What are the storage requirements for embeddings?

The storage requirements for embeddings depend primarily on three factors: the number of embeddings, their dimensionality (i.e., the number of values per vector), and the numeric precision used to store each value. For example, a single embedding with 1,024 dimensions stored as 32-bit floating-point numbers requires 4 KB of space (1,024 × 4 bytes). If you have 1 million such embeddings, this grows to approximately 4 GB. Lowering the precision—for instance, using 16-bit floats or 8-bit integers—can reduce storage by 50% or 75%, respectively, but may impact model performance depending on the use case. Additionally, metadata (e.g., labels, timestamps) and indexing structures (for fast retrieval) add overhead, which must be factored into total storage needs.

A key trade-off involves balancing storage efficiency with the accuracy and utility of the embeddings. For instance, using quantization (reducing numerical precision) or dimensionality reduction techniques like PCA can shrink storage requirements significantly. However, these methods risk losing subtle semantic information encoded in the original vectors. For example, compressing a 768-dimensional BERT embedding to 256 dimensions might save 66% of the space but could degrade performance in tasks like semantic search. Similarly, choosing a database optimized for vectors—such as FAISS, Annoy, or specialized solutions like Pinecone—can reduce memory and disk usage through efficient indexing, though this depends on the algorithm (e.g., tree-based vs. graph-based indexes).

Scalability and infrastructure choices also play a role. Storing embeddings in-memory (e.g., using Redis or in-process arrays) provides fast access but becomes expensive at scale. Disk-based solutions (e.g., SQL/NoSQL databases with vector support) are cheaper for large datasets but introduce latency. For distributed systems, embeddings are often sharded across nodes, requiring redundancy and replication, which increase storage costs. A practical example is a recommendation system with 100 million user embeddings: using 128-dimensional vectors at 8-bit precision would require ~12.8 GB (100M × 128 × 1 byte), but adding replication across three nodes triples this to ~38.4 GB. Developers must align storage strategies with their application’s latency, accuracy, and budget constraints.