AI Data Retention & Reuse
Preserve Today's Data. Power Tomorrow's AI.
Keep Your AI Data Working Beyond the Active Tier
Economically retain and protect AI data beyond active use while keeping it available for retraining, retrieval-augmented generation (RAG), analytics, governance, and other AI workflows.
OVERVIEW
AI Data Doesn't Stop Being Valuable When the Workload Ends
AI workloads create growing volumes of training data, model checkpoints, inference history, and other data that can retain value long after active processing is complete. Keeping all that data on high-performance flash, disk, or primary object storage drives up costs and consumes capacity needed for active AI workloads. But deleting it can mean losing data that may be expensive or impossible to recreate.
The Spectra AI Data Retention & Reuse solution provides another path. Move valuable AI data that no longer needs active-tier performance to an economical long-term storage tier, protect it over time, and keep it available for retraining, RAG, analytics, governance, and other future AI workflows.
AI Data Creates a Long-Term Storage Challenge
AI
DATA
GROWTH
Training data, model checkpoints, and inference history accumulate rapidly, increasing storage costs and putting pressure on active-tier capacity.
THE COST OF INACTIVE
DATA
AI data that no longer needs active-tier performance can remain on expensive storage, consuming budget, capacity, power, and infrastructure resources.
THE RISK
OF DELETING DATA
Deleting AI data can create future recreation costs or permanently eliminate data that is expensive or impossible to reproduce.
LONG-TERM PROTECTION & GOVERNANCE
AI data may need to remain protected, traceable, and accessible for years to support governance, regulatory, legal, audit, and business requirements.
GETTING
DATA BACK
INTO USE
Large retained datasets must be retrieved quickly enough for retraining and other AI workflows without storage becoming a bottleneck to expensive compute.
Do More With Your AI Data
Spectra AI Data Retention & Reuse provides a scalable approach to move AI data beyond the active tier to economical, tape-based long-term storage, protect it over time, and keep it available for reuse.
Retain More
Store AI data affordably.
Move AI data that no longer requires active-tier performance to economical long-term storage, reducing storage costs and infrastructure demands.
Trust More
Keep retained AI data protected and auditable.
Maintain confidence in retained AI data with long-term protection, integrity verification, encryption, observability, and lifecycle management.
Do More
Put retained AI data back to work.
Keep AI data available for retraining, RAG, analytics, governance, and other workflows, with efficient retrieval of large datasets when needed.
Keep AI Data Ready for What Comes Next
AI data can serve different purposes long after its initial workload is complete. Retain training datasets, model history, and inference data economically, then retrieve them when needed for future AI and governance requirements.
Model & Training Data Retention
Preserve training datasets, model checkpoints, and other AI data without keeping everything on costly active storage.
Governance, Legal Hold & Audit
Retain AI data, model versions, and decision lineage to support governance, legal, regulatory, audit, and accountability requirements.
Model Retraining & AI Data Reuse
Bring retained AI data back into use for model retraining, RAG, analytics, and other AI workflows without keeping it indefinitely on expensive active storage.
Store AI Data Affordably Without Sacrificing Access
AI data doesn’t need to remain on high-cost active storage to remain useful. Spectra provides a long-term data tier that keeps retained AI data economically stored, protected, accessible, and available when future workflows need it again.
IDENTIFY & MOVE
Identify training datasets, model checkpoints, inference history, and other AI data that no longer requires active-tier performance, then move it to economical long-term storage.
RETAIN & PROTECT
Keep AI data protected over time with data integrity verification, encryption, observability, and lifecycle management.
ACCESS & MANAGE
Maintain object-based access to retained AI data while managing it across its lifecycle, rather than treating long-term storage as an isolated repository.
RETRIEVE & REUSE
Efficiently retrieve large retained datasets for model retraining, RAG, analytics, governance, and other AI workflows without creating unnecessary delays for expensive AI compute
Get More Value from Your AI Data Over Time
Move AI data that no longer requires active-tier performance to long-term storage designed to reduce costs and infrastructure demands, protect it over time, and keep it available for reuse.
Lower Long-Term Storage Costs
Reduce the cost of retaining growing AI datasets by moving data that no longer needs active-tier performance off premium storage.
