The explosion of AI companies has pushed demand for computing power to new extremes. Companies like CoreWeave, Together AI, and Lambda Labs have capitalized on that demand, attracting immense attention and capital for their ability to offer distributed compute capacity.
However, most companies still store data with the big three cloud providers: AWS, Google Cloud, and Microsoft Azure. Their storage systems were built to keep data close to their own compute resources, not spread across multiple clouds or regions.
Ovais Tariq, co-founder and CEO of Tigris Data, stated that modern AI workloads and AI infrastructure are choosing distributed computing instead of big cloud. He explained that they 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 to 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.
To accomplish this, Tigris recently raised a 25 million dollar Series A round led by Spark Capital with participation from existing investors, which include Andreessen Horowitz. The startup is going against the incumbents, whom Tariq calls Big Cloud.
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, often called a cloud tax, if a customer wants to migrate to another provider or download and move their data to use a cheaper GPU or train models in different parts of the world simultaneously. According to Batuhan Taskaya, head of engineering at Fal.ai, one of Tigris’ customers, those costs once accounted for the majority of Fal’s cloud spending.
Beyond egress fees, Tariq says there is still the problem of latency with larger cloud providers. He stated that egress fees were just one symptom of a deeper problem: centralized storage that cannot keep up with a decentralized, high-speed AI ecosystem.
Most of Tigris’ over four thousand customers are generative AI startups building image, video and voice models, which tend to have large, latency-sensitive datasets. Tariq gave the example of talking to an AI agent that is doing local audio, where you want the lowest latency and your compute and storage to be local.
He added that big clouds are not optimized for AI workloads. Streaming massive datasets for training or running real-time inference across multiple regions can create latency bottlenecks that slow model performance. Being able to access localized storage means data is retrieved faster, allowing developers to run AI workloads more reliably and cost effectively using decentralized clouds. Taskaya from Fal confirmed that Tigris lets them scale workloads in any cloud by providing access to the same data filesystem from all these places without charging egress.
There are other reasons why companies want data closer to their distributed cloud options. In highly regulated fields like finance and healthcare, a major roadblock to adopting AI is the need to ensure data security.
Another motivation, according to Tariq, is that companies increasingly want to own their data. He pointed to how Salesforce earlier this year blocked its AI rivals from using Slack data. Companies are becoming more aware of how important data is, how it fuels large language models and AI, and they want to be more in control of it rather than having someone else in control.
With the fresh funding, Tigris intends to continue building its data storage centers to support increasing demand. Tariq says the startup has grown eight times every year since its founding in November 2021. Tigris already has three data centers in Virginia, Chicago, and San Jose, and plans to continue expanding in the U.S. as well as in Europe and Asia, specifically in London, Frankfurt, and Singapore.