Vector database that can index 1B vectors in 48M

Summary

We are excited to announce Vectroid, a serverless vector search solution that delivers exceptional accuracy and low latency in a cost effective package. Vectroid is not just another vector search solution—it’s a search engine that performs and scales in all scenarios. Why we built VectroidTalk to any team working with large, low latency vector workloads and you’ll hear a familiar story: something always has to give. Vector databases often make significant tradeoffs between speed, accuracy, and cost. That’s the nature of the mathematical underpinnings of vector search works—taking algorithmic shortcuts to get near-perfect results in a short amount of time.There are some common permutations of these tradeoffs:Very high accuracy, but very expensive and slow Fast speed and tolerable accuracy, but very expensive Cheap and fast, but inaccurate to a disqualifying degree Based on the existing vector database landscape, it would seem that building a cost effective vector database requires sacrificing either speed or accuracy at scale. We believe that’s a false pretense: building a cost-efficient database is possible with high accuracy and low latency. We just need to rethink our underlying mechanism. Our “aha” momentQuery speed and recall are largely a function of the chosen ANN algorithm. Algorithms which are both fast and accurate like HNSW (Hierarchical Navigable Small Worlds) are memory intensive and expensive to index. The traditional assumption is that these types of algorithms are untenable for a cost-conscious system.We had two major realizations which challenged this assumption.Demand for in-memory HNSW is not static. Real world usage patterns are bursty and uneven. A cost efficient database can optimize for this reality by making resource allocation dynamic and by individually scaling system components as needed. HNSW’s memory footprint is tunable. It can be easily be flattened (ex. by compressing vectors using quantization) and expanded (ex. by increasing layer co...