Beyond the Flash Tier: Cold Storage’s Strategic Role in the AI Factory Era
From Data Centres to AI Factories
I grew up near a Triumph car factory where they made the two-seater sports car, the Triumph TR7.
As a child, I was fascinated by the rhythm of the plant: raw materials arriving on trucks, finished cars on trailers rolling in the other direction. It was a physical demonstration of flow, of inputs, processes, and outputs perfectly coordinated to produce something of value.
TR7
Today, as data centres evolve into what many are calling AI factories, I can’t help but see similarities, the raw material is data, and the finished product is intelligence.
The recent SiliconANGLE article on AI Factories: Data Centres of the Future describes this shift vividly:
“The data centre as we know it is being reimagined as an ‘AI factory’, a power and data-optimised plant that turns energy and information into intelligence at an industrial scale.”
Just as a physical factory transforms steel and rubber into sports cars through a precise choreography of logistics, machinery, robots and workers, the AI factory transforms raw data into trained models and intelligent applications.
Just as a car factory needs supply chains, assembly lines, and warehouses, the AI factory depends on fast volatile, hot, nearline, and cold storage tiers, each playing a crucial role in moving, processing, and preserving the lifeblood of AI: data.
While the industry’s spotlight often shines on GPUs and ultra-fast interconnects, some of the most strategic innovation in the AI era is happening behind the scenes, in storage. Specifically, in cold and nearline storage, where data economics and architecture are being redefined.
Disaggregated Storage: The Warehouse Model for the AI Factory
In a real-world factory, you don’t store all your materials on the assembly line. You keep only what you need for immediate production, while the rest is safely warehoused.
The same principle applies to AI infrastructure. High-performance flash or RDMA storage is like the assembly line: fast, precise, and expensive. But the bulk of the material, the terabytes and petabytes of training data, logs, and historical models, belongs in the warehouse: cold storage.
This is where disaggregated storage becomes essential.
By separating compute, network, and storage layers, disaggregated storage allows each to scale independently. It enables organisations to leverage low-cost, low-power, commodity cold storage for the majority of their data while keeping only the performance-critical data on high-speed tiers.
This architecture underpins Cloud Cold Storage, which combines a cloud-native software layer from Geyser with proven Spectra Logic BlackPearl and Cube systems in Digital Realty locations. These hardware platforms bring decades of durability and enterprise reliability to a new generation of cloud storage, offering the best of both worlds: the economics of commodity infrastructure and the stability of field-tested enterprise hardware.
Data Migration: The Supply Chain of the AI Factory
In any physical factory, the assembly line is only as efficient as the supply chain feeding it. If materials arrive late or damaged, the whole production slows down.
The same holds true for AI factories. Before data can even enter the production pipeline, it must often be migrated from on-prem systems, legacy archives, other cloud vendors or repositories.
This process, often overshadowed by discussions about “ingest speeds”, is one of the biggest bottlenecks in modern data workflows. Moving petabytes across varying network environments can be slow, complex, and error-prone.
Cloud Cold Storage addresses this challenge directly. Its dedicated data migration service efficiently supports large-scale transfers, acting as the supply chain infrastructure of the AI factory and ensuring that raw materials (data) arrive securely, predictably, and at scale.
With S3-compatible endpoints and optimised transfer tools, Cloud Cold Storage enables organisations to move from fragmented on-prem or multi-cloud silos into a cohesive operational “warehouse”, a data supply chain that keeps the AI production line running smoothly.
The Cold Storage Play in AI Factory Workflows
Just as car manufacturers separate assembly, storage, and logistics, AI factories must structure their data operations into tiered workflows that balance cost, speed, and accessibility.
Factory robots do not store raw materials; they use them.
1. Cost Efficiency at Scale
AI workloads generate enormous datasets: training inputs, model checkpoints, inference logs, and compliance archives. Keeping everything on high-speed flash storage is economically untenable.
Cold storage is the warehouse that makes the AI factory viable. It allows organisations to store vast datasets on low-cost, low to no-power durable infrastructure, reserving high-speed media for time-sensitive operations.
Cloud Cold Storage’s disaggregated design amplifies this efficiency by decoupling capacity growth from performance tiers, avoiding lock-in and runaway costs and power usage.
2. Workflow Integration Across the Data Lifecycle
AI factories are dynamic: models are constantly refined, retrained, and redeployed. That means data moves fluidly between hot, nearline, and cold tiers.
A typical data lifecycle might look like this:
Ingest ➡️ Prepare ➡️ Train ➡️ Deploy ➡️ Archive ➡️ Retrain
When datasets are inactive, they move to cold storage and use little to no power. When needed again, they’re recalled quickly through an S3 interface, much like retrieving components from a warehouse to restart a production line.
