SUSE named Gartner Leader for container management
Tue, 8th Sep 2026 (Today)
SUSE has been named a Leader in Gartner's 2026 Magic Quadrant for Container Management for SUSE Rancher Prime. It is the third consecutive year the company has been placed in the Leaders quadrant.
The recognition centres on SUSE's container management portfolio, designed to manage workloads across public clouds, core data centres and edge locations through a single control plane.
The platform supports any Cloud Native Computing Foundation-certified Kubernetes distribution and allows organisations to run virtual machines and containers on the same infrastructure layer. It is also built for air-gapped deployments and signed and verified software for organisations with strict compliance and sovereignty requirements.
SUSE positions the product as part of its wider open infrastructure offering for businesses managing mixed IT estates that include cloud-native applications, legacy virtualised systems and AI workloads. The system can be used in environments ranging from edge computing sites to GPU-based AI infrastructure.
Peter Smails, General Manager of SUSE's Cloud Native Business, linked the ranking to customer demand for control across varied environments.
"To us, this recognition is first and foremost a celebration of our customers and partners; their trust, innovation, and partnership are what make this possible," said Peter Smails, General Manager of SUSE's Cloud Native Business.
He also outlined how SUSE views its position in the market.
"IT leaders choose SUSE because they need complete control over their heterogeneous, edge, sovereign, and AI-driven environments. We believe what truly sets us apart in the market is our commitment to pure openness. By unifying virtualisation and containers under one control plane, we empower our customers to modernise on their own terms without operational silos or vendor lock-in," said Smails.
Customer use
SUSE pointed to customer deployments in banking and computer vision as examples of how the software is being used in production. These cases highlight uptime, distributed operations and the management of AI-related workloads across multiple sites.
ABA Bank, described as Cambodia's largest private bank, said it runs its internet and mobile banking services on the platform with 99.999% availability. The bank added that new microservices now deploy in half the time and container management costs have fallen by 15%.
"We are excited by the transformative potential of AI. Having an open, flexible and secure containerised infrastructure, we can innovate quickly and confidently," said Men Phalla, Head of DevOps Department, ABA Bank.
VisualCortex, which works in computer vision, said it uses the platform to run models with more than 95% accuracy across distributed edge sites. The company added that it can send new AI models to any site within minutes from one control plane.
"We needed an AI-optimised, cloud-agnostic platform that could support AI/ML workloads at scale across multi-cloud, edge and even air-gapped environments. SUSE Rancher Prime hit the mark perfectly," said Ben Evans, Chief Technology Officer, VisualCortex.
Market focus
The container management market has become increasingly tied to broader infrastructure decisions as companies seek to run applications consistently across different environments. Vendors are also trying to link container management more closely to virtualisation, compliance controls and support for AI systems.
SUSE's emphasis on openness and support for multiple Kubernetes distributions reflects that competitive pressure. Many large organisations want to avoid dependence on a single cloud provider or software stack while keeping tighter control over data location, operations and security settings.
The latest ranking places SUSE among the vendors Gartner considers strongest on vision and execution in the segment. For the company, the result also reinforces its effort to present Rancher Prime not just as a Kubernetes management tool, but as a central layer for running containers, virtual machines and distributed AI workloads across mixed environments.