Azure AI Search Vector Optimization: Reducing Costs While Improving Recall

Vector search costs can spiral quickly at scale. After optimizing Azure AI Search deployments processing 50 million vectors, I’ve identified key patterns that reduce costs by 60% while actually improving search quality.

Quantization Strategies

Azure AI Search now supports scalar and binary quantization natively. Binary quantization reduces storage by 32x with minimal recall impact for many use cases:

from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
    SearchIndex, SearchField, VectorSearch,
    HnswAlgorithmConfiguration, VectorSearchProfile,
    ScalarQuantizationCompression, BinaryQuantizationCompression
)

index_client = SearchIndexClient(endpoint, credential)

# Configure vector search with quantization
vector_search = VectorSearch(
    algorithms=[
        HnswAlgorithmConfiguration(
            name="hnsw-config",
            parameters={
                "m": 4,  # Reduced from default 16
                "efConstruction": 400,
                "efSearch": 500,
                "metric": "cosine"
            }
        )
    ],
    compressions=[
        ScalarQuantizationCompression(
            compression_name="scalar-compression",
            parameters={"quantizedDataType": "int8"}
        ),
        BinaryQuantizationCompression(
            compression_name="binary-compression"
        )
    ],
    profiles=[
        VectorSearchProfile(
            name="optimized-profile",
            algorithm_configuration_name="hnsw-config",
            compression_configuration_name="scalar-compression"
        )
    ]
)

index = SearchIndex(
    name="documents-optimized",
    fields=[
        SearchField(name="id", type="Edm.String", key=True),
        SearchField(name="content", type="Edm.String"),
        SearchField(
            name="embedding",
            type="Collection(Edm.Single)",
            vector_search_dimensions=1536,
            vector_search_profile_name="optimized-profile"
        )
    ],
    vector_search=vector_search
)

Hybrid Search Tuning

Combine vector and keyword search with careful weight tuning. For technical documentation, I’ve found 70% vector / 30% keyword works well. For customer support, flip those ratios.

Dimensionality Reduction

Consider using smaller embedding models or applying PCA to reduce dimensions from 1536 to 512. Test recall on your specific dataset - many domains see less than 2% recall drop with 3x cost savings.