consistency and fast, secure access to all of their organization’s data, from edge to cloud, via a single global namespace.
“With this agreement, Akamai and VAST Data will deliver next-generation AI infrastructure that combines hyperscale performance, security, and efficiency to meet the growing demands of enterprise AI workloads, closer to the digital touchpoint,” said Jeff Denworth, Co-Founder of VAST Data. “Together, we are providing organizations with an AI-ready data platform that ensures seamless scalability, intelligent automation, and enterprise-grade security for the most demanding AI and inference applications.”
The agreement with VAST Data helps advance Akamai’s vision for a more distributed cloud model at a time when even centralized architectures are beginning to struggle to adapt to the demands of an edge-centric future.