US Cloud Object Storage Market Size and Forecast by Type, Deployment Model, Organization Size, Pricing Model, Use Case, and End User Industry: 2019-2033

US Cloud Object Storage Market Outlook

According to David Gomes, Manager – IT, the US cloud object storage market is experiencing exponential growth, projected to reach US$ 12.7 billion by 2033. This market surge is fuelled by massive enterprise-scale adoption of AI model training datasets, video content delivery, multi-region data replication, and long-term regulatory storage needs. Object storage, with its flat address space, scalability, and metadata-rich structure, is now the default storage model for unstructured data in the cloud—powering everything from content delivery networks (CDNs) to AI data lakes and medical imaging repositories

In 2025, generative AI and LLMs are placing unprecedented pressure on storage backends. Enterprises, universities, and defense contractors are using object storage to house petabytes of training data that require high throughput and durability. Amazon S3, Google Cloud Storage, and Azure Blob Storage have responded by enhancing support for intelligent tiering, batch ingestion, and parallel reads, which are essential for scalable ML pipelines. For example, Google Cloud’s GCS now integrates directly with Vertex AI, allowing LLMs to stream training data via high-performance pre-signed

Beyond AI, media, gaming, and streaming platforms in the U.S. are heavily investing in object storage. With support for geo-distribution, lifecycle management, and content indexing, object storage enables video-on-demand (VoD) platforms to maintain 99.999999999% (11 nines) for their digital assets. Netflix and Disney, among others, leverage hybrid object storage to balance cost and content delivery latency across U.S. metros. Additionally, ed-tech and e-learning providers are archiving terabytes of user-generated content, making versioned object storage a must-have

The regulatory landscape is also a significant driver. U.S. enterprises governed by HIPAA, SEC Rule 17a-4, and IRS recordkeeping laws are adopting immutable object storage with Write Once Read Many (WORM) features. These offerings, such as AWS S3 Object Lock and Wasabi’s immutable buckets, ensure legal-grade compliance and chain-of-custody traceability. Financial institutions, healthcare providers, and legal firms increasingly prioritize such features as part of their cyber-resilience and audit-readiness strategies

Another defining trend is the rise of edge and multi-cloud object storage orchestration. Enterprises operating across U.S. regions—especially in sectors like energy, manufacturing, and logistics—require real-time object access near the data source. This is driving demand for S3-compatible object storage systems such as MinIO, Cloudian, and Scality, which support hybrid cloud deployments with on-premise S3 gateways and cross-cloud replication

From a performance standpoint, U.S. enterprises are integrating content delivery acceleration, data lake optimization, and smart data tiering within their object storage strategies. Intelligent object classification and AI-based data placement enable cost savings of up to 45% annually, especially when moving rarely accessed data to cold tiers like AWS Glacier Deep Archive or Azure Archive Blob

Recent developments further highlight the market's direction. In March 2025, Wasabi Technologies partnered with Dell Technologies to launch a bundled object storage + compute solution for U.S. SMBs and government entities, offering compliance-ready storage zones with fixed pricing

According to David Gomes, the future of the US cloud object storage market lies in delivering AI-ready, compliance-anchored, API-accessible, and highly durable unstructured data storage. Enterprises are advised to evaluate solutions based on cost transparency, multi-region failover capabilities, zero egress fee options, and deep integration with data analytics tools

Authors: David Gomes (Manager - IT)

*Research Methodology: This report is based on DataCube’s proprietary 3-stage forecasting model, combining primary research, secondary data triangulation, and expert validation. [Learn more