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

• Hot Tier

  Active training, inference, and real-time pipelines.

  Just like a factory assembly line — fast, precise, expensive
  
  NVMe, RDMA, SCM

• Nearline Tier*

  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

• Cold Tier

  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 Factories: Data Centres of the Future – SiliconANGLE

• 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

• Standard-Triumph Motors Ltd - Britain by Car

• AI Data Centres are Swallowing the Worlds Memory - Toms Hardware

• Powering the AI Revolution - Morgan Stanley

Don’t Keep All Your Data Eggs in One Basket

Don’t Keep All Your Data Eggs in One Basket: Cloud Durability and the Case for Multi-Cloud

Rethinking Cloud Durability

When it comes to storing business-critical data, most organisations assume that if it’s in the cloud, it’s safe. After all, the big names—AWS, Google Cloud, and Microsoft Azure—promise staggering durability levels like “11 nines” (99.999999999%) and near-perfect uptime.

But as the old saying goes, don’t keep all your eggs in one basket. The reality is that even the largest cloud platforms can (and do) fail. And when they do, it doesn’t just cause inconvenience—it can take entire global services offline.

Recent Outages: Proof That Perfection Doesn’t Exist

In recent months, all three hyperscalers have experienced significant outages:

• AWS outage – As reported by The Verge, major AWS infrastructure issues took down services including Alexa, Fortnite, and Snapchat. For hours, millions of users couldn’t access applications they rely on every day.

• Google Cloud outage – Mashable detailed how a Google Cloud networking issue cascaded into widespread downtime across multiple regions, impacting developers, analytics platforms, and customer-facing apps.

• Microsoft Azure outage – According to BleepingComputer, Azure downtime blocked access to Microsoft 365 services and admin portals—paralysing business users who depend on Teams, SharePoint, and Outlook.

These incidents show that even providers with “11 nines” durability claims aren’t immune to disruption. Whether due to software bugs, networking faults, or configuration errors, the outcome for end customers is the same—downtime and inaccessibility.

The Hidden Risk of One-Cloud Dependence

For many organisations, consolidating all infrastructure and data in a single provider’s ecosystem feels convenient—centralised management, unified billing, and straightforward integrations. But that simplicity comes at a cost.

When your workloads, backups, and archives all reside in the same environment, any provider-level outage becomes your outage. Even if the data itself isn’t lost, access interruptions can cripple operations, compliance workflows, or customer services.

The dependency risk goes deeper:

• Egress and retrieval fees make moving data between clouds expensive.

• Proprietary storage architectures make migration complex.

• Latency and compatibility issues can deter multi-cloud adoption.

Yet, when something goes wrong, you’re reminded how fragile single-provider strategies can be.

A Different Approach: Building Real Resilience

Cloud Cold Storage is built on a foundation of Digital Realty data centres, which have delivered 99.999% uptime for over a decade—that’s less than 50 minutes of downtime in ten years.

Unlike the hyperscalers’ massive shared environments, Cloud Cold Storage leverages global, independent data centre infrastructure, offering stability and control without the overheads or lock-in.

The key message: durability and accessibility aren’t just about percentages—they’re about architecture, diversity, and redundancy.

Spreading Your Data Eggs: Multi-Cloud and Third-Party Backups

No single cloud can promise uninterrupted access forever. The smartest strategy isn’t to rely on one—it’s to build resilience through diversity:

1. Use a multi-cloud approach – Store critical workloads or backups across different providers to eliminate single points of failure.

2. Leverage third-party cold storage providers – Services like Cloud Cold Storage offer low-cost, high-durability storage without punitive egress fees, ideal for secondary or tertiary backups.

3. Test recovery procedures regularly – Backups are only as good as your ability to restore them quickly.

By taking a layered approach, you ensure that even if one provider goes offline, your business doesn’t.

The Bottom Line

The cloud is powerful—but not infallible. When AWS, Azure, or GCP experience outages, even the biggest brands go dark.

In a world where uptime, compliance, and customer experience depend on data accessibility, it’s time to stop keeping all your eggs in one basket. Distribute your data, diversify your providers, and protect your business against the unexpected.

A New Cloud Storage Tier?

