The power of optionality in platform engineering in the age of AI
The speed of AI has many dimensions. There is the speed of model improvement (which admittedly took a hit with GPT 5). There is the speed hardware development, led by Chief Revenue Destroyer himself, Jensen Huang of Nvidia but joined by a host of others on the TPU, DPU and xPU front. There is the speed of application innovation from image generation to agentic AI. There is the ecosystem innovation which include MCP, LlamaIndex Query Engine Abstraction, and LSP.
For technical leaders and engineers, the challenge goes beyond keeping up, it essentially requires them to predict the future. Since that is not a realistic requirement, the fallback is to build a foundation that can adapt to this dynamic landscape.
This requires a sophisticated and informed architectural approach. The concept of building for flexibility is not new, it is an engineering first principle. But building a platform that prioritizes optionality while acknowledging the current state of affairs (e.g. the dominance of NVIDIA), is a material challenge.
In the context of AI infrastructure, that means building for Nvidia while simultaneously adopting architectures and protocols that allow for the integration of new technologies and the independence of the roadmap.
For instance, consider platforms built on generic cloud stacks like OpenStack or more generic ones like hypervisors that lack frameworks to live in an AI world. Traditional file storage would be another example, unable to deliver throughput and scale. Each of these offers a cautionary tale - current adoption does not equate to future adoption. AI infrastructure and AI software stacks are ruthless in this regard.
A Homegrown Approach to AI Infrastructure
The Ori Platform is a prime example of this engineering-first philosophy in action. It’s built entirely in-house, with no dependencies on third-party cloud frameworks or managed services that introduce rigid roadmaps and lock-in. All core services—including the control plane, scheduler, cluster OS, and automation logic—are purpose-built for AI and High-Performance Computing (HPC) workloads.
At the same time, Ori does not reinvent the wheel. It is built on proven, well-audited open-source components such as vanilla Kubernetes, standard Linux distributions and established storage frameworks. The differentiation is not secrecy but integration: brittle glue layers are replaced with tightly coupled orchestration, keeping the foundation transparent and verifiable while reducing supply-chain risk. This allows Ori to retain the benefits of community scrutiny and security hardening while optimizing the stack for AI/HPC. The platform's innovation comes from its custom orchestration logic and control plane, which sits on top of these trusted open-source building blocks without being dependent on a vendor-specific distribution.
The architecture cleanly separates business logic from vendor hardware APIs. This separation is key to its flexibility, allowing the rapid integration of new GPUs, storage systems, and network fabrics without waiting for external projects to evolve.
This design principle provides complete roadmap independence, enabling Ori and its customers to adopt new hardware and infrastructure patterns at their own pace. It also eliminates the licensing costs and limitations imposed by generic cloud stacks, reducing the total cost of ownership and ensuring a more efficient deployment.In practice, we have seen this translate into measurable savings: customers running Ori clusters need a fraction of the infra-management headcount compared to OpenStack-based deployments, and node recovery times drop from hours to minutes. These operational efficiencies compound at scale - making them all the more important in the TCO calculations.
Furthermore, a purpose-built, in-house developed platform significantly reduces security and supply-chain risk by removing external dependencies. In an era of increasing cyber threats and geopolitical instability, a platform with minimal third-party exposure is a strategic advantage, particularly for those with an interest in data and platform sovereignty.
Another practical benefit is the platform's agility and scalability. Because it is lightweight and purpose-built, it can be deployed from single-rack pilots to hyperscale GPU superpods. This makes it ideal for a wide range of users, from public cloud users to private-cloud operators and partners who want to start small and scale rapidly without having to completely re-platform.
Embracing the Dynamism of AI and the Dominance of NVIDIA
The AI ecosystem is constantly in flux. New models, algorithms, and hardware are released every week. A platform must be able to embrace this dynamism, not be hindered by it. A platform built for the challenges of AI provides the agility to do just that.
This dynamism also means acknowledging the realities of the market. Currently, NVIDIA's dominance in the GPU space is undeniable. Their GPUs are the de facto standard for AI training and inference, and their ecosystem, from CUDA to their software libraries, is the backbone of the industry. A flexible platform must be able to support and rapidly enable new NVIDIA GPU generations.
The Ori Platform is a testament to this capability. It operates thousands of GPUs across multiple continents, and its architecture allows for the rapid enablement of new NVIDIA GPUs within weeks of their release. This ability to integrate the latest hardware from the market leader is a significant competitive advantage. It ensures that users always have access to the most performant and cutting-edge technology without being bottlenecked by a third-party framework's update cycle.
The engineering-first principle of building for flexibility means you don't just support one vendor; you can support many. This does not mean supporting every possible accelerator or fabric indiscriminately. Optionality at Ori is tightly scoped and unified by a single control plane, so operators don’t inherit complexity. Customers get the benefits of flexibility without the fragmentation of juggling multiple incompatible stacks. For example, Ori supports AMD Instinct GPUs and Groq LPU silicon. We will work with customers on adding to that portfolio. It is not just silicon. Customer deployments have already demonstrated successful integration of new storage back-ends and network fabrics without requiring a complete architectural overhaul.
The Challenges of Off-the-Shelf Frameworks
While off-the-shelf frameworks like OpenStack or Kubernetes distributions tied to vendor roadmaps can seem appealing on the surface, they often introduce significant overhead that translates into a variety of problems. To be clear, we are not anti–Kubernetes or anti–open source. Ori runs on upstream Kubernetes and integrates cleanly with the broader CNCF ecosystem. What we avoid is the extra scaffolding, heavily modified Kubernetes distributions, OpenStack overlays, or vendor-specific lifecycle managers that introduce bloat and AI-blind abstractions. In other words: we keep the open-source strengths while stripping away the layers that slow AI workloads down.
The generic nature of these frameworks means they are not optimized for the specific, high-performance needs of AI/HPC workloads. This can lead to inefficient resource utilization, higher latency, and ultimately, slower time to results. A homegrown platform, built from the ground up for these specific use cases, can eliminate this unnecessary overhead, ensuring every bit of compute is utilized effectively.
Moreover, too much dependency on foreign software makes it difficult to compete for sovereign projects. Many governments and large enterprises require solutions with minimal external dependencies to ensure data security and national sovereignty.
Conclusion
The future of AI infrastructure belongs to platforms built on the principles of flexibility, optionality, and engineering excellence. At Ori, our platform demonstrates that a homegrown approach, free from the constraints of third-party frameworks, provides the agility and control needed to navigate the dynamic AI landscape. By separating business logic from hardware APIs, it allows for rapid innovation and seamless integration of new technologies, including the latest GPUs from NVIDIA, while also preparing for the future of alternative accelerators.
This approach not only reduces cost and risk but also empowers organizations to set their own pace, ensuring they can leverage the latest AI advancements without waiting for an external roadmap to catch up. For technical leaders and engineers, embracing this philosophy is not just a strategic choice—it is a prerequisite for building a truly future-proof AI-ready platform.