Free Active-Tier Capacity
Make more flash, disk, and primary storage capacity available for active AI workloads.
Reduce Infrastructure Demands
Reduce power, cooling, floor space, and other infrastructure demands by moving retained AI data off active storage.
Protect Data for the Long Term
Keep AI data protected, intact, and available to support governance, legal, regulatory, audit, and accountability requirements.
Preserve AI Data for Reuse
Keep retained data available for model retraining, RAG, analytics, governance, and other AI initiatives without storing it indefinitely on expensive active storage.
Built for Long-Term AI Data Management
Retain, protect, manage, and retrieve AI data across its lifecycle with capabilities designed for data that has moved beyond active use but may need to return.
Scalable, Economical Long-Term Storage
Economically retain growing volumes of AI datasets, model checkpoints, inference history, and other AI data beyond active use.
Data Integrity & Proactive Protection
Monitor retained data and storage infrastructure to help keep AI data intact, readable, and available over time.
Encryption & Data Protection
Protect retained AI data with encryption and long-term protection capabilities that support governance, legal, regulatory, audit, and business requirements.
Amazon S3-Compatible Object Access
Connect object-based AI workflows to long-term tape storage through a familiar Amazon S3-compatible interface.
Lifecycle Management
Use policy-driven data movement and retention to move AI data beyond the active tier and manage it over its lifecycle.
Data Retrieval & Reuse
Efficiently retrieve large AI datasets for model retraining, analytics, RAG, governance, and other AI workflows without keeping them indefinitely on active storage.
Customer Stories
Exabyte-Scale Storage For AI-Driven Research
We needed a new system that could keep up with the demands of our users and compute clusters.”
– David Cooper, Sr. Systems Administrator, HPC Large-Scale Systems, Texas Advanced Computing Center (TACC)
AI-Ready Data Storage for Advanced Weather Forecasting
Our partnership with Spectra Logic is central to protecting and managing the nation’s data assets so they can be reliably used at scale, including for advanced AI-driven modeling. This gives us a much stronger basis to plan, protect people and infrastructure, and make decisions with more confidence.”
– Warrick Johnston, Chief Digital & Technology Officer, Earth Sciences New Zealand
Preserving Research Data for Future Discovery
Tape was the optimal choice for our access patterns because much of our data is rarely accessed after initial use. It’s also a secure archive format that requires minimal electricity — tape is the only medium that can be stored completely offline for years.”
– Julian Hniopek, Head of Staff for IT & Research Data Infrastructure, Leibniz Institute of Photonic Technology
Technology That Powers AI Data Retention & Reuse
Spectra combines data analysis, lifecycle management, object-based access, and intelligent tape storage to help organizations retain, protect, manage, and reuse AI data over time.
Data Analysis & Insights
Understanding what data exists, how it's used, and where opportunities exist to optimize storage helps inform data-placement and retention decisions.
Spectra Storage Analyzer
Use Storage Analyzer to better understand your data environment and inform decisions about what should remain active and what is appropriate for long-term retention.
Data Lifecycle Management
Data lifecycle management helps organizations place data on the appropriate storage tier as its access requirements and value change over time.
Spectra StorCycle Software
Use StorCycle software for lifecycle and policy management as data transitions from active storage to economical long-term retention.
Object-Based Access
Object-based access provides a practical way to connect modern AI data workflows with an economical long-term storage tier.
Spectra BlackPearl Object Gateway
BlackPearl Object Gateway provides an Amazon S3-compatible interface to tape, connecting object-based workflows with long-term tape storage.
Tape Storage & Intelligent Library Management
Tape storage provides economical long-term protection for AI data that no longer requires active-tier performance. Intelligent library management helps preserve the data and the infrastructure that supports it over time.
Spectra Tape Libraries with LumOS
Spectra tape libraries combine scalable storage with LumOS capabilities that monitor media, drives, library hardware, environmental conditions, and data readability.
Resources
Smarter Storage: Managing Data Efficiently and Reducing Costs with an Archive Tier white paper
SEE WHAT’S POSSIBLE
Ready to Rethink Long-Term AI Data Storage?
Find out how Spectra can help you retain more AI data economically, protect it over time, and keep it available when AI needs it again.
Frequently Asked Questions
What is AI backup?