When you are paying around $10–$20 per GB per month for the fastest in-memory cache layer in the cloud, $0.015 per GB per month for Nearline object storage might seem cheap as chips. But as anyone managing petabyte-scale storage estates knows, the older and colder the data becomes, the less frequently accessed it is, the more sensitive cloud storage economics become.

Unlike expensive NVMe or DRAM, which lose data when powered off, cold data is persistent. Because cloud archive data is often measured in petabytes (PBs) rather than gigabytes, costs mount quickly. Even at a fraction of a cent per GB, storing multiple petabytes over many years becomes a strategic cost centre. Add retrieval costs, egress fees, air-conditioning, power for spinning disks, and operational management, and what looks cheap on paper can balloon in practice.

S3 to Cold Cloud Storage

As data ages, organisations weigh up two priorities: minimising cost and maintaining accessibility. Hyperscaler deep-archive tiers (e.g., Amazon Glacier Deep Archive or Azure Archive) are often positioned as the cheapest cloud option, at as little as $0.001 per GB. But these tiers are typically used for insurance backups, data not expected to be touched again, because retrieving anything substantial can be slow, operationally awkward, and surprisingly costly. In short, hyperscaler deep cold archive storage offerings are not designed to serve up production data.

The reality is that production data doesn’t vanish after its “hot” phase. Old production datasets, scientific experiments, digital media assets, compliance records, clinical archives, or AI training datasets may only be accessed occasionally; however, when needed, fast access without punitive egress fees is essential.

As Gartner noted in a 2023 report on cloud storage economics, “egress and retrieval charges remain a significant source of unplanned expenditure, often exceeding initial storage budgets for archival data projects” (Gartner, 2023).

So organisations typically store infrequently accessed production data on the HDD-based nearline storage tier rather than the environmentally friendly, lower-cost cold storage tier.

Real-World Use Cases

Media & Entertainment: Studios like A+E Networks generate petabytes of broadcast content each year. Archiving to hyperscaler storage may be cost-effective short term, but production demands quick access to old footage for remastering or licensing. As A+E’s CTO once said: “Access is everything… we don’t just want to store our content, we want to monetise it later” (Broadcast Tech, 2022).

Research & Academia: CERN produces over a petabyte of physics data every day. Much of this must be archived, but scientists still need fast retrieval for future analysis. In such environments, retrieval penalties aren’t just financial but can slow discovery (CERN Annual Report).

Healthcare & Life Sciences: Hospitals are required to retain patient imaging data for decades. A 2022 study in Applied Radiology highlighted how rising retrieval costs made AI-driven diagnostic model training prohibitively expensive for some institutions.

Financial Services: Compliance regulations often require firms to retain records for 7–10 years. A major European bank noted in 2021: “Our cloud storage costs tripled in three years, largely due to retrieval fees during audits” (The Banker).

AI & ML: AI training pipelines often handle terabytes to petabytes of historical data—from medical images to AI-generated datasets. Traditional hyperscaler archive storage incurs not only lengthy restore delays but also substantial egress costs, making experimentation, iterative model retraining and inference workloads expensive. (arXiv).

These industries highlight a shared pain point: long-term data retention with unpredictable retrieval costs if data is archived.

The On-Premises Parallel

Solutions such as Spectra Logic BlackPearl pioneered on-premises nearline S3 interfaces to complex high-capacity cold storage. Enterprises can store petabytes of production data with object storage, low-cost archive back-end infrastructure, simply storing data with little to no power requirements, all accessible via S3-like APIs, but crucially without retrieval fees. For some organisations, this on-premises model still makes perfect sense.

The barrier, however, has always been scale and skills. High upfront hardware costs, ongoing technology refreshes, and shrinking pools of storage administrators mean these complex systems are out of reach for many mid-sized and scaling organisations.

Cloud Cold Storage Becomes Nearline

Cloud Storage Providers are now challenging this status quo by offering the same enterprise-grade complex cloud cold object-based storage with a simple, seamless nearline S3 user experience and characteristics in the cloud:

• $0.0015 per GB per month storage costs, comparable to deep archive pricing.

• Access to streamed data in minutes, without rehydration delays.

• No egress or retrieval fees, removing the budget shock of restores.

For example:

• Wasabi offers hot cloud storage at around $7 per TB per month, chosen by organisations like the Boston Red Sox to manage historical video and analytics data (Wasabi Case Study).

