Wasabi Technologies has launched a dedicated AI business in Silicon Valley. Wasabi Technologies also appointed semiconductor and storage veteran Pinaki Mukherjee to lead the business. Mukherjee will serve as senior vice president and general manager. He will oversee the new unit as AI data demand continues to grow.
Wasabi already stores hundreds of petabytes of AI data worldwide. Its customers include frontier model labs, generative AI startups, and neocloud compute providers. The new business will create a dedicated focus for this growing demand. It will also strengthen Wasabi’s strategy, partnerships, and go-to-market efforts. The company expects AI infrastructure to continue evolving rapidly. Therefore, Wasabi aims to support organizations that need flexible and independent storage.
“Mukherjee has a track record of driving the kind of high-value partnerships that move markets, and that’s exactly what this moment requires,” said Marty Falaro, president and COO of Wasabi. “AI workloads are pushing storage demand to a scale we’ve never seen, and Mukherjee is the right person to build the partnerships that extend Wasabi’s position as the industry’s choice for cloud storage, at the exact moment inference is reshaping what that storage needs to do.”
Pinaki Mukherjee Brings Silicon Valley Experience
Mukherjee brings more than 20 years of technology ecosystem experience. His background covers semiconductors, storage, and AI infrastructure. He will lead Wasabi’s AI strategy from Silicon Valley. He will also manage partnerships and go-to-market activities for the new business. His career includes several major strategic achievements. Mukherjee helped generate more than $2 billion through partnerships.
He also contributed to more than $10 billion in strategic M&A and investment outcomes. His previous roles included senior positions at Fungible, Druva, and Western Digital. In addition, Mukherjee led semiconductor strategy and AI infrastructure engagements at Alvarez & Marsal. The firm operates as a global consulting organization.
His experience positions him to develop strategic relationships across the AI infrastructure market. Consequently, Wasabi expects the new leadership to accelerate its AI business plans. The company believes strong partnerships will become increasingly important. AI companies need infrastructure that can support changing workloads and growing datasets.
AI Demand Changes the Storage Infrastructure Market
Storage now represents a major part of AI infrastructure budgets. AI training workloads can access the same datasets tens or hundreds of times. At the same time, inference pipelines continuously move data between storage and GPU compute. Therefore, AI workloads require storage environments that can handle frequent data movement. Traditional hyperscaler pricing models may not match these changing requirements. AI workloads often move data repeatedly across different environments.
Furthermore, companies increasingly want to avoid vendor lock-in. Organizations want flexibility as AI infrastructure continues to change. Wasabi’s framework addresses these requirements across the AI lifecycle. The approach allows data to move to wherever compute operates.
Wasabi provides this model at a flat and predictable cost. The framework also eliminates egress and API fees. As a result, businesses can reduce concerns around unexpected infrastructure expenses. They can also maintain greater flexibility when selecting AI infrastructure. Under Mukherjee’s leadership, Wasabi plans to bring this framework to the AI industry at scale.
“AI customers don’t need another hyperscaler, they need the freedom to move their data wherever their workloads take them, without egress fees, API fees, or lock-in dictating their architecture,” said Mukherjee. “Wasabi is already the independent choice that innovative enterprises, neoclouds, and data platforms turn to as they scale beyond what any single hyperscaler ecosystem can offer. I’m looking forward to building the ecosystem that scales that freedom across the AI ecosystem.”
Wasabi Expands AI Infrastructure Capabilities
Wasabi has been building out its cloud storage infrastructure for the past decade. The company now has petabytes of AI data for its customers in global markets. There are 16 storage regions globally. Wasabi has 18,000 channel partners and has raised more than $700 million in funding. The company is also continuing to build its AI infrastructure position through strategic alliances. Its deal with Megaport is part of its wider cloud and AI infrastructure approach.
Wasabi is also expanding capabilities for developers and agentic AI. These include Wasabi MCP. Meanwhile, autonomous AI agents are assuming more responsibilities over the data lifecycle. These tasks involve retrieving training datasets and checkpointing intermediate results. AI agents can also transfer data between different compute environments. These tasks can be performed without the intervention of humans. Predictable storage costs have become more important hence. AI agents may have to read, write and move data a lot.
Wasabi allows for these operations without adding the cost to every API call. This model allows organizations to predictably manage their AI workloads. Recent deployments also illustrate the potential return on investment. An image and video generation lab pulled 175 petabytes out of a hyperscaler in months. Another deployment was from a robotics data consortium which stored multiple petabytes. The organization expects to achieve annual savings of over JPY 100 million.
These deployments indicate a growing need for standalone AI storage. They also illustrate how companies are rethinking cloud storage strategies. As AI workloads grow, Wasabi also plans to deepen its involvement in AI infrastructure. That strategy will be supported by the new Silicon Valley business. The company’s main areas of focus are partnerships, AI storage and flexible infrastructure. Mukherjee will also head up Wasabi’s efforts to grow its presence across the AI ecosystem.
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News Source: Businesswire.com
