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Notes & wrap-up

Memory Estimates (per collection, no quantization)​

Medium (1M objects):    ~6GB
Large (25M objects):    ~150GB
XLarge (500M objects):  ~3TB

💡 Quantize vectors for significant reductions to memory requirements. How much depends on the algorithm: 8-bit RQ and SQ compress 4x (75% less memory), while 1-bit RQ and BQ approach 32x (~97%) as dimensionality grows. RQ-8 is a good starting point. It holds 98-99% recall in internal testing.

Common Anti-patterns​

Architecture Anti-patterns​

Many collections (>50) sharing settings

  • May be inefficient if the number of collections keep growing
  • Consider using a multi-tenant collection instead

Cross-references with high cardinality

  • Performance penalties incurred
  • Consider denormalizing data

HNSW without vector compression without specific retrieval quality needs

  • Higher memory footprint & resource requirements
  • Evaluate compression algorithms

Data Design Anti-patterns​

Vectorization of all text, including non-semantic data (IDs, URLs, etc.)

  • Adds to vectorization cost for little benefit
  • Consider what semantic value those properties are adding

Key Takeaways​

  1. Plan for scale early - Some decisions are expensive to change later
  2. Denormalize when possible - Cross-references are slow at scale
  3. Use compression wisely - Balance memory savings with search quality
  4. Test your patterns - Use staging environments that mirror production
  5. Monitor and iterate - Vector databases benefit from continuous optimization

Architectural decisions in vector databases require balancing multiple concerns: performance, cost, scalability, and operational complexity. Use this guide's decision patterns as starting points, but always validate choices against your specific requirements and constraints.

Next Steps​

  • Set up a development environment that mirrors your planned production architecture
  • Implement monitoring to track key metrics (query performance, memory usage, costs)
  • Plan regular architectural reviews as your usage patterns evolve
  • Stay updated with Weaviate's evolving features and best practices
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