Most AI platforms bind organizations to a single cloud provider because they depend on proprietary storage layers, networking APIs, identity systems, and managed services that exist only inside that provider’s ecosystem. Once workloads adopt these components, migrating them becomes costly and disruptive, and the cloud provider’s pricing, regional availability, and policy decisions start shaping the organization’s entire infrastructure strategy. Over time, the platform no longer runs in the cloud — it runs on that cloud, and moving away becomes nearly impossible.
Super eliminates this dependency by providing an execution layer that runs the same way across all major public clouds, private data centers, regional providers, and on-prem clusters. It does not rely on vendor-specific services or cloud-native abstractions, and enclaves behave identically regardless of where they are deployed. Workloads can be shifted between environments without modification, and the surrounding Super components remain portable, version-consistent, and independent of provider APIs.
For clients, this restores genuine deployment freedom. They can relocate workloads to meet regulatory requirements, optimize costs, improve latency, or reduce geopolitical exposure without rearchitecting their systems. Cloud choice becomes a strategic tool rather than a technical constraint, and organizations regain the ability to shape their own infrastructure roadmap instead of inheriting the limitations of a single vendor.
Most AI platforms are closed systems. Their internal mechanisms, security decisions, and runtime behavior are hidden from customers, and using them means accepting the vendor’s assurances without being able to verify anything yourself. This creates long-term risk: if the vendor changes strategy, pricing, or technology — or if the company simply isn’t around in a few years — the customer is locked into an opaque foundation they cannot control.
Super avoids this problem by being open source. The core execution layer, the components that validate workloads, and the mechanisms that enforce confidentiality are all transparent and available for inspection. Organizations can see exactly how the system works, audit the security model, and run the platform entirely inside their own environment. Nothing depends on hidden services or internal operators controlled by Super.
For clients, this removes vendor lock-in and eliminates the dependency on a startup’s longevity. Even if Super as a company changes direction, the platform itself remains usable, inspectable, and maintainable. The organization keeps full control of its confidential-compute foundation, with the ability to audit it, extend it, or operate it independently. Open source turns Super from a vendor product into a stable architectural choice.
In most cloud environments, the platform operator retains fundamental control: they manage encryption keys, maintain privileged access roles, determine how workloads run, and ultimately hold the authority to inspect, pause, or alter execution. Even when confidentiality features exist, the client still relies on the vendor’s policies, monitoring systems, and internal teams, which leaves sensitive workloads dependent on someone else’s trust boundaries.
Super reverses this relationship. Clients run Super in their chosen cloud or private environment, manage their own encryption keys, and maintain exclusive authority over infrastructure boundaries. The platform does not require privileged roles, operator access, or backend visibility to function. Data remains sealed inside enclaves that only the client controls, and the system enforces a model where Super itself cannot override or bypass the client’s security posture.
For organizations, this results in true ownership: full control over deployment, data residency, key management, and operational governance. They are not forced to relinquish authority to a platform vendor, and they do not inherit hidden dependencies that compromise internal policies or regulatory requirements. Self-sovereign control ensures that critical AI workloads run on terms defined entirely by the client — not by a third-party provider.
