Cost Fix Cosmos DB DiskANN Matryoshka Embeddings Quantization The Runaway Vector Index Bill: Tuning Dimensions & Quantization in Azure Cosmos DB for AI Your bill is not high because AI is expensive. It is high because you are storing every document as a 3,072-number vector when — by OpenAI's own benchmarks — you could store it as a 256-number vector and get better retrieval than your predecessor architecture had at 1,536. The default was chosen for you, and it is the wrong one for almost every RAG workload. The failure signature this guide resolves # Azure Cosmos DB cost report — the line item nobody expected: Resource: cosmos-rag-prod (Provisioned throughput, DiskANN vector index) Period: last 30 days Item RU-hours Cost (USD) Storage — indexed data 18,240,000 $3,650 Storage — VECTOR INDEX (3072-dim, 8M docs) $9,720 ← 62% of bill Point reads ...
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