Seismora Develops Control Plane to Route AI Tasks Across Clouds and Devices
Seismora is building an intelligent control plane to automate AI task routing across local hardware, edge infrastructure, and hyperscalers based on cost and latency.
As artificial intelligence applications grow more sophisticated, allocating computing tasks efficiently has become a significant technical challenge. Start-up Seismora Inc. is building networking software designed to coordinate AI workloads across fragmented environments, including end-user hardware, edge servers, neoclouds, and hyperscale cloud providers. According to founder and chief executive officer Vito Palermo, the goal is to relieve software engineers from having to hardcode complex routing logic directly into their codebases.
Palermo shared details of the architecture during an interview on SiliconANGLE's theCUBE livestreaming studio with host John Furrier. He explained that computing requirements are shifting rapidly from traditional human-to-system interactions to direct machine-to-machine operations. Seismora aims to serve as an intelligent control plane that directs AI network traffic regardless of underlying hardware, cloud vendors, or physical network infrastructure.
Cognitive Routing Across Heterogeneous Networks
AI systems rarely rely on a single execution layer. Different components of a modern application demand different trade-offs in compute power, memory, and responsiveness. Seismora terms its method "cognitive routing," evaluating intent, latency limits, cost parameters, and enterprise policies to determine where each segment of an AI task should execute.
Under this model, an application might process initial inputs directly on a user's mobile device, such as an iPhone, before offloading intermediate steps to edge infrastructure. Heavy processing tasks—such as generating a digital twin inside Nvidia Corp.'s Omniverse—can automatically be sent to a specialized neocloud provider. The control plane handles these decisions autonomously in the background, balancing performance constraints while actively optimizing financial costs.
Palermo highlighted that real-world enterprises do not operate on a single platform. Organizations regularly manage diverse configurations involving open-weight models from hosting providers like Fireworks.ai Inc., graph and contextual databases from Neo4j, and multiple infrastructure vendors. To illustrate the industry's shifting focus toward automated distribution, Palermo referenced Stripe Inc.'s agreement to acquire AI routing platform OpenRouter Inc. for a reported valuation of roughly $7.5 billion.
What it means for developers
For engineers designing agentic applications, the emergence of intelligent control planes represents a shift away from static cloud hosting. Instead of locking an entire pipeline into an expensive centralized data center, developers can divide agentic workloads across distributed tiers. Lightweight inferences or preliminary filtering can happen locally or at the edge, saving expensive cloud computing time for heavy model calls.
Navigating this multi-provider ecosystem also requires practical access to diverse model APIs. Developers exploring distributed architectures can try top AI models cheaply through one API at https://apixoai.online, making it simpler to test and evaluate various foundation models without managing separate vendor contracts.
Ultimately, Seismora is tailoring its technology specifically for developers creating agentic software rather than retail consumers. By offloading routing logic to a dedicated network layer, development teams can connect contextual data, open-weight models, and specialized accelerators across the enterprise while retaining the flexibility to send workloads wherever execution is most effective.
Source: Seismora builds a control plane to route AI workloads across devices and clouds — SiliconANGLE AI. Written by the Apixo team from that report.
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