Backblaze and WEKA have validated their two platforms together for AI pipelines, pairing WEKA NeuralMesh as the performance tier that feeds GPUs with Backblaze B2 Cloud Storage as the capacity tier that holds everything else. The integration, sizing, tuning, and testing are already done, so an AI infrastructure team can deploy a proven two-tier layout without building and qualifying its own. Certification of B2 for NeuralMesh is underway, and both companies say customers can contact either one to get started now.
Two Tiers, One Data Lifecycle #
Raw, unstructured data, meaning the training sets, media libraries, and source files, lives in B2. When a dataset becomes part of a performance-sensitive job, it’s made available to NeuralMesh and served to the accelerators from there. Once a checkpoint, an output, or any other asset no longer needs high-performance access, it goes back to B2, where it can be reused in a later run or pulled back if a job has to recover to an earlier stage. The companies frame it as speed where the GPUs are and capacity everywhere else, with the data moving between the two as its access pattern changes.
“AI teams need their GPUs fed and an infrastructure with the performance and capacity to support the full AI data workflow. WEKA has mastered the performance tier. We’ve spent nearly two decades doing the same for capacity storage,” said Gleb Budman, CEO of Backblaze. Nilesh Patel, Chief Strategy Officer at WEKA, described the same pressure from the other direction: “AI workloads are stretching storage in two directions at once. GPUs need microsecond access to data to stay fed, while datasets and checkpoints are growing to exabyte scale. Our collaboration with Backblaze gives customers a validated path to both, without the cost of building and testing that integration themselves.”
Snap-to-Object Tested Against B2 #
Another interesting piece is NeuralMesh’s Snap-to-Object, which the companies say they’ve tested with Backblaze. Snap-to-Object writes a consistent snapshot of a NeuralMesh file system out to an object store, and with B2 as the target, that store is the same capacity tier the raw data and retired checkpoints already end up in. For a training run, that means a team can revert to a checkpoint or recover saved inference data from B2 without improvising a fix in the middle of the job, and without maintaining a separate destination for snapshots. The economic effect is a cloud object tier behind NeuralMesh that’s priced as capacity, holding the snapshots alongside the data they protect.
Backblaze has been positioning B2 as the capacity layer for AI throughout the year, from the B2 Neo offering for neocloud platforms to its performance benchmarking program, and WEKA gives it a performance-tier partner on the GPU side of that.