Data storage sellers are now integrating data management tools into their offerings to attract businesses investing in AI infrastructure. These enhancements range from software improvements within storage systems to additional features in core storage solutions.
As AI apps and model training evolve, enterprises are rethinking their approach to data storage. One of the biggest challenges storage vendors must address involves optimizing AI inferencing and training—critical processes that refine AI models. Organizations, particularly those with limited data expertise, may find value in bundled solutions, though seller lock-in remains a concern.
Different vendors define data management capabilities in their own way. For instance, software storage vendors like DataDirect Networks’ Infinia and Pure Storage’s Fusion leverage automation, API connectivity, and metadata tagging to enhance performance and availability. Others focus on integrating storage infrastructure with data lakehouse functionalities to increase their platform’s value.
Storage hardware has largely become a commodity, making data management tools a key differentiator. For instance, Vast Data is preparing to launch Vast Event Broker, a Kafka API-compatible streaming engine that syncs archived and active data for warehouse ingestion—though this feature is exclusive to Vast users.
AWS launched S3 Tables last year, transforming object data in S3 storage into a tabular format for Apache Iceberg data lakes. In January, AWS also unveiled S3 Metadata, a tool that automatically generates metadata for objects stored in S3. These additions aim to improve compatibility between data warehouses or lakes and storage platforms.
As AI adoption increases, businesses will likely turn to data meshes, which offer a decentralized architecture to pull data from various sources. For seamless AI operations, storage platforms must integrate effortlessly across different departments.
Connecting storage solutions with such services should not disrupt performance, but dependence on specific vendor ecosystems can present challenges.
For some organizations, using seller-provided data management tools may outweigh the risks of seller lock-in, particularly if they lack in-house data specialists or extensive IT resources. However, even with a simplified tech stack, complexities can still arise.
As more vendors introduce data management capabilities, companies must develop clear strategies for both data and storage handling. Strict policies are necessary to ensure performance and that only relevant data enters storage systems.
AI implementation in enterprises is still in its early stages, meaning specialized tools and best practices are still being refined. Legal uncertainties regarding data ownership and compliance continue to pose challenges, preventing many AI initiatives from reaching full production. Organizations must navigate these evolving landscapes carefully as they shape their AI and storage strategies.
For big tech companies like Apple Inc. (NASDAQ: AAPL), commercial data storage facilities may not cut it, so they create their own data storage and management systems.
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