AI backup protects AI models, model snapshots, checkpoints, training data, and related assets so you can recover them when needed. For AI workflows, backup can help organizations preserve previous model states and supporting data without keeping every version on higher-cost active storage.
This is particularly valuable when teams need to recover an earlier version of a model or its supporting data.
What is an AI archive?
An AI archive is the long-term retention of valuable AI data that is no longer actively used but may be needed again for retraining, governance, analysis, audit, or future reuse.
Unlike a traditional archive where data may rarely return, AI workflows can reactivate retained datasets when models or business requirements change. This makes the AI archive a long-term data layer that preserves the future value of AI data without keeping it on higher-cost active storage.
How is AI backup different from AI archive?
AI backup and AI archive serve different purposes. AI backup focuses on recovering models, checkpoints, and supporting data. In contrast, AI archive retains valuable AI data for longer-term needs such as governance, legal hold, retraining, and future reuse.
AI environments may require both. Backup can help recover an earlier model state, while archive preserves information whose value extends beyond the original workflow.
What types of AI data should be archived?
AI data with long-term value that doesn’t require continuous active-tier performance is a strong candidate for archive. Examples include training datasets, model checkpoints, curated datasets, inference history, raw source data, model weights, training manifests, dataset versions, embeddings, lineage information, and other AI-generated derivatives.
Not all AI data should be archived. Data needed frequently for active training or production may belong on high-performance storage, while transient data that is inexpensive to recreate may not need retention.
Who is the Spectra AI Data Retention & Reuse solution built for?
The Spectra AI solution is built for AI and data leaders responsible for large AI datasets and the infrastructure that supports them, particularly in data-intensive environments where training data, model checkpoints, inference history, and other AI data must be retained beyond active use.
It is designed for organizations that need to control the growing cost and infrastructure demands of retaining AI data without deleting data that may be needed for retraining, governance, compliance, analytics, or future AI initiatives.
Which industries can benefit from an AI archive?
Any data-intensive industry that retains valuable information for AI retraining, reuse, governance, or audit can benefit from an AI archive strategy. Examples include media and entertainment, healthcare and life sciences, research and high-performance computing (HPC), aerospace and advanced manufacturing, and financial services.
Across these industries, the underlying storage challenge is similar: data may become inactive for a period while remaining valuable, expensive or impossible to recreate, and potentially important to future AI workflows.
Is tape a legacy technology?
Tape is an established storage technology that still meets modern needs for large-scale, long-term data retention. For AI data storage, the important question is whether the storage technology matches the data’s access frequency, retention requirements, and future value.
Why use tape instead of cloud archive?
Tape and cloud archives can both provide long-term storage for infrequently accessed data. However, an on-premises tape archive gives organizations direct control over their data, retention policies, and retrieval economics.
Cloud archives offer economical long-term retention but may introduce retrieval fees, egress costs, and delays when large datasets are needed again. An on-premises tape archive provides greater control over data placement, retention policies, and retrieval costs, helping organizations plan for future AI data reuse.
Tape does not need to replace cloud storage in every environment. The appropriate approach depends on data volume, access patterns, retention requirements, infrastructure strategy, and where an organization wants its long-term AI data to reside.
How do I determine which AI data should be archived?
Determine which AI data to archive by considering how expensive it would be to recreate and how frequently it needs to be accessed. High-value AI data that is costly to regenerate but accessed infrequently is a strong candidate for long-term archive. Frequently accessed information belongs on an active tier, while transient data that is inexpensive to reproduce may not need to be retained.
Organizations should also consider whether they may need data later for retraining, reprocessing, governance, audit, or legal purposes.
A useful question is: If this AI data is deleted today and needed again later, what would it cost to recreate it?
Can Spectra support exabyte-scale AI archives?
Yes. Spectra is designed for large-scale, long-term data environments, from tens and hundreds of petabytes to exabyte scale. As AI data grows, Spectra provides a scalable, economical storage tier to retain valuable datasets long-term while keeping them protected and available for future use.
Why choose Spectra for long-term AI data retention and reuse?
Spectra gives organizations a practical way to preserve AI data beyond the active workflow, economically retaining large datasets while keeping them protected, managed, and available for retraining and reuse.
