The explosion of AI companies has driven the demand for computing power to new extremes, and companies like Corewave, Together AI, and Lambda Labs have capitalized on that demand, attracting huge amounts of attention and capital for their ability to offer distributed computing 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, rather than spread across multiple clouds or regions.
“Modern AI workloads and AI infrastructures are choosing distributed computing rather than large clouds,” Owais Tariq, co-founder and CEO of Tigris Data, told TechCrunch. “We want to provide that 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 local data storage centers that it claims can meet the distributed compute requirements of modern AI workloads. The startup’s AI-native storage platform “runs with your compute, (allows) data to automatically replicate to where the GPU is, supports billions of small files, and provides low-latency access for training, inference, and agentic workloads,” Tariq said.
To top it all off, Tigris recently raised a $25 million Series A round, led by Spark Capital and seeing participation from existing investors, including Andreessen Horowitz, TechCrunch has exclusively learned. The startup is going up against the incumbents, whom Tariq calls “Big Cloud.”
Tariq feels that these incumbents not only provide a more expensive data storage service, but are also less efficient. AWS, Google Cloud, and Microsoft Azure have historically charged exit fees (called “cloud taxes” in the industry) if a customer wants to migrate to another cloud provider, or download and transfer their data if they want to use a cheaper GPU or train models in different parts of the world simultaneously. Think of it this way, you’ll have to pay extra to your gym if you want to stop going there.
According to Batuhan Taskaya, head of engineering at Fal.ai, one of Tigris’ customers, these costs once accounted for the majority of Fal’s cloud spending.
techcrunch event
san francisco
,
October 27-29, 2025
In addition to exit fees, Tariq says there are still latency issues with larger cloud providers. “Exit fees were just a symptom of a deeper problem: centralized storage that cannot keep up with a decentralized, high-speed AI ecosystem,” he said.
Most of Tigris’s 4,000+ customers are like Fal.ai: generative AI startups building image, video, and voice models with large, latency-sensitive datasets.
“Imagine talking to an AI agent that is doing native audio,” Tariq said. “You want the lowest latency possible. You want your compute to be local, close, and you want your storage to be local, too.”
He said large clouds are not optimized for AI workloads. Streaming massive datasets for training or running real-time inference across multiple regions can create latency bottlenecks, slowing down model performance. But being able to access localized storage means data is retrieved faster, meaning developers can run AI workloads reliably and more cost-effectively using decentralized clouds.
“Tigris lets us scale our workloads to any cloud by providing access to the same data file system from all these locations,” said Fals Taskaya.
There are other reasons why companies want to keep data close to their distributed cloud options. For example, in highly regulated sectors such as finance and healthcare, a major barrier to the adoption of AI tools is that enterprises need to ensure data security.
Another motivation is that companies increasingly want to own their data, says Tariq, who points to Salesforce’s move earlier this year to Blocked your AI rivals By using Slack data. “Companies are becoming more aware of how important data is, how it’s fueling LLM, how it’s fueling AI,” Tariq said. “They want to be more in control. They don’t want someone else to have control over it.”
With the new funding, Tigris intends to continue building out its data storage centers to support growing demand – Tariq says the startup has grown 8x per year since its founding in November 2021. Tigris already has three data centers in Virginia, Chicago and San Jose, and is looking to continue expansion in the US as well as in Europe and Asia, specifically London, Frankfurt and Singapore.
