Enterprise cloud storage to hit $128 billion by 2028
Enterprise cloud storage spending is projected to more than double by 2028, reaching $128 billion—a substantial increase from the $57 billion spent last year. Accelerating integration of AI technologies within enterprises is driving the need for expansive and efficient storage solutions.
Surging spending here has highlighted the importance of cloud storage in supporting the expansive data requirements of AI applications, analytics, and other data-intensive operations.
As organizations continue to generate and analyze larger volumes of data, demand for scalable and secure storage options will inevitably rise, further propelling the market towards this projected growth.
AWS dominates with 30% share as cloud storage titans compete
In the hyper-competitive enterprise cloud storage sector, AWS is arguably the most dominant position, having captured 30% of the nearly $60 billion market—bolstered by AWS’s wide-reaching presence in object storage, which accounts for 38% of the capacity consumed.
Microsoft’s Azure follows with a 22% market share, leveraging its strong enterprise relationships and integrated cloud services. Google Cloud then follows, having secured a 14% share, continuously expanding its infrastructure and service offerings.
AWS’s extensive global infrastructure and end-to-end ecosystem give it a considerable advantage, making it the preferred choice for many organizations looking to optimize their cloud storage capabilities.
Key trends shaping the future of cloud storage
Revenue trends and forecasts show steady growth in cloud storage
Distribution of revenue among hyperscalers closely aligns with the overall cloud services market. According to Gartner, IT spending on cloud services is forecasted to approach $700 billion this year, pointing out a growing reliance on cloud infrastructure, with Omdia anticipating cloud storage spending to nearly reach $70 billion by 2024.
Enterprises are recognizing the need for robust and flexible storage solutions to support their digital transformation initiatives, data analytics, and AI-driven applications, contributing to sustained market expansion.
Economic shifts and tech innovations redefine cloud storage spending
Last year, enterprises embarked on an optimization cycle, trimming cloud spending in response to economic cooling. This was particularly evident in storage services, which faced cuts due to overprovisioning in previous years.
Surging demand for GPUs to power generative AI has also contributed to a shift in spending priorities.
This “GPU gold rush” has greatly impacted storage budgets, diverting funds towards high-cost GPU services. In 2022, the industry experienced a 30% year-over-year increase in storage spending, which slowed to 10% growth last year as enterprises adjusted their budgets and resource allocations.
The current economic environment and technological advancements are reshaping how organizations allocate their cloud budgets. While the initial boom in cloud storage spending has tempered, the need for scalable and efficient storage solutions remains a priority.
Cloud storage market set to expand by over 18% in 2024
AI-driven technologies propel cloud storage growth in 2024
The enterprise cloud storage market is expected to grow by around 18% year-over-year in 2024—primarily driven by the adoption of AI-related technologies that focus heavily on data.
Organizations are increasingly leveraging AI for many different applications, leading to an amplified need for robust storage solutions to handle the vast amounts of data these technologies generate and process.
AI integration is pushing enterprises to expand their cloud storage capabilities to support data-intensive operations such as machine learning, predictive analytics, and real-time data processing. As AI continues to mature and become more integral to business operations, the demand for cloud storage will rise correspondingly, fueling broad market growth.
How AI and analytics are transforming cloud storage consumption
AI projects drive new data management needs in cloud storage
AI projects are fundamentally changing how enterprises manage and consume cloud storage. A large portion of an AI project involves data management tasks, such as combing through enormous datasets, ensuring data accuracy, cataloging, and sorting data—processes that are key for training AI models and developing analytics capabilities.
Generative AI and advanced analytics are expected to greatly boost demand for cloud storage as they require extensive data handling and storage capabilities.
AI adoption is also altering storage consumption patterns. Enterprises are amassing large volumes of data to train and refine large language models (LLMs) and other AI tools, which in turn drive increased usage of different types of cloud storage to accommodate the specific needs of AI projects.
As AI tools and applications continue to evolve, the storage infrastructure must adapt to support more sophisticated and data-intensive workloads, leading to higher storage consumption and new storage management strategies.
Exploring different types of cloud storage solutions
Why object storage leads with 70% capacity in cloud
Object storage is the most prominent type of cloud storage, accounting for 70% of total capacity. It’s the most cost-effective storage option and is widely used for data management and warehousing.
Object storage is more affordable and scalable, making it ideal for storing large volumes of unstructured data, such as media files, backups, and big data analytics. Despite its extensive capacity, object storage only accounts for around one-third of total storage spending due to its lower cost.
The role of block storage in databases and business intelligence
Block storage accounts for another one-third of cloud storage capacity and is optimized for high-performance tasks—designed for applications requiring quick and consistent data access, such as databases, business intelligence systems, and containerized workloads.
Block storage is more expensive than object storage but provides the performance and reliability necessary for mission-critical applications. Its architecture supports fast input/output operations, making it suitable for structured data storage and transaction-heavy environments.
Generative AI sparks increased demand for file storage
File storage then accounts for the remaining one-third of cloud storage capacity and is the most expensive storage option. It’s critical for environments that require shared access to data, such as office collaboration tools and server farms.
File storage is expected to see increased demand as it’s suitable for generative AI applications and retrieval-augmented generation, a method used to fine-tune large language models to specific domains. As enterprises deploy more generative AI solutions, the need for high-performance file storage will grow, supporting advanced data retrieval and processing tasks.
AWS boosts file storage for unstructured data and legacy support
AWS improves file services for legacy and high-performance needs
AWS is intensifying its focus on file services to support legacy applications and provide high-performance storage for unstructured data interactions. As organizations transition from on-premises environments to the cloud, many legacy applications still require comprehensive and reliable file storage solutions.
AWS is expanding its file storage offerings to cater to these needs, so that enterprises can seamlessly migrate and operate their legacy systems in the cloud.
In addition to offering legacy support, AWS’s file services are also designed to handle high-performance workloads involving unstructured data. This is particularly valuable for applications in media and entertainment, scientific research, and AI development, where large volumes of unstructured data need to be stored, accessed, and processed efficiently.
Final thoughts
Enterprise leaders need to gear up for the upcoming explosive surge in cloud storage demands driven by the AI revolution. With AI technologies advancing rapidly, comprehensive and scalable storage solutions must – and will – become a priority.
Leaders must prioritize optimizing their storage infrastructure to handle vast data volumes and boost performance for AI-driven applications, so that their organizations can stay competitive in a rapidly evolving space.
