All-Flash Arrays (AFAs): Everything You Need To Know

For many years, storage architects were forced to choose between performance and price. Though flash storage has long been an option, it has historically come with a steep premium, and organizations often continued to rely on massive arrays of spinning hard disk drives (HDDs) to get the capacity they needed. In many cases, this meant overprovisioning storage resources to meet peak performance demands, leading to large data center footprints and substantial power and cooling costs.

Today, technological advances and the rapid decline of flash prices have made all-flash arrays a more practical choice for many organizations. Modern AFAs now leverage Non-Volatile Memory Express (NVMe) protocols and high-density quad-level cell (QLC) media to deliver consistent sub-millisecond latency.

These advances have allowed organizations to consolidate their data center footprints, reduce power and cooling requirements, and improve the performance of applications ranging from virtual desktop infrastructure (VDI) to real-time analytics.

This guide provides a comprehensive overview of the enterprise AFA market, including the architecture and data reduction mechanisms that define modern flash storage, key performance metrics (and how they compare to legacy storage systems), and the essential criteria IT leaders must consider to select the right vendor for their environment and maximize their organization’s ROI.

An AFA is an external storage array that supports only flash media for persistent storage. In an AFA, flash memory is used instead of the HDDs that have traditionally been associated with networked storage systems.

The widespread use of AFAs is a relatively recent phenomenon. Flash is faster than spinning disk, but historically it has been much more expensive, and for many years it was reserved exclusively for high-value, latency-sensitive workloads. Organizations have also frequently deployed hybrid arrays, which use both flash and spinning-disk storage to balance performance requirements with cost constraints.

The terms flash storage and solid-state drives (SSD) often are used interchangeably, but while these technologies both store data, it’s worth noting some key differences between them.

Technology breakthroughs and economies of scale drove the price of AFAs down during the 2010s, with vendors able to legitimately claim for the first time that the price of the technology could rival or even beat spinning-disk arrays for certain workloads. This was especially true for highly deduplicable workloads such as VDI, with AFAs helping organizations to achieve cost savings through data reduction, as well as decreases in power, space and administrative overhead needs.

Today, AFAs have effectively replaced hybrid and HDD-only systems as the default for primary enterprise storage within some organizations. These arrays increasingly run on NVMe protocols, which further increases parallelism and reduces latency. And advances such as QLC flash, which increases storage density, have driven down the effective cost per terabyte, making flash a practical option even for long-term and large-scale data storage.

By combining NVMe with technologies such as QLC and always-on data reduction, organizations can now use AFAs to handle secondary workloads, backup and large unstructured data sets at a total cost that rivals that of legacy spinning-disk storage systems.

At a high level, AFAs can be categorized according to one of two main types of architecture: scale-up architecture and scale-out architecture.

In a scale-up architecture, organizations expand capacity by adding additional storage to existing arrays. This allows for a simple and cost-effective way to increase capacity, but it can also lead to performance limitations, as the architecture model relies on a central storage management controller that can sometimes act as a scalability bottleneck.

In a scale-out architecture, by contrast, organizations expand capacity by adding more nodes to their systems. The scale-out model better accommodates rapidly expanding storage needs and ensures consistent performance even as a system grows.

Organizations often adopt AFAs to attain benefits such as improved performance, increased reliability, reduced data center footprint and better efficiency.

• Performance: AFAs deliver significantly higher input/output operations per second (IOPS) and lower latency compared to spinning disk, ensuring that mission-critical applications run smoothly, even during periods of peak demand.
• Reliability: AFAs lack the moving mechanical parts found in traditional hard drives, making them less prone to physical failure. This durability leads to lower downtime and less risk of data loss.
• Footprint: Flash offers far greater storage density than spinning disk, allowing organizations to use fewer rack units in their data centers. This consolidation reduces the physical floor space required for infrastructure, as well as power and cooling needs.
• Efficiency: The increased energy efficiency of AFAs helps organizations save money on long-term operational expenses. Also, flash can introduce time-saving automations, helping organizations to make more efficient use of IT labor.

AFAs can deliver storage using several models. In a block storage model, data is written as fixed-size blocks that are presented to hosts as raw volumes for databases, virtual machines and other latency-sensitive applications. In a file storage model, data is organized into files and directories and accessed over standard file protocols such as NFS or SMB. And in an object storage model, data is stored as objects with rich metadata and accessed via application programming in interfaces, making it ideal for large volumes of unstructured data.

Organizations may opt for a specific model depending on their desired use case.

Virtualization: Organizations often opt for flash storage to support virtualized environments because hypervisors depend on low latency and consistent throughput to keep virtual machines responsive. Block storage is typically the right model for virtualization, as it offers hypervisors direct access to storage hardware and allows for granular control over performance.

