Backblaze and WEKA Simplify Data Management Across the AI Lifecycle

Backblaze and WEKA are offering a pre-validated integration that links WEKA NeuralMesh performance storage with Backblaze B2 capacity storage for AI workloads.

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Collaboration gives AI infrastructure teams a validated solution for the AI data lifecycle, protecting engineering resources and preserving efficiency and performance at scale

SAN FRANCISCO & CAMPBELL, Calif.--(BUSINESS WIRE)-- Backblaze, Inc. (Nasdaq: BLZE), the high-performance capacity storage layer for AI, and WEKA, the AI data and memory infrastructure company, today announced a collaboration designed to simplify how AI teams manage data across the AI lifecycle. As AI teams generate enormous volumes of data across ingestion, training, checkpointing, inference, and downstream workflows, the solution gives customers a validated way to keep performance-sensitive workloads on WEKA NeuralMesh while retaining large datasets, checkpoints, and outputs in Backblaze B2, with the integration, sizing, tuning, and testing already done.

“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. Together, AI teams get a qualified, complete solution to ensure fast and efficient production,” said Gleb Budman, CEO, Backblaze.

WEKA's NeuralMesh is built for performance-intensive AI and accelerated computing environments, delivering predictable performance as workloads, datasets and GPU clusters scale. Backblaze B2 provides cloud object storage capacity for large datasets and retained AI assets. The work validates the two platforms together, so customers can deploy a proven integration instead of building and testing one themselves.

“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,” said Nilesh Patel, Chief Strategy Officer, WEKA. “Our collaboration with Backblaze gives customers a validated path to both—without the cost of building and testing that integration themselves. Speed where it matters, scale wherever you need it.”

Customers can retain raw, unstructured data (training sets, media libraries, source files) in Backblaze B2. When those datasets become part of a performance-sensitive workload, they can be made available to NeuralMesh and served to accelerated compute. Checkpoints, outputs and other assets that no longer require high-performance access can be retained in B2 for reuse in future workloads or in situations where recovery to an earlier stage of testing is necessary. NeuralMesh's Snap-to-Object capability, tested with Backblaze, lets teams revert to a checkpoint from a training run or recover saved inference data without improvising a fix mid-run, pulling from the same B2 capacity tier everything else already lands in.

Availability

Certification of B2 Cloud Storage for NeuralMesh is underway. Customers can contact Backblaze or WEKA to get started.

About Backblaze

Backblaze (NASDAQ: BLZE) is the high-performance capacity storage layer for AI. Built over two decades, the company has turned hardware, software, and operational innovation into a platform that delivers the performance and economics the AI era demands — without lock-in. Today, more than 500,000 customers trust Backblaze to move and store the data powering their businesses, reaching hundreds of millions of end users across 175 countries. For more information, visit www.backblaze.com.

About WEKA

WEKA is the AI data and memory infrastructure company transforming the economics of agentic AI. Its NeuralMesh™ platform unifies high-performance data storage with extended GPU memory, giving enterprises, AI cloud providers, and AI builders a single foundation for training, inference, and agentic workloads. Trusted by 30% of the Fortune 50, WEKA enables organizations to scale AI faster, optimize GPU utilization, and reduce the cost of every token served. Learn more at www.weka.io.

Cautionary Note Regarding Forward-looking Statements

This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which involve risks and uncertainties. These forward-looking statements are frequently identified by the use of forward-looking terminology that relate to our future performance, expectations, strategy, plans or intentions. Our actual results could differ materially from those stated in or implied by the forward-looking statements in this press release due to a number of factors, including those under the caption “Risk Factors” and elsewhere in our Quarterly Reports on Form 10-Q and other filings and reports we make with the SEC from time to time.

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View source version on businesswire.com: https://www.businesswire.com/news/home/20260909312569/en/

Backblaze
Renatta Siewert
press@backblaze.com

WEKA
pr@weka.io

Source: Backblaze, Inc.

Key Terms

object storage technical

Object storage is a way of saving digital files where each item is kept as a single unit with a unique name and descriptive tags, like labeled bins in a huge warehouse instead of folders on a shelf. Investors care because it scales cheaply for vast amounts of unstructured data (photos, backups, logs), influences cloud and storage costs, supports fast retrieval and compliance, and can be a driver of recurring revenue and margin for tech and cloud providers.

unstructured data technical

Unstructured data is information that doesn’t fit neatly into rows and columns—think emails, reports, images, audio, and social media posts rather than spreadsheets. Like a cluttered attic full of useful items, it can hide customer feedback, operational problems or market trends that matter to investors; extracting those signals requires special tools, and firms that do so can gain insights that improve forecasts, reduce risk and create competitive advantage.

accelerated compute technical

Computing that uses specialized processors or hardware—such as GPUs, TPUs, FPGAs, or dedicated accelerators—to run specific, math-heavy tasks much faster than a general-purpose CPU. It matters to investors because it shapes a company’s ability to handle large-scale AI, machine-learning, or high-performance workloads, affecting product capability, operating costs and capital spending; think of it like swapping a multi-tool for a power saw when you need to cut through a lot more wood quickly.