Tigris Data Raises $25M to Challenge Big Cloud with AI-Optimized Storage

Tigris Data: Challenging Big Cloud with AI-Optimized Distributed Storage

In a bold move to revolutionize data storage for AI workloads, Tigris Data has secured $25 million in Series A funding, led by Spark Capital with participation from Andreessen Horowitz1

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. The Sunnyvale-based startup, founded in November 2021, aims to provide a more efficient and cost-effective alternative to traditional cloud storage providers.

The AI Storage Dilemma

As the demand for AI computing power skyrockets, companies like CoreWeave, Together AI, and Lambda Labs have capitalized on offering distributed compute capacity. However, data storage has largely remained centralized with the "Big Three" cloud providers: AWS, Google Cloud, and Microsoft Azure.

Ovais Tariq, co-founder and CEO of Tigris Data, explains the problem: "Modern AI workloads and AI infrastructure are choosing distributed computing instead of big cloud. We want to provide the same option for storage, because without storage, compute is nothing."1

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Tigris Data's Innovative Solution

Tigris Data's platform offers several key advantages:

1. Distributed Storage: The company is building a network of localized data storage centers to meet the distributed compute needs of modern AI workloads1

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2. AI-Native Design: The 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," according to Tariq1

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3. Optimized Performance: Tigris uses a log-structured merge-tree (LSM) data structure to optimize data organization and speed up retrieval times2

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4. Flexible Storage Tiers: The platform offers four storage infrastructure tiers, catering to different needs and budgets2

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Addressing Industry Pain Points

Tigris Data tackles several challenges faced by AI companies:

1. Egress Fees: Traditional cloud providers often charge hefty fees for data migration or downloads. Batuhan Taskaya, head of engineering at Fal.ai, a Tigris customer, noted that these costs once accounted for the majority of their cloud spending1

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2. Latency: By offering localized storage, Tigris reduces latency for AI workloads, crucial for applications like real-time audio processing1

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3. Data Control: As companies become more aware of the value of their data in fueling AI models, Tigris offers greater control over data storage and access1

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Future Expansion and Market Impact

With its recent funding, Tigris Data plans to expand its network of data centers beyond its current locations in Virginia, Chicago, and San Jose. The company aims to establish a presence in Europe and Asia, specifically targeting London, Frankfurt, and Singapore1

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As Tigris Data continues to grow—having expanded 8x every year since its founding—it poses a significant challenge to traditional cloud storage providers. By offering a more flexible, efficient, and cost-effective solution tailored to AI workloads, Tigris Data is positioning itself as a key player in the evolving landscape of AI infrastructure.