VAST Data breaks decades-old IT compromises by offering the elite performance of all-flash storage combined with the limitless scalability and economics of an archive, making it an ideal operating system for heavy AI and deep learning workloads. Review collected by and hosted on G2.com.

Because the compute nodes rely entirely on the network to access the shared storage drives, users have noted that occasional network connection issues can immediately lead to unpredictable performance or noticeable lag during data usage. Review collected by and hosted on G2.com.

What I like best about VAST Data is its simplicity, scalability, and performance. The platform brings storage, database, and AI-focused data infrastructure together in a way that helps reduce complexity while supporting very large workloads. I also like its focus on modern AI and data-intensive environments, where performance and efficient data management are increasingly important. Review collected by and hosted on G2.com.

One area that could be improved is the learning curve, particularly for teams that are new to VAST Data. Some of the advanced features and architecture can take time to fully understand. More simplified documentation, practical examples, and step-by-step guidance could make onboarding and day-to-day management easier. Review collected by and hosted on G2.com.

What I appreciate most about Vast data is that it simply eliminates waiting around for data. Previously, if someone needed older data, there was always a delay. With Vast, everything is on fast storage. So accessing a file from a year ago feels just as instantaneous as pulling up something created an hour ago. Another huge benefit is how smoothly it handles different tools. Some of our applications are like a normal network folder while others expect modern cloud-based data access. With Vast, both sides can tap into the exact same data set without having to make duplicate copies or run conversion jobs. It keeps our daily workflow clean and removes a lot of usual data transfer shortcomings. Review collected by and hosted on G2.com.

The biggest limitation with VAST is that it’s really built for large-scale operations. If your data footprint isn’t that big, the platform often doesn’t make financial or practical sense. The initial entry point also requires a significant physical footprint, which can rule it out for smaller branch offices, small pilot projects, or teams that only need tens of terabytes. Another consideration is how demanding it can be on your network infrastructure. Because the hardware relies on high-bandwidth switches to deliver those top-tier speeds, your network environment has to be in good shape. Even a small configuration oversight in your network fabric can quickly bottleneck the system. It isn’t a simple “plug it into an existing rack and forget it” box. Review collected by and hosted on G2.com.

I really like Vast Data's unified platform that brings together files, objects, tables, and streams into a single namespace, which saves time and reduces complexity. The AI-native design built with GPUs and AI pipelines in mind is another standout feature, making it feel future-proof. I also appreciate the combination of speed and simplicity that Vast Data offers, which makes management easier for IT teams and data scientists alike. Lastly, the initial setup was easier than legacy systems because of Vast Data's containerized, unified design. Even though the main challenges are more about adoption and integration than the installation itself, it provides consistent speed even at an exabyte scale, which is relatively rare. Review collected by and hosted on G2.com.

The high upfront costs of Vast Data's all-flash architecture are a bit of a downside. While it's powerful, it can be expensive and tricky for smaller organizations or those with more modest workloads to justify. Even though deduplication and compression help over the long term, the initial barrier is pretty steep. This doesn't mean it's not valuable, but it's definitely better suited for organizations with large-scale, performance-hungry workloads. For smaller IT environments or companies that prioritize cost over cutting-edge performance, other options might make more sense. There's also the learning curve for teams switching from legacy systems, which takes time to navigate, and the ecosystem maturity compared to older players is still something Vast is working on, with integration and community resources still growing. Review collected by and hosted on G2.com.

I like how Vast Data makes managing and working with large data amounts feel more streamlined and scalable. It's really helpful for data-heavy workloads where performance and quick access are crucial. What stands out for me is how responsive it is with demanding workloads, even with a lot of data. I don't have to wait around or worry about performance issues. I appreciate Vast Data's unified storage approach, fast data access, and scalability. The centralized platform simplifies my work because it saves me from handling separate storage environments for different workloads. Plus, the initial setup was pretty simple. Review collected by and hosted on G2.com.

The main area for me personally is always the learning curve. Vast Data has a lot going on under the hood, and some of the terminology and configuration options can feel quite technical when you're first getting familiar with the platform. The complexity was most noticeable for me when first understanding how the different parts of the platform fit together, especially around configuration storage, managing data access, and knowing which settings are the best for different workloads. Review collected by and hosted on G2.com.

What I like most about VAST Data is its innovative approach to managing and processing data at scale. I especially appreciate how it brings together high performance, scalability, and simplicity, which makes it easier for organizations to take on large, complex data workloads without adding unnecessary complexity. Review collected by and hosted on G2.com.

What I dislike about VAST Data comes down mostly to cost and complexity. It can be expensive to get started, and in smaller environments it can feel like overkill for what you actually need. The initial setup may also demand a lot of technical expertise, and some workloads might not benefit enough from VAST’s architecture to truly justify the investment. Review collected by and hosted on G2.com.

What I like most about Vast Data is its high performance, scalability, and ability to manage large volumes of data efficiently. Its unified data platform makes it easier to handle data-intensive workloads while maintaining fast access and flexibility. The value is good compared with the cost. Vast Data provides strong performance, scalability, and efficient data management, which makes the investment worthwhile for organizations handling large and complex data workloads. Review collected by and hosted on G2.com.

What I dislike about Vast Data is that it can take some time to understand and configure, especially for users who are new to the platform. The overall solution can also feel complex compared with simpler data storage and management tools. Review collected by and hosted on G2.com.

VAST Data replaces complex multi-tier storage with an all-flash Disaggregated Shared-Everything (DASE) architecture, delivering archive-level pricing using QLC flash and global deduplication. Its stateless compute-to-storage design scales linearly and maximizes throughput to keep modern AI and GPU workloads fully saturated. Review collected by and hosted on G2.com.

VAST Data comes with a high entry cost and a sizable hardware footprint, which makes it impractical for smaller, sub-petabyte environments. It also depends on an ultra-low-latency RDMA network fabric to prevent bottlenecks. In addition, it feels less well suited to heavy, random transactional write workloads, especially when compared with read-heavy AI pipelines. Review collected by and hosted on G2.com.

It is a data storage and management platform. so we don't need to store our data into separate place for files, documents and other data, we can place all data into one platform and access parallel as a team. As our data grows we don't need to transfer our data to another platform, this platform scales according to our data. Review collected by and hosted on G2.com.

Its initial setup can be complex. For smaller organizations and budget-sensitive projects, it can be costly, otherwise nothing to dislike. Review collected by and hosted on G2.com.

I really like its performance when working with large datasets, UI feels smooth and the platform scales very well too, which makes it better than most of the platforms out there. On top of that, I also like its integration with existing data and compute environments. Review collected by and hosted on G2.com.

Onboarding can take some time to understand, especially the initial setup. The pricing also seems to make more sense at a larger scale. Also, comparatively, it has fewer AI features than other platforms. Review collected by and hosted on G2.com.