Tigris Data Challenges Big Cloud with Distributed AI-Native Storage Network
The rapid rise of artificial intelligence startups has pushed global demand for computing and data storage power to new levels. Companies like CoreWeave, Together AI, and Lambda Labs have already capitalized on this trend, gaining investor attention for their distributed compute capacity. However, when it comes to data storage, most organizations still rely on the major cloud providers — AWS, Google Cloud, and Microsoft Azure — whose centralized infrastructures were never designed for today’s distributed AI workloads.
“Modern AI workloads are moving toward distributed computing instead of relying solely on traditional cloud giants,” explained Ovais Tariq, co-founder and CEO of Tigris Data, in an interview with TechCrunch. “Our goal is to provide the same level of flexibility and performance for storage — because without storage, compute means nothing.”
Founded by the engineers who built Uber’s internal storage platform, Tigris is developing a network of localized data storage centers optimized for modern AI and machine learning systems. Its AI-native storage platform dynamically replicates data near active compute resources, enabling ultra-low-latency access for AI training, inference, and agentic workloads, all while supporting billions of small files efficiently.
To accelerate its mission, Tigris Data recently raised a $25 million Series A round led by Spark Capital, with participation from existing backers like Andreessen Horowitz, TechCrunch has learned. Tariq says the company aims to directly challenge what he calls “Big Cloud” — the large centralized providers that dominate enterprise storage.
According to Tariq, traditional cloud services are not only more expensive, but also less efficient for distributed workloads. For years, the big players have charged egress fees — often referred to as the “cloud tax” — when users transfer data to other platforms or regions. This makes it costly for companies to move workloads between clouds or train models in multiple locations simultaneously.
“Think of it like being charged extra just to cancel your gym membership,” Tariq joked.
For Fal.ai, a generative AI company and one of Tigris’ customers, these egress costs once represented the majority of its cloud expenses. Beyond the cost issue, Tariq highlighted another major pain point — latency. “Egress fees were only a symptom of a much bigger issue: centralized storage simply can’t keep up with the decentralized, high-speed nature of modern AI ecosystems,” he said.
Most of Tigris’ 4,000+ customers are AI startups developing generative models for images, audio, and video — all of which demand fast, localized data access. “Imagine an AI voice agent processing local audio — you want your compute and your storage as close together as possible,” Tariq noted. “That’s how you eliminate latency.”
He added that big cloud architectures are not optimized for this type of workload. Streaming large datasets across regions creates bottlenecks that slow performance, increase costs, and limit real-time capabilities. With Tigris’ distributed storage, data can be accessed locally across multiple clouds without the penalty of egress fees, allowing developers to scale AI workloads seamlessly.
Beyond technical advantages, data sovereignty and security compliance are driving more enterprises to seek control over where their information resides. Industries like finance and healthcare require strict data handling policies, and many organizations are now wary of sharing data with cloud providers who may also compete in AI. Tariq points to Salesforce’s decision to block AI rivals from using Slack data as a sign of changing attitudes. “Companies are realizing their data is the fuel behind AI,” he said. “They want to own it and control how it’s used.”
With its new funding, Tigris Data plans to expand its growing network of data centers — currently operating in Virginia, Chicago, and San Jose — to key global regions including London, Frankfurt, and Singapore. Tariq revealed that the company has achieved 8x annual growth since its founding in November 2021, positioning Tigris as one of the most promising cloud alternatives in the AI infrastructure space.
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