Disaster recovery: Flash can shrink backup and restore windows, helping organizations improve recovery time objectives while simplifying their backup workflows. File storage is a common model for backup and disaster recovery.

Analytics and AI: Flash provides the consistent, high-bandwidth data delivery needed to support artificial intelligence training cycles and inferencing. Due to its inherent scalability, object storage is often seen as a fit for analytics and AI use cases.

Data reduction is not a single technology or feature but rather a benefit that can be achieved in a number of ways. The term refers to the process of reducing data volumes while retaining essential information, which can lead to process improvements, cost savings and improved compute efficiency. Most often, data reduction is achieved through data deduplication or data compression.

Data deduplication (often shortened to “dedupe”) eliminates redundant data on storage systems, using a single copy whenever a request is made to access a piece of data. Data deduplication tools use pattern recognition to identify redundant data and replace them with references to a single saved copy. Data compression, by contrast, reduces data by encoding information using fewer bits of data. Using a lossless compression algorithm, data compression tools can fully preserve all information by removing statistical redundancy.

AFA vendors rely on data deduplication and data compression tools to make the arrays viable for general-purpose storage. Because raw flash media is significantly more expensive per gigabyte than spinning disk, achieving high data reduction ratios (often 3:1 or greater) allows organizations to store more data on less physical hardware, bringing the effective cost down to parity with HDDs. Additionally, by reducing the actual amount of data written to the drive, these technologies minimize wear and can extend the lifespan of storage arrays.

Although performance and cost vary somewhat by vendor, comparing the key performance and efficiency metrics of AFAs and traditional spinning-disk arrays can help IT and business leaders decide which is the best fit for their environment.

• IOPS range: IOPS represents how quickly a storage array can read and write commands. Generally, HDDs have a range of somewhere between 55 and 180 IOPS. AFAs have a much higher range, into the hundreds of thousands of IOPS for NVMe SSDs, depending on configuration. For random read data access, this can reduce retrieval time from 10 to 15 milliseconds down to well under 1 ms. For some NVMe drives, this number is in the tens of microseconds.
• Failure rate: Failure rates will depend on vendor and configuration. However, one cloud storage provider reports its SSD annual failure rate as 0.9%. That is about half the annual failure rate of the provider’s HDDs (1.71%).
• Cost: In 2024, the price of flash storage averaged $0.085 per gigabyte, more than double the $0.039 average cost per GB of spinning-disk storage. Still, the total cost of ownership for AFAs is often lower than that of HDDs.
• Power/Space Requirements: AFAs are much more energy-efficient than HDDs. According to one storage vendor, HDDs use about 4-5 watts of power in write/read mode and 2-3 watts in idle mode. By contrast, all-flash consumes about 3.5 watts in write mode, 2 watts in read mode and even less in idle mode. Some all-flash storage can reduce power and space needs by up to 80%, compared with spinning-disk arrays.
• Density: The same vendor notes that AFAs provide seven times the density of HDDs, making scaling more manageable.

While flash storage remains more expensive than spinning-disk storage on a per-GB basis, the total cost of ownership for flash is often lower, after accounting for factors such as power, cooling, space, durability and management.

According to the previously cited vendor, all-flash storage can reduce operational costs by up to 60%. Factoring in the higher upfront cost of purchasing an AFA, this results in a six-year TCO that is 40% lower than that of HDD arrays.

When considering purchasing an AFA, IT and business leaders must consider a variety of factors, including performance, compatibility, cost and security. The table below highlights key evaluation criteria to guide your decision-making.

Moving to an AFA is a major infrastructure upgrade that requires careful planning. A trusted partner such as CDW can help organizations assess their environments, compare vendor options, and design a deployment that aligns with long-term storage and application needs. CDW can also help organizations avoid common pitfalls, guide teams through budgeting and sizing decisions, and lead rollouts with minimal disruption to the business.

The following steps can help IT teams find and implement a cost-effective data storage solution that fits their environment:

1. Assess workloads and infrastructure: Review existing storage environments and latency requirements to identify which applications will gain the most from flash. Validate compatibility with the existing environment.
2. Define goals: Establish success criteria, such as reducing latency, condensing hardware, reducing backup windows and reducing data volumes.
3. Design the architecture: Plan for network bandwidth, protection policies, replication needs and cloud integration. Outline a data migration strategy to ensure minimal disruption, whether moving virtual machines, databases or large file repositories.
4. Deploy, migrate and optimize: Start with a pilot workload to validate performance and data reduction. Migrate data in phases and then monitor the environment for latency and capacity. Leverage built-in analytics and automation features to fine-tune operations and plan for future growth.