AI is reshaping industries at an unprecedented pace, but behind every breakthrough lies a fundamental challenge: scaling AI applications efficiently. From real-time recommendations to generative models, AI-driven products demand fast, scalable, and cost-effective infrastructure. Yet, existing solutions often struggle to keep up, leaving companies to grapple with latency issues and spiraling compute costs.

 Vector databases sit at the heart of this shift. As AI adoption surges, the ability to store and retrieve high-dimensional data in milliseconds is no longer a luxury—it’s a necessity. While the market is still early, it’s growing fast, with projections surpassing $4.3 billion by 2028. The race is on to build the backbone of AI-powered applications, and we believe Vectroid is well-positioned to make a significant impact.

Vectroid is a next-generation vector database built for scale. It’s engineered for ultra-low latency, handling over a billion vectors with single-digit millisecond query speeds, all while dramatically lowering infrastructure costs. Unlike existing players that repurpose general-purpose databases for vector search, Vectroid was built from the ground up with AI workloads in mind. Whether powering real-time personalization, retrieval-augmented generation (RAG), or semantic search, its architecture enables companies to process massive datasets without performance trade-offs.

 Despite the clear demand, most vector databases today face a core limitation: they struggle to balance speed, scale, and cost-efficiency. This is where Vectroid stands out. By combining high-performance indexing techniques with advanced memory management, it offers an enterprise-grade solution that keeps up with AI’s relentless compute demands.

 At the center of this transformation is an exceptional founding team. CEO Talip previously founded Hazelcast, a widely adopted in-memory computing platform. With deep expertise in distributed systems and real-time data processing, he’s spent decades solving problems at the intersection of performance and scale. Alongside Kevin (ex-MongoDB, Grafana Labs) and Zoltán (ex-Hazelcast, low-latency systems expert), the team brings a rare blend of database innovation, infrastructure know-how, and go-to-market execution.

 At Metis Ventures, we’ve built MetisX, our proprietary in-house platform, to help us surface the most promising founders before they hit the mainstream. By combining data-driven signals with a structured investment framework, MetisX enables us to cut through the noise and zero in on startups that align with our thesis. It’s how we identified Vectroid early, recognizing the team’s deep technical expertise and their approach to solving a real and growing need in AI infrastructure. More than just a sourcing tool, MetisX gives us early access to standout founders like Talip, helping us back transformative companies before they scale.

 Leading this investment at Metis Ventures, I worked closely with Talip, Kevin, and Zoltán throughout the process. Their approach, balancing speed, scale, and efficiency, made a strong impression from the start, and we’re excited to help them accelerate their vision.