Data Stories From 2025: What Our Customers Taught Us About Cloud Storage

Case studies are our field notes. They capture the unpolished truth so we can separate the outcome from the conditions that made it possible, whether those are budget realities, operational constraints, or what happens on a bad day. 

In 2025, we featured customers across nearly every industry, each with a different set of challenges on paper: a teaching hospital trying to close backup gaps, a global broadcaster dealing with decades of audio, a university overwhelmed with research data, a pro sports league trying to serve fans more content, a gym network wiring its locations for AI video analytics.  

Most teams started with a straightforward mandate: close protection gaps, modernize aging infrastructure, or tackle an overdue archive. But once fast storage and predictable costs were in place, a new set of trends emerged. The conversation has moved past capacity as the headline and into innovations that support true cyber resilience and future-readiness. 

These examples are signals of where data strategy is headed as we enter 2026.  

Active archiving as the new default 

Archiving was one of the clearest indicators of how storage expectations are changing. The common thread wasn’t just “we need a cheaper place to put old data.” It was “we need data we can keep and still use.” Across industries, archives are staying live: searchable, retrievable, and ready for day-to-day pulls, audits, and operational needs. And as soon as access becomes slow, complicated, or fee-triggering, teams stop treating the archive as an asset and start treating it as a risk.  

For media companies like iHeartMedia, archives aren’t a historical record sitting on a shelf. Past broadcasts support operations and get accessed regularly, which makes tiered storage a problem: complexity goes up, and costs get harder to predict. iHeartMedia moved its active archive to Wasabi for flat-rate simplicity, with costs 2x less expensive than other providers, zero egress fees, and an implementation that took one line of code to redirect. The platform now provides cost-effective, immutable storage for thousands of frequently accessed audio files. 

Sports organizations are seeing the same shift, just with different stakes. The Premier Lacrosse League didn’t digitize legacy footage just to preserve it. They did it so content teams could pull from the full history of the sport: highlights, features, and moments that still matter to fans today. The key difference is workflow: before Wasabi, editors could only access media on-site or through shipping hard drives back and forth; with Wasabi, the league emphasized secure, scalable storage that supports remote access and collaboration from anywhere. 

In higher education and research, the pressures look different but lead to the same conclusion. At the University of Hull, research projects can generate huge datasets of up to 150 TB for a single project, some of which need to be retained indefinitely. Hull called out the budgeting challenge directly (unpredictable egress and API fees), and Wasabi made it easier to give researchers clear cost expectations. 

Taken together, these stories make for a straightforward lesson: “archive” is no longer the last stop. It’s shared infrastructure, and it has to behave like working storage without turning usage into a budgeting event. When access is predictable and the overhead stays low, archives stop being a storage problem to manage and start becoming a foundation that teams can build on. 

Storage built for always-on AI workloads 

In 2025, we saw a clear shift in what storage is being asked to do for AI-heavy use cases. It’s no longer just about where to park data cheaply; it’s about whether teams can access and work with that data constantly without performance hiccups or cost surprises. AI pipelines create a lot of small activity at scale: clips, freeze frames, indexes, and metadata that get read, written, and queried all day long. In traditional cloud models, that usage pattern is exactly where fees pile up, and it’s a big reason cloud storage budgets keep drifting after the plan is approved.  

Performance Hub’s AI surveillance platform for the UBX global gym network is a strong example of how this is playing out. Its SaaS ingests surveillance footage and AI-enriched metadata from thousands of devices in the field, and customers expect to pull data on demand without lag or timeouts. With its previous provider, storage costs climbed quickly, performance issues degraded the user experience, and even basic usage reporting for customer billing was expensive and time-consuming. After moving video and metadata to Wasabi, Performance Hub reported 4–6× lower cloud storage costs, <1-minute bucket configuration time per region, and 20× cheaper customer billing calculations versus AWS.  

What’s important here is why those results matter for AI-ready workloads. This was about removing friction in the loop: eliminating direct-from-device upload timeouts, handling high volumes of small files, and keeping API-driven access intact while meeting regional data residency requirements. In other words, storage had to behave like infrastructure the platform could build on, not a cost center that punishes usage. When performance stays steady, and costs stay predictable, AI stops being a budgeting debate and starts being something teams can scale. 

