The explosion of AI companies is pushing the demand for computing power to unprecedented limits, and companies like CoreWeave, Together AI, and Lambda Labs are capitalizing on that demand and attracting tremendous attention and funding for their ability to provide distributed computing power.
However, most businesses still store their data in the three largest cloud providers: AWS, Google Cloud, and Microsoft Azure. These storage systems are built to keep your data close to your own computing resources, rather than having it spread across multiple clouds or regions.
“Modern AI workloads and AI infrastructure are choosing distributed computing over big cloud,” Ovais Tariq, co-founder and CEO of Tigris Data, told TechCrunch. “We want to offer the same options for storage, because without storage, computing is meaningless.”
Founded by the team that developed Uber’s storage platform, Tigris is building a network of localized data storage centers that it claims can meet the distributed computing needs of modern AI workloads. The startup’s AI-native storage platform “moves with compute, ensures data is automatically replicated to the GPU’s location, supports billions of small files, and provides low-latency access for training, inference, and agent workloads,” Tariq said.
To make all this happen, Tigris recently raised a $25 million Series A round led by Spark Capital, with participation from existing investors including Andreessen Horowitz, TechCrunch has learned exclusively. The startup is competing against what Tariq calls the “big cloud” incumbents.
Tariq feels that these incumbent companies not only provide more expensive data storage services, but are also less efficient. AWS, Google Cloud, and Microsoft Azure have traditionally charged egress fees (referred to in the industry as a “cloud tax”) when customers want to move to another cloud provider, download and move data if they want to use cheaper GPUs or train models in different parts of the world at the same time. Think of it like if you want to stop going to the gym, you have to pay extra to the gym.
Batuhan Taskaya, head of engineering at Fal.ai, one of Tigris’ customers, said these costs used to make up the bulk of Fal’s cloud spending.
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Beyond egress fees, Tariq says large cloud providers still have latency issues. “Egress fees are just one symptom of a deeper problem: centralized storage that cannot keep up with a decentralized, high-speed AI ecosystem,” he said.
Most of Tigris’ more than 4,000 customers are like Fal.ai. Fal.ai is a generative AI startup that builds image, video, and audio models and tends to have large, latency-sensitive datasets.
“Imagine talking to an AI agent responsible for local audio,” Tariq said. “We want to minimize latency. We want the compute to be close to the local, and we want the storage to be local as well.”
Big cloud is not optimized for AI workloads, he added. Streaming large datasets for training or real-time inference across multiple regions can create latency bottlenecks and degrade model performance. But having access to localized storage means faster data retrieval and means developers can use the distributed cloud to run AI workloads reliably and more cost-effectively.
“Tigris allows you to scale your workloads across any cloud by providing access to the same data file system from all these locations without charging egress fees,” said Fal’s Taskaya.
There are other reasons why businesses want to move their data closer to distributed cloud options. For example, in highly regulated sectors such as finance and healthcare, one of the major obstacles to implementing AI tools is the need for companies to ensure data security.
Another motivation is that companies increasingly want to own their data, Tariq said, noting that earlier this year Salesforce blocked AI rival Slack from using its data. “Companies are becoming increasingly aware of how important data is, how data is driving LLM, and how it is driving AI,” Tariq said. “They want more control. They don’t want someone else to control them.”
With the new funding, Tigris intends to continue building data storage centers to meet growing demand. Tariq said 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 hopes to continue expanding in the United States as well as Europe and Asia, particularly London, Frankfurt, and Singapore.
