Tigris Data just secured $25 million in Series A funding to expand its network of localized data storage centers, directly challenging the dominance of AWS, Google Cloud, and Microsoft Azure. Led by Spark Capital with participation from Andreessen Horowitz, the round positions the startup to capitalize on AI companies' growing frustration with traditional cloud storage costs and latency issues.
Tigris Data is betting that the AI boom will force a fundamental rethink of how companies store their data. The startup, founded by the team that built Uber's storage platform, just closed a $25 million Series A round to expand its network of localized data centers - positioning itself as the distributed storage layer for an increasingly decentralized computing world.
The funding, led by Spark Capital with participation from existing investor Andreessen Horowitz, comes as AI companies grow frustrated with what CEO Ovais Tariq calls "Big Cloud" - the triumvirate of Amazon Web Services, Google Cloud, and Microsoft Azure that dominates enterprise data storage.
"Modern AI workloads and AI infrastructure are choosing distributed computing instead of big cloud," Tariq told TechCrunch. "We want to provide the same option for storage, because without storage, compute is nothing."
The timing couldn't be better. While companies like CoreWeave, Together AI and Lambda Labs have attracted billions in funding by offering distributed GPU capacity, most AI companies still store their data with the traditional cloud giants. That creates a fundamental mismatch - distributed compute paired with centralized storage.
Tigris's AI-native platform promises to solve this by automatically replicating data to wherever GPUs are located, supporting billions of small files while maintaining low-latency access for training, inference, and what the industry now calls "agentic workloads." The company already operates three data centers in Virginia, Chicago and San Jose, with plans to expand to London, Frankfurt and Singapore.
The pain points Tigris addresses are real and expensive. Batuhan Taskaya, head of engineering at customer Fal.ai, says egress fees - the notorious "cloud tax" charged when companies want to move data between providers - once accounted for the majority of his company's cloud spending. Think of it like your gym charging extra fees just because you want to cancel your membership.
"Tigris lets us scale our workloads in any cloud by providing access to the same data filesystem from all these places without charging egress," Taskaya explained to TechCrunch.
But egress fees are just one symptom of a deeper architectural problem. Traditional cloud storage was designed to keep data close to each provider's own compute resources, not spread across multiple clouds or regions. For AI workloads that need to stream massive datasets for training or run real-time inference across different locations, this centralized approach creates latency bottlenecks that can cripple model performance.
"Imagine talking to an AI agent that's doing local audio," Tariq said. "You want the lowest latency. You want your compute to be local, close by, and you want your storage to be local, too."
Tigris has found its sweet spot serving over 4,000 customers, mostly generative AI startups building image, video and voice models that require large, latency-sensitive datasets. These companies increasingly want to own and control their data rather than depend on Big Cloud providers - especially after incidents like Salesforce blocking AI rivals from using Slack data earlier this year.
"Companies are becoming more and more aware of how important the data is, how it's fueling the LLMs, how it's fueling the AI," Tariq noted. "They want to be more in control. They don't want someone else to be in control of it."
The regulatory angle adds another layer of urgency. In highly regulated fields like finance and healthcare, enterprises need to ensure data security and residency requirements - something much easier to achieve with localized, distributed storage than with centralized cloud providers.
Tigris has been growing 8x annually since its November 2021 founding, according to Tariq. The fresh $25 million will fund continued expansion of its data center network to meet this surging demand. The startup faces the classic infrastructure challenge of needing to build out capacity ahead of demand, making the funding runway crucial for maintaining its growth trajectory.
The competitive landscape is heating up as more companies recognize the distributed storage opportunity. But Tigris benefits from its founding team's deep storage expertise - having built the infrastructure that handled Uber's massive global data needs - and its early focus on AI-native features that traditional cloud providers are still adapting to support.
Tigris represents a fundamental bet that AI's distributed computing future demands distributed storage to match. With major cloud providers still charging premium egress fees and struggling with latency bottlenecks, the startup has found a compelling wedge in the market. The question isn't whether distributed storage will grow - it's whether Tigris can build out its network fast enough to stay ahead of both customer demand and inevitable competition from Big Cloud incumbents who won't cede this territory without a fight.
