Deploying embeddings and vector search in production requires attention to cost, latency, and reliability.

Cost Optimization

Embedding costs add up at scale:

• Cache embeddings: Never re-embed the same text. Use a hash-based cache.
• Dimensionality reduction: OpenAI's text-embedding-3 models support native dimension reduction (3072 → 1024 → 512) with minimal quality loss.
• Open-source models: Run BGE or Nomic locally to eliminate per-token API costs. A single GPU can embed millions of documents per day.
• Batch processing: Embed during off-peak hours when cloud GPU costs are lower.

Latency Optimization

For real-time applications, every millisecond counts:

• Approximate search: Accept 95-99% recall for 10-100× speed improvement over exact search
• Quantized vectors: Store as int8 instead of float32 for 4× less memory and faster distance calculations
• Index tuning: Adjust HNSW parameters (ef_construction, M) for your recall/speed tradeoff
• Caching: Cache frequent queries and their results

Keeping Embeddings Fresh

Documents change. Your embeddings need to stay current:

• Incremental updates: Re-embed only changed documents, not the entire corpus
• Change detection: Hash document content and compare to stored hashes
• Versioning: Track which embedding model version was used — re-embed everything when upgrading models
• TTL (Time-To-Live): For time-sensitive content, automatically expire and refresh embeddings

Observability

Monitor your embedding pipeline:

• Embedding generation latency and error rates
• Index size growth and memory usage
• Query latency percentiles (p50, p95, p99)
• Retrieval quality metrics over time
• Cost per query breakdown (embedding + search + generation)