Blackstone Invested $300 Million in DataDirect Networks
The data infrastructure firm reached a $5 billion valuation following the January 2025 funding round.
Updated on Oct. 6, 2026 in Data Centers
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DataDirect Networks, founded by Alex Bouzari and Paul Bloch in 1998, secured a $300 million investment from Blackstone in January 2025. The funding round valued the company at $5 billion as it continues to support large-scale AI operations.
Why it matters
The company sought a strategic partner to expand its high-performance data systems to enterprises and nations actively building AI capacity. The partnership aims to scale data engines that optimize GPU profitability and productivity globally.
DataDirect Networks currently supports more than 500,000 NVIDIA GPUs and maintains a client base of over 85 Fortune 500 companies. The firm successfully increased GPU productivity at Salesforce by 70%.
The players
DataDirect Networks
This technology firm specializes in high-performance storage solutions designed to support intensive AI and machine learning workloads.
Blackstone
This multinational private equity firm is one of the world's largest alternative asset managers and leads major investments in infrastructure and technology.
Alex Bouzari
He is a co-founder of DataDirect Networks and has spent 25 years developing specialized data systems for high-performance computing.
Paul Bloch
He co-founded DataDirect Networks in 1998 and serves as a key architect in the company's long-term development of storage engines.
The details
DataDirect Networks designs high-performance data systems specifically engineered to feed large-scale AI infrastructure. By developing specialized data engines, the company helps organizations improve the profitability and output of their hardware investments.
Timeline
Alex Bouzari and Paul Bloch founded the company in 1998.
DataDirect Networks received $300 million in funding from Blackstone in January 2025.
The company projects annual revenue to reach $1 billion in 2026.
The Tech Race
The company positions itself as a critical layer in the AI arms race by solving the bottleneck of data delivery for massive GPU clusters. This development marks a transition from legacy storage models to specialized engines capable of sustaining the high-throughput requirements of generative AI.
The firm's ability to boost GPU productivity directly influences the speed and cost of training large-scale AI models used in modern consumer and enterprise software. These infrastructure improvements ultimately reduce the processing time and operational overhead for global technology services.
The takeaway
The firm's focus on high-performance data engines underscores how storage architecture has become as vital as processing power in the modern AI economy. Enterprises and nations alike are increasingly prioritizing specialized hardware integration to maximize the utility of their compute assets.
Further reading
Learn more about the infrastructure behind massive compute clusters in our Data Centers section.
Source note: This article includes information reported by The AI Software Report.
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