Azure Cosmos DB Spherical Quantization: Faster Vector Search in Public Preview

What's Changing

Azure Cosmos DB is bringing spherical quantization to public preview, a vector compression technique designed to accelerate vector indexing while preserving search quality and recall. This capability is now available for SQL API and MongoDB vCore API containers with vector search enabled.

Spherical quantization converts high-dimensional vectors into quantized representations using spherical geometry, reducing memory footprint and computational overhead without catastrophic accuracy loss. Microsoft reports typical storage reduction of 20–40% with 95–99% precision retention, making this a meaningful optimization for large-scale vector workloads.

Who's Affected & When

• Cosmos DB accounts using SQL API or MongoDB vCore API with vector search enabled
• All regions where Cosmos DB vector search is available
• Opt-in during preview: quantization is not enabled by default. You must explicitly set Quantized = true in your container's vector embedding policy
• Non-production only: preview environments only; production migration blocked until GA announcement
• SDK requirement: Azure SDK for .NET v3.32.0 or later. Older SDKs will not expose the Quantized parameter and will throw property-not-found errors

There is no announced general availability (GA) date. Microsoft has not published a roadmap for transitioning spherical quantization from preview to production, so plan your testing with a 3–6 month buffer before assuming production support.

What This Means for Your Environment

Search Performance Improvement

For vector datasets larger than 100,000 embeddings, enabling quantization typically reduces query latency by 10–30% because the quantized index consumes less memory and requires fewer floating-point comparisons. If your application uses LangChain, Semantic Kernel, or custom agents that call Cosmos DB vector search frequently, you'll observe wall-clock improvement in retrieval speed.

Storage and Cost Impact

Quantized vectors consume 20–40% less storage than their 32-bit float counterparts. However, this is preview-only and SLA-free, so your RU consumption during quantization may fluctuate. After GA, storage savings should translate to lower indexed vector costs, but do not assume this during testing.

What Breaks (and What Doesn't)

Query syntax remains identical—your application code does not change. Cosmos DB automatically handles quantization/dequantization at the index level. The potential risk is recall degradation: applications expecting 99.9% accuracy on specific edge-case queries might see 1–5% of results reranked differently. For semantic search, RAG, and similarity matching, this is typically imperceptible. For exact-match or regulatory-grade precision (e.g., medical or legal document retrieval), test extensively before enabling.

What You Must Do Right Now

1. Update your SDK

   If you're using Azure SDK for .NET, update to v3.32.0 or later. Run dotnet add package Azure.Cosmos --version 3.32.0 (or later) in your project directory.

   // Update your .csproj or use NuGet Package Manager
   dotnet list package --outdated
   dotnet add package Azure.Cosmos --version 3.32.0

2. Create an isolated test container

   Do not enable quantization on production containers yet. Create a new, non-production container with the same schema and sample data. Enable quantization only on this test container.

3. Benchmark baseline performance

   Before enabling quantization, run your representative query workload (10,000–100,000 vectors) and measure: latency (p50, p95, p99), recall@K (check how many of the top-K results match a non-quantized run), and storage size. Document these as your baseline.

4. Enable quantization and test

   Update your container's vector embedding policy to set Quantized = true on your embedding definition. Reindex the test container and repeat your benchmark suite.

5. Measure the delta

   Compare quantized vs. non-quantized results. If recall stays above 95% and latency improvement is acceptable, quantization is safe for your workload. If recall drops below 90% or queries become outliers, quantization may not suit your use case.

Action Items

• SDK upgrade: Ensure Azure SDK for .NET is v3.32.0+; other SDKs (Python, JavaScript, Java) should be checked for matching or preview-equivalent versions.
• Create test environment: Provision a non-production Cosmos DB container with vector search enabled and identical schema to your production vectors.
• Baseline testing: Run a representative query workload with non-quantized vectors and document latency, recall, and storage metrics.
• Enable quantization: Update the test container's vector embedding policy to set Quantized = true on the embedding definition. Monitor index building time and RU consumption.
• Comparative benchmarking: Execute the same query workload on quantized vectors and compare results against baseline. Measure recall@10, recall@100, and end-to-end latency.
• Document findings: Record whether quantization meets your accuracy and performance requirements. Share results with stakeholders before GA.
• Plan GA migration: Set a calendar reminder to revisit this feature once it reaches general availability. At GA, plan a controlled re-indexing of production vectors if the preview results were positive.
• Monitor announcements: Watch the Azure Updates channel for the GA announcement and any breaking changes between preview and general availability.

Code Example: Enabling Spherical Quantization

Here's how to create a container with quantized vector search using Azure SDK for .NET:

using Azure.Cosmos;

// Initialize Cosmos DB client
var cosmosClient = new CosmosClient("your-connection-string");
var database = cosmosClient.GetDatabase("your-database");

// Create container with quantized vector embedding policy
var containerProperties = new ContainerProperties
{
    Id = "vectorContainer",
    PartitionKeyPath = "/id"
};

// Define vector embedding policy with quantization enabled
containerProperties.VectorEmbeddingPolicy = new VectorEmbeddingPolicy
{
    VectorEmbeddings = new Collection<Embedding>
    {
        new Embedding
        {
            Name = "vectorContent",
            DataType = VectorDataType.Float32,
            Dimensions = 1536,
            DistanceFunction = DistanceFunction.Cosine,
            Quantized = true  // Enable spherical quantization
        }
    }
};

// Create the container
await database.CreateContainerAsync(containerProperties);

Query Execution

Your query syntax does not change. Cosmos DB automatically dequantizes vectors at query time:

// Your query remains the same—quantization is transparent
var queryDefinition = new QueryDefinition(
    "SELECT c.id, c.text, VectorDistance(c.vectorContent, @embedding) AS similarity " +
    "FROM c ORDER BY VectorDistance(c.vectorContent, @embedding) OFFSET 0 LIMIT 10"
)
    .WithParameter("@embedding", queryVector);

var results = await container.GetItemQueryIterator<dynamic>(queryDefinition).ReadNextAsync();

Common Gotchas During Testing

What to Measure in Your Test

Timing & Rollout Expectations

The public preview is available now (as of May 2026). Microsoft has not announced a GA timeline, so assume 3–6 months minimum before this feature moves to production support. Use this window to:

• Test thoroughly in isolated, non-production environments
• Document your benchmarks and communicate findings to your team
• Prepare a migration plan for GA, including re-indexing strategy and rollback procedures
• Monitor the Azure Updates page and Cosmos DB release notes for any breaking changes between preview and GA