This rhythm ensures cost and power-effective continuity: data circulates through the AI factory like materials through a supply chain, never wasted, always available.
3. Data Governance, Versioning, and Provenance
In manufacturing, traceability ensures quality, knowing which batch, supplier, or process produced each part.
AI factories need the same assurance. They must know which datasets trained which models and when. Cold storage enables this through durable object metadata, versioning, and indexing, ensuring compliance, reproducibility, and auditability, all at a fraction of the cost of hot storage.
4. Scalability and Future-Proofing
Factories expand by adding warehouse space. AI factories do the same by scaling cold storage.
As data grows exponentially, with data flowing in from IoT, telemetry, migrated data, and other multi-modal sources, disaggregated cold storage enables seamless data expansion without overhauling the entire compute infrastructure.
With Spectra Logic’s BlackPearl and multi-petabyte Cube systems at its core, Cloud Cold Storage can scale organically while maintaining predictable cost and performance profiles for our customers.
5. Durability and Simplicity
Factories depend on infrastructure that works, conveyors, forklifts, and storage racks that run daily without fail.
Cold storage requires the same reliability. Cloud Cold Storage inherits decades-tested durability from Spectra Logic hardware used across industries, from research to high-performance media. Combined with a simple S3 interface, this gives organisations enterprise-grade dependability with cloud-native simplicity, not the fragility of unproven start-up architectures. Add to that our service offers options to air-gap data to prevent accidental deletion and ransomware, plus data replication across sites.
Designing the Multi-Tier AI Factory Stack
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Active training, inference, and real-time pipelines.
Just like a factory assembly line — fast, precise, expensive
NVMe, RDMA, SCM -
Staging, retraining, and recent model versions.
Like a work-in-progress / development area or bespoke component area, it is close and ready to use.
SSD/HDD hybrid, NAS
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Archival datasets, checkpoints, and compliance.
Just like the component warehouse in a factory, it is vast, secure, and economical.
Commodity object storage, Cloud Cold Storage
By orchestrating movement between these tiers, just as logistics and supply chain managers balance throughput and inventory, organisations achieve optimal cost, performance, and scalability across their AI operations.
*With cloud cold storage, retriving data in seconds, we discuss the case for a new storage tier, ‘cool nearline storage’, in this recent blog
Addressing Challenges: Managing the Supply Chain of Data
No factory runs without friction. The same is true in AI.
Latency and Recall – Cold storage has longer retrieval times; stage data into nearline tiers before use.
Lifecycle Automation – Use metadata and policies to automate migration between tiers.
Migration Bottlenecks – Plan network capacity and parallel transfers; Cloud Cold Storage provides our migration service to simplify this.
Egress Costs – Treat data retrieval like logistics: consolidate, plan routes, and avoid unnecessary movement.
Security and Durability – Every safe factory relies on sound engineering; Cloud Cold Storage’s foundation is encryption, redundancy, and proven hardware integrity.
Why This Matters Now
The AI infrastructure boom is an arms race for compute, but compute is only as useful as the data supply chain supporting it.
The bottleneck of the AI era isn’t GPU capacity; it’s the inefficiency of storing and moving data at scale. Without affordable cold storage, organisations must choose between keeping history and controlling cost.
Cold storage removes that trade-off. It provides the warehouse space every AI factory needs: vast, dependable, and economical, ensuring no valuable data is discarded for budgetary reasons.
And while Cloud Cold Storage may be a new brand, it’s built on decades of proven technology and enterprise trust. Backed by Spectra Logic’s field-tested hardware, a well-funded foundation, Digital Realty's global locations and modern cloud integration, it offers organisations a low-risk, high-value way to extend their AI storage strategy with confidence.
Conclusion - rolling off the production line
Just as no car factory would clutter its assembly line with crates of unused materials or waste pressious power, no AI factory should fill its flash tier with cold, seldom-used data.
Disaggregated, commodity-based cold storage is the warehouse of the AI age, keeping data accessible, affordable, and durable for future intelligence production.
Cloud Cold Storage embodies this principle: proven hardware, simple cloud integration, and transparent economics for an era when data is the new raw material.
In the AI factory, yesterday’s data becomes tomorrow’s advantage, and cold storage keeps that advantage safe, affordable, and ready for use.
Further Reading
AI Driving Data Centre and Storage Transformation – Blocks & Files
Future-Proof Data Centres to Power the AI Factory Boom – SiliconANGLE
AI Factories Must Deliver Scalable Intelligence – SiliconANGLE
Designing Outcome-Driven AI Factory Architecture – Wesco Knowledge Hub
AI Data Centres are Swallowing the Worlds Memory - Toms Hardware
