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CTERA launches AI-assisted archiving tool to cut costs

CTERA launches AI-assisted archiving tool to cut costs

Tue, 29th Sep 2026 (Today)
Joseph Gabriel Lagonsin
JOSEPH GABRIEL LAGONSIN News Editor

CTERA has launched an AI-assisted data archiving product designed to help businesses cut storage costs.

The offering combines CTERA InsightAI Data Service with CTERA Archive to identify inactive files and move them from primary storage to lower-cost tiers. It adds AI-based recommendations and a governed workflow to data lifecycle management, a process often handled manually by IT teams.

The launch comes as companies face growing volumes of unstructured data across network-attached storage environments and cloud systems. In CTERA's analysis of more than 16 petabytes of live production data across 856 file-share discovery scans, only 4.4% of stored capacity consisted of data regularly accessed by users and applications.

The same analysis found that 88.3% of files had not been accessed in more than a year, while less than 10% of stored capacity had been accessed within the previous 90 days. The figures highlight a familiar challenge for corporate IT departments: deciding what information can be moved to cheaper storage without losing access, governance controls or potential future value.

How it works

CTERA said its InsightAI service examines file metadata, including filename, path, extension and directory context, to infer file contents and assess business value. It combines that information with age, type, size and usage patterns to recommend files for archiving.

Those files can then be moved with CTERA Archive into a separate archival tier within the same environment. Archived data can sit on lower-cost hardware or cloud storage services while remaining available to AI tools and analytics systems without a separate retrieval or rehydration stage.

The product is part of the company's Intelligent Data Platform. Under that structure, archived files remain within the same governance framework as active data rather than being stored in a disconnected repository.

Cost and control

CTERA is positioning the product around four main issues: storage spending, the burden of manual lifecycle management, governance requirements and the exposure of sensitive data in file systems. Moving inactive information off primary storage can reduce the volume of material held in systems more frequently accessed by users and applications.

CTERA Archive includes configurable retention policies and an audit trail for archive and restore operations. That record includes the operator, source, destination, reason, file count, size and timestamp for each operation.

The launch also reflects a wider shift in enterprise data management as businesses face pressure to control infrastructure costs while keeping information usable for analytics and AI models. As interest in AI grows, companies are paying closer attention not only to active data but also to large stores of inactive material that may still hold business value.

One of the main questions for IT teams is whether cold data can be removed from expensive production systems without making it harder to use later. CTERA's approach aims to keep archived material accessible within the same platform rather than sending it to long-term storage that requires a separate restoration process before use.

Oded Nagel, Chief Executive Officer at CTERA, said growing enterprise data volumes had made those decisions more urgent for IT departments managing storage estates across multiple systems and locations.

"Enterprise data volumes are going to continue to grow, but not all data needs to remain on primary storage or be immediately accessible. The challenge has always been knowing what data can safely move and then providing a practical path to execute that migration," Nagel said.

"By pairing CTERA InsightAI, which understands what data means to the business and not just how old it is, with the governance and controls of CTERA Archive, the CTERA Data Archiving Solution gives IT teams a much simpler, AI-guided way to bring discipline to the enterprise data lifecycle without sacrificing access. Archived data stays usable for AI and analytics rather than locked away," he added.