Exact Nearest Neighbor (ENN) algorithms guarantee finding the absolute closest vectors. However, they require comparing the query vector against every vector in the dataset, making them prohibitively slow for large datasets. Approximate Nearest Neighbor (ANN) algorithms, such as Hierarchical Navigable Small Worlds (HNSW) or Inverted File Index (IVF), trade perfect accuracy for significant speed improvements. They achieve this by partitioning the vector space or building graph-like structures, allowing for faster, albeit approximate, searches. The trade-off here is between recall (finding all relevant items) and latency (how quickly results are returned).

Scaling a vector database can be achieved through sharding (partitioning data across multiple nodes) or replication (creating copies of data). Sharding allows for horizontal scaling, distributing the load and enabling larger datasets. However, it complicates query routing and can lead to uneven data distribution. Replication improves read availability and fault tolerance but increases storage costs and requires careful management of data consistency across replicas. The trade-off is between handling massive datasets and managing the operational overhead and potential consistency challenges of distributed systems.

Vector databases often support filtering results based on associated metadata. This is crucial for RAG systems where you might want to retrieve documents from a specific author or within a certain date range. The trade-off lies in how this filtering is implemented. Some databases perform filtering before the vector search, which can be efficient if the metadata significantly reduces the search space. Others perform filtering after the vector search, which might require more computational resources. Efficiently combining vector similarity search with metadata filtering is a key design challenge.