Image search systems require significant storage capacity to handle raw image data, feature vectors, and indexing structures. The primary factors are the volume of images, the way features are extracted and stored, and the trade-offs between search speed and storage efficiency. Systems must balance these elements to scale effectively while maintaining performance.
First, raw image storage depends on resolution, format, and quantity. For example, storing 100 million JPEG images averaging 1MB each requires roughly 100TB. Metadata (like timestamps, tags, or geolocation) adds overhead—10KB per image would consume 1TB. Compression (e.g., WebP) can reduce raw storage, but high-quality searches may require lossless formats. Systems often use distributed file systems (e.g., HDFS) or object storage (e.g., Amazon S3) to manage this data durably and cost-effectively.
Second, feature vectors and indexing dominate storage for large-scale systems. When images are processed by models like CNNs, each generates a feature vector (e.g., 512 dimensions as 32-bit floats), requiring ~2KB per image. For 100 million images, this totals 200GB. Indexes like FAISS or Annoy optimize vector search but add overhead—an HNSW index might use 10-20x the vector data size (e.g., 4TB for 200GB of vectors). Quantization (e.g., 8-bit instead of 32-bit floats) can cut vector storage by 75% but impacts accuracy. Some systems store multiple vector versions (e.g., for different search types), multiplying requirements.
Finally, redundancy and backups must be factored in. Production systems often replicate data across zones (3x storage) and retain snapshots. If the raw data and indexes total 500TB, replication and weekly backups (kept for a month) could require 2PB. Tiered storage helps: hot data (indexes) uses SSDs for speed, while cold data (raw images) uses cheaper HDDs or cloud archival storage. Developers must also plan for growth—adding 10,000 images daily expands storage by ~10GB/day, requiring scalable architectures like sharding or cloud auto-scaling.
