.NET 10’s Vector Search: Your Fast Lane to Production AI Apps

TL;DR .NET 10 brings AI-ready vector search to Entity Framework Core 10 , eliminating the friction of bolting on separate vector databases. With full support for SQL Server 2025’s new vector data type and VECTOR_DISTANCE() function, you can now build semantic search and RAG workloads directly in your data layer —no middleware, no operational headaches, just LINQ.

Why This Matters Right Now

Microsoft announced the general availability of .NET 10, described as the most productive, modern, secure, and high-performance version to date, the result of a year-long effort involving thousands of contributors with improvements across runtime, libraries, languages, tools, frameworks, and workloads .

But here’s the kicker: AI-focused capabilities expand through the Microsoft Agent Framework, Microsoft.Extensions.AI, and first-class Model Context Protocol (MCP) support, aiming to standardize patterns for building agentic workflows, integrating external tools, enabling telemetry, and working across multiple AI providers .

The vector search piece is the unglamorous MVP that actually ships features. Before now, .NET teams building RAG (Retrieval-Augmented Generation) systems had to either:

Spin up a separate Pinecone or Weaviate cluster (cost + ops overhead)
Hack around with JSON columns (slow, clunky)
Use Azure AI Search as a sidecar (adds latency and complexity)

Not anymore.

What’s Actually New

Entity Framework Core 10 supports the new vector data type and VECTOR_DISTANCE() function, enabling AI workloads like semantic search and RAG with SQL Server 2025 and Azure SQL Database .

In practice, this means you can now store embeddings as first-class citizens in your relational database and query them with full LINQ support:

// Pseudo-code—actual syntax coming in EF Core 10 docs
var query = dbContext.Documents
    .OrderBy(d => EF.Functions.VectorDistance(d.Embedding, userQueryEmbedding))
    .Take(5)
    .ToList();

No ORM impedance mismatch. No manual SQL. Just type-safe, composable queries that your DBA can actually understand.

The Broader AI Story in .NET 10

.NET Aspire 13 ships with .NET 10 as a cloud-native application framework, strengthening orchestration for front ends, APIs, containers, and data stores with improvements in development workflows, deployment performance, multi-language integration, simplified templates, new resource types, enhanced security options, and dashboard improvements . Microsoft has gone all-in on making AI development in .NET easy and powerful, with tooling around LLMs, Microsoft Extensions for AI (now generally available), Semantic Kernel, and AI agents getting really mature .

For Azure shops specifically: The model router in Azure AI Foundry allows developers to automatically select the optimal Azure OpenAI models for different prompts—boosting quality while reducing costs, and Azure AI Foundry Agent Service is now generally available, helping companies automate complex business processes .

Integration Checklist

If you’re shipping a .NET app on Azure and want to use vector search:

Upgrade to .NET 10 (or wait for .NET 9.1 patch if you’re LTS-bound)
Update Entity Framework Core to 10.0+
Provision SQL Server 2025 or Azure SQL Database (vector support is built-in)
Use Microsoft.Extensions.AI for your embedding model calls
Query vectors via LINQ — no custom SQL needed

Cost-wise: you’re paying only for your database and API calls to your embedding model (OpenAI, Mistral, or local). No extra vector DB subscription. That’s the win.

The Catch

.NET 10 is a Long Term Support (LTS) release and will be supported for three years until November 10, 2028, with LTS releases receiving critical updates and security patches, making .NET 10 the recommended version for production applications requiring stability and extended support .

Vector search is production-ready, but early. If you’re integrating this into a mission-critical system, run benchmarks on your query patterns first. The VECTOR_DISTANCE() function uses cosine similarity by default; make sure that matches your embedding model’s training assumptions.

.NET 10's Vector Search: Your Fast Lane to Production AI Apps