• Customers like Verizon Media leverage Backblaze B2 Cloud Storage to handle large-scale nearline workloads at predictable costs (Backblaze Customers).

• CloudColdStorage stores data at deep archive prices and then streams that data back in minutes without expensive fees.

This allows IT teams to design data pipelines where:

• Active workloads stay on high-performance SSD/NVMe.

• Nearline low latency data moves to non-hyperscaler cloud storage (~$7 per TB) with hot storage performance.

• Cold production archives shift to more efficient cloud cold storage (~$1.55 per TB), without any restore penalties.

The result: significant long-term savings and greater predictability across multi-tier cloud storage strategies.

Store older production data for cents and use less energy retaining it.

The Numbers at Petabyte Scale

Here’s how costs stack up over 10 years for a 1 PB dataset:

• Hyperscaler block general-purpose SSD-based low ms latency hot storage: $22,800,000 (plus egress fees)

• Hyperscaler Nearline storage: $1,800,000 (plus egress fees and retrieval fees of around $1M per year to retrieve 1PB)

• Hyperscaler Deep Archive storage: $120,000 to $300,000 (plus retrieval and egress fees of around $6M per year to retrieve 1PB)

• Non-hyperscaler hot storage: $840,000 (limited or no egress fees or api calls)

• Cloud cold storage: $186,000 (no retrieval or egress fees)

At this scale, avoiding unpredictable retrieval charges is as impactful as reducing raw $/GB pricing.

Summary

• Cloud archive vs nearline is no longer a binary choice. Emerging cloud storage tiers blur the line by combining archive pricing with nearline access.

• Egress and retrieval fees are the silent budget killer; eliminating them is as important as lowering storage rates.

• Industries like healthcare, finance, media, and research all need affordable, predictable long-term storage that still enables access for compliance, monetisation, and AI training datasets.

Next Steps

1. Audit Your Data Estate: Identify how much of your current “hot” data actually needs to be kept spinning with no latency. You might be surprised at how much data can be kept cooler.

2. Model Retrieval Patterns: Estimate retrieval demand over 3–5 years; this is where hidden costs emerge.

3. Compare Cloud Archive vs Nearline Providers: Assess hyperscaler storage tiers alongside alternatives like Wasabi, Backblaze and CloudColdStorage.

4. Factor in AI and Analytics Growth: Retrieval demands are rising, not falling.

5. Run a Pilot: Benchmark access speeds, costs, and API compatibility before committing at scale.

Conclusion

The economics of cloud storage are shifting. What was once “deep archive only” is becoming viable for nearline production data. By adopting cloud cold storage with no egress or restore penalties, organisations can finally align long-term data strategy with predictable budgets.

Whether it’s a hospital safeguarding decades of scans, a studio monetising its film library, or a research lab mining historic datasets for AI, the ability to store at deep archive prices and retrieve at nearline speeds is a genuine new tier in the storage landscape, one that could redefine cloud storage economics over the next decade.

🎄 Be Prepared: Why Christmas Stocking and Cloud Storage Have More in Common Than You Think

The sun might be shining now, but Christmas is already on the horizon — just 120 sleeps away. As the Scouts say: “Be prepared.”

For many businesses, preparing for Christmas starts months in advance. A small retailer might not have the buying power of a supermarket chain, but still needs to stay competitive. A restaurant owner might prefer to focus on perfecting a festive menu rather than trawling the internet for decorations. That’s where wholesalers step in. They’ve already managed the supply chain, filled their warehouses, and made it easy for members to stock up at trade prices.

The same logic applies to data. Companies are producing and storing more of it than ever, knowing it may be needed again — sometimes in less than 120 days. But rather than sink time and money into managing infrastructure, most would rather focus on what they do best: creating brilliant content, innovating with research, or serving their own customers. Just like retailers lean on wholesalers, businesses lean on cloud providers to handle the heavy lifting of data storage and management.

But here’s the thing: The dominant wholesaler Costco may already have Christmas trees on display (see pic), but it isn’t the only one. According to IBISWorld, the UK has around 4,000 grocery businesses. A Chinese restaurant in the South East, for instance, might prefer SeeWoo over Costco for its festive menu. Different wholesalers meet different needs.

