Their Platforms Aren’t Built for Virtual Workloads – Ours Are

Managing virtual workloads like VMs, databases, and containers in modern IT environments is becoming increasingly complex. As organizations embrace digital transformation and cloud initiatives, the landscape only gets more challenging. With virtualized workloads spanning various applications and teams, scaling, optimizing, and maintaining high performance can feel like an endless struggle. Traditional LUN/Volume-based storage platforms weren't designed with these demands in mind, leaving IT leaders searching for a better solution.

For years, the go-to strategy was simply to add more flash storage or expand capacity. While that might address performance issues in the short term, it doesn't tackle the root causes inherent to legacy LUN/Volume architectures. When hundreds or thousands of virtual workloads are running concurrently, traditional systems often struggle to handle sudden spikes in demand, leading to resource contention and latency issues.

A major limitation of LUN/Volume-based approaches is the lack of fine-grained control over resource allocation, making it difficult to manage unpredictable workloads efficiently. Without robust Quality-of-Service (QoS) capabilities, applications competing for resources can experience performance degradation, creating frustration for both application owners and IT administrators.

Moreover, data protection and disaster recovery (DP/DR) are critical considerations that legacy systems don't adequately address for virtual workloads. Traditional backup and recovery methods, often designed for static environments, can result in long recovery point objectives (RPOs), leaving organizations vulnerable to extended downtime.

Tintri offers a purpose-built storage platform that solves these challenges. Our Tintri VMstore is designed specifically for virtual workloads, providing a new approach to storage management that overcomes the limitations of LUN/Volume-based architectures. By managing at the level of individual VMs, databases, and containers, VMstore guarantees QoS, minimizes latency, and eliminates resource contention.

With AI and machine learning-driven automation, Tintri VMstore simplifies performance optimization, accelerates DP/DR processes, and enhances visibility. Users can snap, clone, and recover data at the level of a single managed object-whether a VM, database, or container-without the inefficiencies of traditional volume-level recovery. The result is rapid restoration, space-efficient snapshots, and minimized RPO, translating to substantial time and cost savings during disruptions.

Tintri VMstore's AI-powered capabilities take the guesswork out of managing virtual workloads, making it the ideal choice for IT leaders looking to streamline operations and maintain peak performance in dynamic environments.

To see how Tintri can transform your virtual workload management, request a demo attintri.com.