Added link to cost/compression blog

Author: HeidiSteen

articles/search/vector-search-how-to-quantization.md

@@ -28,6 +28,9 @@ To use built-in quantization, follow these steps:
 > - Load the index with float32 or float16 data that's quantized during indexing with the configuration you defined
 > - Optionally, [query quantized data](#query-a-quantized-vector-field-using-oversampling) using the oversampling parameter. If the vector field doesn't specify oversampling in its definition, you can add it at query time.
 
+> [!TIP]
+> [Azure AI Search: Cut Vector Costs Up To 92.5% with New Compression Techniques](https://aka.ms/AISearch-cut-cost) compares compression strategies and explains savings in storage and costs. It also includes metrics for measuring relevance based on Normalized discounted cumulative gain (NDCG), demonstrating that you can compress your data without sacrificing search quality.
+
 ## Prerequisites
 
 - [Vector fields in a search index](vector-search-how-to-create-index.md), with a `vectorSearch` configuration specifying either the Hierarchical Navigable Small Worlds (HNSW) or exhaustive K-nearest neighbor (eKNN) algorithm, and a new vector profile.