The same is true in the cloud. AWS, GCP, Azure, and IBM Cloud are household names — the Costcos of the digital world. They’re great for scalable compute and general-purpose storage. But they’re not always the best fit. ElasticSearch has a sharper solution for data analysis. Wasabi focuses on hot cloud storage at lower cost. And at CloudColdStorage, we specialise in super-low-cost, long-term data archiving.

You don’t have to wait 120 days for data to chill either. Data that’s just 30 days old can be actively archived with us — and streamed back in minutes — cutting cloud costs dramatically.

At just £1.16 per TB, you’ll even have budget left over for the Christmas party.

So whether you’re prepping your shelves or your servers, don’t limit yourself to just one “Costco” of the cloud. Explore the full marketplace.

✨ Merry Christmas from all of us at CloudColdStorage

Data increasing energy bills!

71 TWh of extra electricity required just to power future data centres

This intriguing BBC News article highlights concerns about rising energy bills due to the construction of power-hungry data centres, adding up to 71 TWh of electricity demand!

https://www.bbc.co.uk/news/articles/clyr9nx0jrzo

New data centres are being built, while existing ones, that are just a few years old, now have costly but empty floorspace. This is partly due to power restrictions imposed by local electricity boards, which limit the power available for older data centres to deploy more compute in the vacated shelves of older equipment.

These not-so-old data centres may quickly become obsolete.

Yet, there might be a surprisingly simple and quite obvious solution to reuse that empty floor space: data storage! Specifically, cold data storage. With all the focus on AI and computation, many in the industry are not considering data and how it should be correctly stored, even though data and vast amounts of it are critical for LLMs.

Once data is created, it is quickly moved to storage - something most people are familiar with from their home computers. Similarly, when data is stored in the cloud, it is staged on different tiers or classes of storage device depending on its age. Unfortunately, some cloud-based services and applications only consider the 'hot' storage tiers. We are now running out of power to support the unnecessary demands of these services. Added to that, the storage technologies available today won’t be able to keep up with the data demands of the next couple of years. New technologies like HoloMem are coming online, but they will be of no use if data is not directed to them.

It is time for more developers to adopt cold storage, so that 71 TWh of energy is not wasted spinning hard disks or cooling them.

By utilising cold storage services such as Cloud Cold Storage, slightly older data can be stored efficiently in the cloud. This service uses 97% less electricity than storing data on hard disks and restores data quicker than hyperscaler archive offerings without the retrieval and egress fees. As the service uses so little power to store so much data, data centre floor space can be utilised to store PBs of data in rackspace and aisles that are currently empty!

As more applications and online services adopt efficient data lifecycle tiering, including cold data storage tiers, then a higher percentage of data centre energy consumption can be dedicated and focused on compute rather than wasted spinning disks that hold data that is rarely accessed.

If you’re engineering data workflows, you can start saving budget, freeing up hot storage capacity and reducing energy consumption right now, start using CloudColdStorage for free today without entering credit card details and with no tie-ins.

Cool your data, use less power.

Tackling storage and budget pressures in media & entertainment workflows

From streaming blockbusters to immersive VR experiences, media production today is defined by unprecedented data demands. As projects scale in ambition and complexity, storage emerges as both a cornerstone and a cost pressure point across the industry.  Keeping pace with the data deluge introduces persistent challenges around cost, accessibility, and long-term usability. In production workflows, storage management becomes both a financial and logistical battleground.

The M&E mega-trends shaping cost and data storage landscapes

1. Explosive Data Growth in the Zettabyte Era

Global data production continues to surge. At the time of writing, we now have around 175 zettabytes of data circulating globally. For the M&E sector, this translates into storing, managing, and retrieving ever-larger media files while balancing speed, resiliency, and cost.

2. Ultra-High resolution, Immersive formats, & streaming expansion

The shift to 4K, 8K, VR/AR, and other high-definition formats is driving exponential storage growth. Meanwhile, streaming platforms continue to proliferate, each maintaining vast on-demand libraries, adding both scale and complexity to media storage infrastructures.

3. Hybrid storage architectures & automation

The prevailing trend is toward simplistic, flexible, hybrid storage models, blending on-premises, cloud, and cold tiers to optimise costs, accessibility, and performance. Orchestration systems now manage access, latency, and expenses in real-time, especially crucial for live production media workflows.