Healthcare and the shift toward hot cloud storage 

Healthcare organizations don’t experiment lightly. Systems support clinical care, research, and compliance, so failure isn’t an option. That’s why many healthcare teams start their cloud journey cautiously, often with backup, because it’s the fastest way to reduce risk without disrupting workflows. 

In several conversations, healthcare IT leaders described the same pain points: partial backup coverage, aging tape systems, long restore times, and growing anxiety about whether systems could recover fast enough when it mattered. 

At Kansas University Medical Center, the pain was classic and expensive: legacy, tape-based backups with outdated agents, manual scheduling, spreadsheet tracking, and restores that could take several days. Backup coverage was also constrained; only ~40% of server data was protected. With HYCU and Wasabi, they eliminated tape workflows, expanded coverage to 100% of application servers, and turned days of recovery work into restores completed in a few clicks, the kind of operational calm healthcare teams actually notice. 

Imperial College Healthcare NHS Trust saw the same dynamic from a different angle: digital pathology images created massive archive growth, but cloud storage options were mostly cold; retrieval could take 12–14 hours. With Wasabi Hot Cloud Storage, clinicians now retrieve 1.5 GB pathology images in as little as 30 seconds without lodging a support ticket, while avoiding a scheduled five-year on-prem refresh cycle. In other words, the archive stayed fast enough to feel local, without forcing more infrastructure back on-prem. 

Across both stories, the themes are consistent: predictable access matters as much as raw performance, especially when teams can’t afford surprise costs tied to retrieval, and retiring tape and refresh cycles gives healthcare IT time back and confidence that storage won’t be the weak link when it matters. 

Expansion to new use cases 

The most telling success metric in a case study isn’t the first project; it’s what happens after it. Once teams adopt Wasabi for a core need and get comfortable with the model, it becomes much easier to spot the next place storage is getting in the way. The buying motion shifts from solving this one storage problem to identifying where else we can simplify, because the operational approach is already proven and the economics are already understood.  

Berry College is a great example of that expansion in action. After successfully adopting Wasabi for critical backups, the team came back in 2025 to extend Wasabi into two new projects: surveillance video and marketing archives, using Wasabi Surveillance Cloud and Wasabi Cloud NAS.  

Surveillance systems were generating 4–5 TB per day, quickly overwhelming local servers and making it harder to meet retention requirements without expensive on-prem upgrades. Their marketing archive was also nearing capacity, with decades of photo and video content that still needed long-term retention and on-demand access. Berry’s approach was practical: keep several weeks of footage on-site for quick retrieval, then push older video to the cloud for retention and DR/backup, while moving archived marketing media into scalable cloud storage.  

This is part of the broader pattern we saw in 2025: when storage is fast, predictable, and easy to operate, it stops being a constraint and becomes something teams can build on. Berry is a clear example of how that foundation supports the next workload without multiplying complexity. 

2026: A look ahead 

Based on what we heard in 2025, the shift is clear: cloud storage is being judged less on raw capacity and more on whether teams can use data without friction or financial surprises. “Archive” is staying live. AI and video-heavy workloads are increasing access frequency. And the strongest signal that a storage strategy is working is what happens next: teams reuse the same foundation for a second and third use case because it’s predictable to operate and easy to extend. 

That foundation is only going to matter more in 2026. Active archiving will keep growing as organizations hold onto and use more data. Hybrid workflows will continue to take shape as teams connect on-prem systems with cloud storage. As AI becomes more operational than experimental, storage will be expected to keep up with constant reads, writes, and metadata at scale without turning every access pattern into a budgeting event. 

The reason these patterns matter is that they’re not theoretical. They show up in the decisions teams make when budgets are tight, risk is high, and the business expects data to be available on demand. We’re grateful to the organizations who shared their time and experiences with us. Their stories help shape how we think about storage and help others see what’s possible. 

If you’re navigating similar questions about backup, archiving, or what comes next after moving data to the cloud, we’d love to hear your story, too.