How do embeddings scale in production systems?

Embeddings scale in production systems through a combination of efficient storage, optimized retrieval, and distributed computing. The core challenge is handling high-dimensional vectors (embeddings) at scale while maintaining low latency and high accuracy. For example, a recommendation system might need to compare millions of user and item embeddings in real time. To achieve this, systems often use approximate nearest neighbor (ANN) algorithms like FAISS, Annoy, or HNSW, which trade a small amount of precision for significant gains in speed and memory efficiency. These tools allow queries like “find similar items” to execute in milliseconds, even with billions of embeddings.

Infrastructure design plays a critical role. Embeddings are typically stored in specialized vector databases (e.g., Pinecone, Milvus) or extensions to traditional databases (e.g., PostgreSQL with pgvector). For large-scale systems, embeddings are distributed across multiple nodes using partitioning strategies like sharding. For instance, a search engine might split its document embeddings by language or category to reduce the search space. Caching frequently accessed embeddings in memory (using tools like Redis) and precomputing embeddings during data ingestion pipelines also help reduce latency. Batch processing frameworks like Apache Spark are often used to generate embeddings offline, while real-time services handle user queries with minimal overhead.

Performance tuning and monitoring are equally important. Developers track metrics like query latency, recall rates (how many true matches ANN algorithms find), and memory usage. They might adjust ANN parameters, such as the number of search trees in HNSW, based on these metrics. Compression techniques like quantization (storing embeddings as 8-bit integers instead of 32-bit floats) can reduce memory usage by 75% with minimal accuracy loss. For example, a chatbot using sentence embeddings might quantize its model to serve more users concurrently without overloading memory. Regular reindexing and retraining pipelines ensure embeddings stay relevant as data evolves, balancing computational cost with freshness.