Backblaze

Long time readers: I must start with a mea culpa that this article was not released on our traditional May the Fourth (or thereabouts) report date. Please know I missed making the many and sundry Star Wars puns as much as (I’m sure) you missed hearing them—so, I’ll probably slip a few in, just because. That said, there was good reason for the delay. We were diligently investigating a disturbance in the force. (See?

On April 7, 2026, Anthropic announced a model so capable they refused to release it publicly. Claude Mythos, their most advanced frontier AI, was deemed too dangerous for open access because of one thing: it can hack. Not in the vague, theoretical sense that security researchers worry about. In the “completed 73% of expert-level capture-the-flag challenges that no AI could touch before April 2025” sense.

Backblaze, Inc. (Nasdaq: BLZE), the cloud storage platform for the AI era, today announced an agreement with CoreWeave, Inc. (Nasdaq: CRWV), The Essential Cloud for AI™. Under the multi-exabyte, $335 million agreement, Backblaze will provide cost-efficient storage capacity that supports portions of CoreWeave’s managed storage infrastructure, helping optimize placement of data across performance tiers while preserving high-performance storage resources for the demands of AI workloads.

The generative AI wave is moving from text into media. ChatGPT taught a generation of developers how to wire up an LLM. The next chapter is harder and more interesting: video, image, audio, and multimodal workflows that combine them. New models for each of these are landing every few weeks, from established labs and from teams nobody had heard of a quarter ago. The quality keeps climbing, and the catalog keeps expanding. That pace is good news, but it asks more of the apps that depend on it.

Customer records live in the database. Payment activity is safely stored in your payment processor. Call recordings and transcripts live in Zoom, Teams, Webex, or another video conferencing application–or are shared to Gong for customer insights. Telemetry resides in an observability tool like Grafana or DataDog. Your own day-to-day work is in Google Drive or OneDrive.

A year ago, a good video model was a novelty. Today there are at least six worth using, and most of the teams we talk to are wiring up two or three of them into the same product, alongside image models, voice synthesis, and music generation. The hard question isn’t whether you can generate this kind of media. It’s how to build a pipeline that handles five providers without falling over.

AI infrastructure is difficult to observe directly. That’s because AI systems are highly distributed. They span storage platforms, GPU clusters, training pipelines, and multiple cloud providers—and those looking to optimize their stacks have to build out a visibility layer that takes all of those things into account. Data is constantly moving between different tools and infrastructure layers, and it’s imperative to understand how and why.

Two-thirds of enterprise leaders see significant potential in integrating AI models with their proprietary data. Yet only 22% feel confident their current IT infrastructure could actually support new AI applications. That gap—between ambition and capability—is where most AI investments stall, budgets balloon, and promising projects quietly get shelved. The problem isn’t the models, the change management, or even the cost. It’s the underlying data foundation.

There’s a familiar tension in enterprise technology: buy a fully integrated platform or assemble a stack of best-of-breed components. The platform story is compelling: one vendor. One console. One contract. Everything bundled together. The best-of-breed story is different. Best-of-breed optimizes each layer for performance, flexibility, and cost. Historically, platforms often won the narrative. But in periods of rapid change—like today’s AI era—modularity regains the advantage.

AI cloud platforms have spent the last few years competing on GPU availability, interconnects, and cluster performance. That makes sense. GPUs are the engine of AI infrastructure, and demand for them has been relentless. But a GPU can only work as fast as the data pipeline feeding it. Think of it like a fleet of race cars. You can have the best cars on the track, the fastest pit crew, and a perfect race strategy.