Distributed Data Storage Startup Challenges Major Cloud Providers

The rapid growth of AI companies has created unprecedented demand for computing power. Firms like CoreWeave, Together AI, and Lambda Labs have seized this opportunity, drawing significant investment and attention by offering distributed computing capacity.

However, most businesses still rely on the three major cloud providers—AWS, Google Cloud, and Microsoft Azure—for data storage. Their storage infrastructures are designed to keep data near their own computing resources rather than distributed across multiple clouds or regions.

"Modern AI workloads and infrastructure are shifting toward distributed computing instead of relying solely on big cloud providers," Ovais Tariq, co-founder and CEO of Tigris Data, explained to TechCrunch. "We aim to offer the same flexibility for storage, because compute resources are useless without it."

Tigris, founded by the team behind Uber’s storage platform, is developing a network of localized data storage centers. The startup claims its solution meets the distributed computing requirements of contemporary AI workloads. Its AI-native storage platform "moves with your compute, automatically replicates data to GPU locations, supports billions of small files, and provides low-latency access for training, inference, and agentic workloads," Tariq stated.

To support these efforts, Tigris recently secured a $25 million Series A funding round. Spark Capital led the round, with participation from existing investors including Andreessen Horowitz, TechCrunch has learned exclusively. The startup is positioning itself against established providers, whom Tariq refers to as "Big Cloud."

Tariq believes these incumbent providers offer not only more expensive storage services but also less efficient ones. AWS, Google Cloud, and Microsoft Azure have traditionally imposed egress fees—often called a "cloud tax"—when customers migrate to another provider or need to download and move data to access cheaper GPUs or train models in different global locations simultaneously. It's comparable to paying a penalty for canceling a gym membership.

According to Batuhan Taskaya, head of engineering at Fal.ai and a Tigris customer, these costs once made up the bulk of Fal's cloud expenses.

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Beyond egress fees, Tariq points out that larger cloud providers still face latency challenges. "Egress fees were just one symptom of a deeper issue: centralized storage systems that can't keep pace with a decentralized, high-speed AI ecosystem," he said.

Most of Tigris's 4,000+ customers are generative AI startups—such as those developing image, video, and voice models—that typically handle large, latency-sensitive datasets.

"Consider interacting with an AI agent that processes local audio," Tariq noted. "You need the lowest possible latency. That means having both your compute and storage resources located nearby."

He added that major cloud platforms aren't optimized for AI workloads. Streaming large datasets for training or performing real-time inference across regions can create latency bottlenecks, slowing model performance. Localized storage allows faster data retrieval, enabling developers to run AI workloads more reliably and cost-effectively using decentralized clouds.

"Tigris enables us to scale workloads across any cloud by providing access to the same data filesystem from all locations—without charging egress fees," said Fal's Taskaya.

There are additional reasons why companies prefer keeping data closer to their distributed cloud resources. In regulated sectors like finance and healthcare, a major barrier to AI adoption is the need to maintain data security and compliance.

Tariq also highlighted a growing desire among companies to own their data, citing Salesforce's earlier move to restrict AI competitors from using Slack data. "Businesses are increasingly aware of how valuable their data is—how it powers LLMs and fuels AI systems," Tariq explained. "They want greater control. They don't want another entity managing it."

With the new funding, Tigris plans to expand its data storage centers to meet rising demand. The startup has grown eightfold annually since its founding in November 2021, according to Tariq. With existing data centers in Virginia, Chicago, and San Jose, Tigris aims to expand further in the U.S., Europe, and Asia—specifically targeting London, Frankfurt, and Singapore.