EasyStack has launched EasyStack EAF, an enterprise AI infrastructure software platform designed to extend existing cloud environments into AI-ready infrastructure without requiring a separate AI software stack. Scheduled for general availability on September 30, 2026, EAF targets enterprises managing heterogeneous AI accelerators, inference workloads and the growing operational costs of deploying AI at scale.
EasyStack is taking aim at a growing problem in enterprise AI infrastructure: how to add accelerated computing and AI workloads to existing cloud environments without creating another disconnected infrastructure layer.
The enterprise cloud and AI infrastructure software provider has officially launched EasyStack EAF, a platform intended to help organizations move from cloud-native infrastructure toward what the company calls AI-native infrastructure. General availability is scheduled for September 30, 2026.
Rather than positioning EAF as a collection of AI tools sitting on top of a conventional cloud platform, EasyStack says the software is designed to extend an existing cloud foundation with AI infrastructure capabilities. The approach reflects a broader shift in enterprise computing as organizations move beyond AI proofs of concept and begin dealing with production inference, accelerator utilization, governance and cost allocation.
Those challenges are becoming more significant as AI workloads consume an increasing share of infrastructure budgets. Gartner forecasts worldwide AI spending of $2.59 trillion in 2026, up 47% year over year, with AI infrastructure accounting for more than 45% of total AI spending.
EAF focuses particularly on heterogeneous accelerator environments. According to EasyStack, the platform can manage multiple chipset architectures within a single cloud and supports AI accelerators from NVIDIA, Hygon DCU and Huawei Ascend.
That hardware-neutral approach is increasingly relevant as enterprises contend with different accelerator architectures, availability constraints and workload requirements. Instead of designing infrastructure around a single AI processor vendor, organizations can potentially manage different resources through a common software layer.
EAF also includes mechanisms for virtual partitioning and inference optimization. EasyStack says its multi-layer optimization capabilities are intended to improve accelerator utilization, while tracking inference-token consumption gives IT teams a way to monitor how AI workloads consume computing resources.
The platform also adds governance and automated cost allocation across departments. That puts EAF closer to an AI infrastructure management layer than a conventional GPU management tool. For enterprises operating shared AI environments, the ability to identify resource consumption and assign costs to individual teams can become important as AI moves from experimentation into everyday business applications.
Deployment is another focus. EasyStack says EAF supports three configurations: a single-node appliance, a high-availability converged model and a large-scale disaggregated architecture.
The configurations are designed to accommodate different stages of AI adoption, from proof-of-concept environments to multi-tenant production inference. The platform also supports fine-tuning workloads, according to the company.
That progression mirrors a wider change in AI infrastructure requirements. Gartner estimates that global spending on AI-optimized infrastructure as a service will reach $42.3 billion in 2026, representing 96.4% growth from 2025. Gartner also forecasts inference spending at $23.3 billion in 2026, exceeding the $19 billion expected for training.
As inference becomes a larger part of enterprise AI operations, infrastructure software has to address more than model deployment. Capacity management, latency, accelerator utilization, security policies and cost visibility increasingly become part of the production AI stack.
EasyStack is positioning EAF as the AI infrastructure component of its broader cloud portfolio. The platform works with EasyStack’s ECF cloud foundation, ECNF cloud-native platform and Cortex enterprise agentic platform, creating a stack that extends from infrastructure and cloud-native operations to AI agents and business applications.
The company says EAF operationalizes mainstream open-source technologies and uses a per-AI-card licensing model. It is also designed to remain independent of particular hardware or AI models, according to EasyStack.
That strategy places EAF in a competitive market spanning cloud infrastructure providers, GPU orchestration software, AI platforms and enterprise Kubernetes ecosystems. Microsoft, Google and Amazon all provide infrastructure and managed AI services, while NVIDIA has expanded its software stack around accelerated computing and AI deployment. The distinction for infrastructure vendors such as EasyStack is increasingly about how much control enterprises retain over their existing infrastructure and how easily heterogeneous hardware can be incorporated.
IDC estimates that worldwide AI infrastructure spending reached $318 billion in 2025 and forecasts the market to exceed $1 trillion by 2029. The research firm expects continued spending across accelerated computing, servers and the supporting infrastructure needed for AI workloads.
For enterprises, that spending growth creates a parallel software-management challenge. AI infrastructure cannot simply be treated as another pool of conventional cloud compute when different accelerators, models and inference workloads have different performance and cost characteristics.
EasyStack says it serves more than 2,000 enterprise customers across regions including Southeast Asia, China, Central Asia, the Middle East, Africa, Europe and the Americas. With EAF, the company is attempting to make AI infrastructure an extension of existing cloud operations rather than a separate environment that enterprises have to build and manage from scratch.
Market Landscape
Enterprise AI infrastructure is shifting from model experimentation toward continuous production inference. Gartner expects inference to overtake training as the larger AI-optimized IaaS workload category in 2026, increasing the importance of utilization, cost tracking and infrastructure governance.
The competitive landscape includes hyperscalers, GPU vendors, cloud infrastructure companies and specialized AI platform providers. The emerging differentiation is increasingly around heterogeneous accelerator support, workload orchestration, governance, observability and the ability to integrate AI infrastructure into existing cloud environments.
Top Insights
- EasyStack EAF extends existing cloud environments with AI infrastructure capabilities instead of requiring a separate AI software foundation.
- The platform supports NVIDIA, Hygon DCU and Huawei Ascend accelerators in heterogeneous enterprise cloud environments, according to EasyStack.
- Virtual partitioning and inference optimization are designed to improve accelerator utilization and provide greater visibility into AI resource consumption.
- EAF offers single-node, converged high-availability and disaggregated deployment models for different enterprise AI workloads.
- Gartner expects AI-optimized IaaS spending to reach $42.3 billion in 2026 as inference workloads become increasingly important.
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