Seismora Develops Control Plane to Route AI Workloads Across Devices and Clouds

Palermo described the effort in an interview with John Furrier for theCUBE + NYSE Wired: AI Luminaries interview series on theCUBE, SiliconANGLE Media's livestreaming studio. He said the AI boom is moving communication from human-to-system to machine-to-machine. 'What we're building at Seismora is an intelligent control plane that allows AI traffic to move across the network, regardless of the network, regardless of who the neocloud is or the hyperscaler, or even the devices themselves,' he said.

According to Palermo, different parts of an AI application may require different computing resources. Seismora's control plane coordinates that work across devices, edge infrastructure and cloud providers. He said a distributed control plane can make decisions based on user intent and application needs. 'In a distributed control plane, the system becomes smart enough to say, Okay, based on the intent of this user for this application, we can do X amount of work here on the iPhone, we can do Y amount of work here at the edge,' Palermo told theCUBE. For specific parts, he said, a developer might want to build a digital twin using Nvidia Corp.'s Omniverse in a neocloud, and the control plane would automatically make those decisions and optimize cost.

Palermo pointed to Stripe Inc.'s agreement to acquire OpenRouter Inc., an AI model routing platform, as evidence of growing interest in infrastructure for machine-to-machine transactions. The August announcement involved a price reportedly around $7.5 billion. He said enterprises today work with multiple neoclouds and multiple models. 'You want to be heterogeneous because, frankly, the enterprise today, I'm sure they have multiple neoclouds they work with, they have multiple models they work with,' Palermo said. 'My problem is routing across all of these providers, and that's the focus.'

Seismora calls its approach 'cognitive routing': choosing where AI work runs based on capability, cost, latency and policy constraints. Palermo said the intended customers are developers building agentic applications for end users, rather than consumers themselves. He described a world with open-weight models from providers such as Fireworks.ai Inc. and contextual knowledge from Neo4j, with compute and storage distributed across the edge and the enterprise. 'Then, having compute and storage distributed across the edge, across the enterprise, and to be able to move intelligence where it makes sense,' he explained.

The interview was part of SiliconANGLE's and theCUBE's coverage of theCUBE + NYSE Wired: AI Luminaries interview series. TheCUBE is a paid media partner for the series; Neo4j, the sponsor of theCUBE's event coverage, and other sponsors do not have editorial control over content on theCUBE or SiliconANGLE, according to the disclosure.