Exact and approximate vector search are two approaches for finding similar vectors in a dataset, each with distinct trade-offs in accuracy, speed, and scalability. Exact vector search guarantees precise results by comparing a query vector to every vector in the dataset, ensuring no matches are missed. Approximate vector search, on the other hand, prioritizes speed and efficiency by using techniques that reduce the number of comparisons, accepting minor inaccuracies in exchange for faster results. The choice between them depends on the use case—exact methods are ideal when accuracy is critical, while approximate methods suit applications where speed and scalability matter more than perfect precision.
Exact vector search methods, such as brute-force k-nearest neighbors (k-NN), calculate the distance (e.g., Euclidean or cosine) between the query vector and every vector in the dataset. This ensures that the returned results are the closest matches possible. For example, in a small dataset of 1,000 product embeddings, a brute-force search would compute 1,000 distances to rank products by similarity. However, this approach becomes computationally expensive as datasets grow. A dataset with 10 million vectors would require 10 million distance calculations per query, leading to high latency and resource usage. Exact methods are typically used in scenarios where accuracy is non-negotiable, such as validating the results of an approximate search system or analyzing small, static datasets where latency isn’t a concern.
Approximate vector search avoids exhaustive comparisons by using techniques like hashing, graph traversal, or quantization to narrow down the candidate vectors. For instance, algorithms like Hierarchical Navigable Small Worlds (HNSW) create a network of interconnected vectors, allowing the search to “hop” through the graph to find neighbors without checking every vector. Similarly, libraries like FAISS use clustering to group similar vectors and search only within relevant clusters. While these methods return results in milliseconds even for billion-scale datasets, they may miss some true nearest neighbors. For example, a recommendation system using approximate search might return 95% of the ideal matches but omit 5% to achieve real-time performance. Approximate methods are widely used in production systems like search engines, fraud detection, or real-time recommendations, where sub-second response times are critical and minor inaccuracies are acceptable.