Cost Optimization for Vector Databases
Reduce the cost of running vector databases in production through quantization, tiered storage, and dimensionality reduction.
Where the Costs Come From
Vector workloads are dominated by memory. Indexes like HNSW keep vectors in RAM, so cost scales with vector count times dimensions times precision.
The Memory Formula
Approximate memory = vectors x dimensions x bytes_per_value, plus index overhead. Cutting any factor cuts cost.
memory_bytes = num_vectors * dims * 4 # float32
# 1M vectors x 1536 dims x 4 bytes = ~6.1 GBAll lessons in this course
- Deployment and Scaling Strategies
- Monitoring and Observability
- Security Best Practices
- Cost Optimization for Vector Databases