This distributed data storage startup wants to take on Big Cloud

Summary

The explosion of AI companies has pushed demand for computing power to new extremes, and companies like CoreWeave, Together AI and Lambda Labs have capitalized on that demand, attracting immense amounts of attention and capital for their ability to offer distributed compute capacity. But most companies still store data with the big three cloud providers, AWS, Google Cloud, and Microsoft Azure, whose storage systems were built to keep data close to their own compute resources, not spread across multiple clouds or regions. “Modern AI workloads and AI infrastructure are choosing distributed computing instead of big cloud,” Ovais Tariq, co-founder and CEO of Tigris Data, told TechCrunch. “We want to provide the same option for storage, because without storage, compute is nothing.” Tigris, founded by the team that developed Uber’s storage platform, is building a network of localized data storage centers that it claims can meet the distributed compute needs of modern AI workloads. The startup’s AI-native storage platform “moves with your compute, [allows] data [to] automatically replicate to where GPUs are, supports billions of small files, and provides low-latency access for training, inference, and agentic workloads,” Tariq said. To do all of that, Tigris recently raised a $25 million Series A round that was led by Spark Capital and saw participation from existing investors, which include Andreessen Horowitz, TechCrunch has exclusively learned. The startup is going against the incumbents, who Tariq calls “Big Cloud.” Ovais Tariq, CEO of Tigris, at a Tigris data center in Virginia.Image Credits:Tigris Data Tariq feels these incumbents not only offer a more expensive data storage service, but also a less efficient one. AWS, Google Cloud and Microsoft Azure have historically charged egress fees (dubbed “cloud tax” in the industry) if a customer wants to migrate to another cloud provider, or download and move their data if they want to, say, use a cheaper GPU or train model...