4. “Doing Less with Less”: Streamlined workflows & intelligent automation

As spotlighted at this year's NAB Show, the industry’s new mantra is simplicity: eliminate unnecessary steps, standardise workflows, and embrace AI-driven automation, so it is not just to do more, but to do less more efficiently.

5. Consolidation, monetisation, & AI-driven efficiencies

Broadcasters are consolidating operations for cost leverage, aided by generative AI tools for tagging, compliance, and content monetisation. Meanwhile, streaming platforms are moving into a profitability phase, bundling services and focusing on sports and mature franchises to increase margins.

Storage challenges in production

• The CAPEX vs. OPEX dilemma

Whether investing in on-site high-speed storage SAN/NAS, RAID arrays, or on-prem disk pools that offer the throughput essential for editing and transcoding workflows, these systems come with steep capital overheads: hardware provisioning, power, cooling, maintenance, and scale-up limitations; or optimising for a simpler fully managed storage as a service and paying ongoing cloud fees. High-performance and long-term workflows remain a financial burden, especially for mid-sized studios.

• Hidden cloud costs, egress & recovery fees

Hyperscale platforms can lure studios in with low per-GB storage costs, but unwelcome surprises in the guise of inflationary retrieval and egress fees often surface later when media sets are requested.

• On-prem data longevity & digital preservation

Owning storage media, whether drive, flash, or tape, requires lifecycle planning, format migration, and redundancy strategies to ensure access over many years. This process requires additional skills, management, and cost. Utilising an online cold storage service reduces the effort needed and provides data management and access long into the future.

• Discoverability & metadata gaps

Without effective metadata tagging and access tools, archives become opaque, undermining potential reuse, monetisation, or compliance.

Solutions that add long-term value

• Tiered storage with intelligent lifecycle management

Adopt fast tiers for active projects, simplify tiered access, and reducing the length of time before you tier down to cold storage as content ages, and automate transitions to minimise manual effort and cost with media management in between.

• Incorporate MAM/DAM tools

These offer web-based archive, backup, and metadata workflows that support on-premise disk, tape, or large long-term cloud repositories, providing automated and user-friendly solutions. Metadata-rich MAM features, such as customisable fields, previews, searchable proxies, cloning, encryption, and off-site storage management, are critical for long-term accessibility and preservation.

• Orchestration in hybrid workflows

Manage both on-site and cloud workflows seamlessly, particularly for live or peak workloads, using orchestration systems that automatically allocate resources, monitor performance, ensure security, and scale efficiently.

• For long-term storage, select providers that avoid recovery/egress fees.

New archival services from the likes of CloudColdStorage.com will save budgets over the short and long term because they are price-optimised for lower-cost storage tiers, preventing surprise restore charges as they utilise technology specifically chosen for its long-term storage credentials over millisecond access. A cloud-based cold storage service should be considered as part of a broader, diversified data storage strategy.

• Automate and simplify workflows

Use AI-assisted automation to prune redundant processes, generate metadata on ingestion, and ensure you store less, but smarter.

Next steps for media organisations

1. Assess your data footprint & storage model

Map current spending across active, nearline, and archival tiers—identify egress costs, duplication, and archival gaps.

2. Plan hybrid tiers with lifecycle automation

Define new policies (e.g., rather than wait 90 days “after 30 days inactive → cold tier”) and implement tools that trigger these transitions automatically.

3. Implement robust MAM/DAM systems

Prioritise MAM solutions for metadata-rich, search-friendly archiving, or integrate AI-enhanced DAM platforms when scale demands.

4. Use orchestration for peak and live workflows

Dynamically match compute/storage needs to production demand using orchestration systems, thereby reducing wasted capacity and spend.

5. Future-proof through metadata and AI

Ensure that stored assets are discoverable, accessible, and monetizable, both today and in the years to come, by embedding metadata generation and searchability at the start.

By understanding mega-trends—like the zettabyte explosion, immersive content, hybrid workflows, and AI-driven automation—media organisations can transform storage from a cost sink into a long-term asset. Starting with tiered storage that includes a cloud-based cold storage without egress fees, metadata-rich archiving, and smart orchestration, you build a resilient infrastructure that supports cost control, efficiency, and future reuse.

Archiving doesn't have to be a horror story.