Choosing a Vector Store for .NET AI Apps: sqlite-vec vs SQL Server 2025 vs Qdrant - Enterprise Decision Guide

Every .NET team building a Retrieval-Augmented Generation (RAG) feature or semantic search endpoint hits the same question early: which vector store should we use? The answer shapes infrastructure cost, dev-loop speed, and how far the system scales - and it is not the same answer for every team.

The production code that ties these choices together - setting up IEmbeddingGenerator, wiring Microsoft.Extensions.VectorData connectors, and structuring the ingestion pipeline - is available on Patreon alongside a working ASP.NET Core support-desk API that exercises all three stores.

The deeper context for this decision lives in Chapter 8 of the AI-Powered .NET APIs course, which covers vector store selection, collection design, and the full ingestion + retrieval flow inside a single running C# project you can run locally today.

In this guide I will compare the three stores .NET teams reach for most often in 2026 - sqlite-vec, SQL Server 2025 (with native vector type), and Qdrant - with honest trade-offs drawn from production experience.

What Is Microsoft.Extensions.VectorData and Why Does It Matter?

Microsoft.Extensions.VectorData (GA in .NET 10) is the abstraction layer that decouples your application code from any specific vector store. You program against IVectorStoreRecordCollection<TKey, TRecord> and swap the underlying provider with a single DI registration change - the same pattern IChatClient gives you for LLM providers.

// Register sqlite-vec for local dev
builder.Services.AddSqliteVectorStore("./vectors.db");

// Swap to Qdrant in production - application code unchanged
builder.Services.AddQdrantVectorStore("localhost", 6334);

This means the vector store choice is a deployment concern, not an application architecture concern. You can prototype with sqlite-vec, run integration tests against SQL Server 2025, and ship to Qdrant - without touching your ingestion or retrieval code.

The Three Contenders

sqlite-vec

sqlite-vec is a C extension that adds ANN vector search to SQLite. The .NET connector ships as Microsoft.SemanticKernel.Connectors.Sqlite and integrates with Microsoft.Extensions.VectorData.

What it is good for:

• Local development and prototyping - zero infrastructure, the database is a file
• Unit and integration tests - spin up a fresh in-memory or file-backed collection per test, no Docker required
• Single-tenant apps with modest data volumes (under ~500k vectors per collection)
• Embedded scenarios where you cannot run a separate process (edge deployments, Raspberry Pi, offline apps)

What it is not good for:

• High write concurrency - SQLite's single-writer model becomes a bottleneck at scale
• Multi-tenant SaaS products with a large shared knowledge base
• Horizontal scaling - you cannot shard or replicate a sqlite-vec database easily

In production I have seen teams use sqlite-vec for their staging environment even when Qdrant runs in prod. The parity is close enough that you catch most bugs locally, and the dev loop is dramatically faster when there is no vector service to stand up.

SQL Server 2025 - Native vector Type

SQL Server 2025 ships a first-class vector(N) column type with built-in ANN index support (VECTOR_SEARCH T-SQL function and CREATE VECTOR INDEX). The .NET connector is Microsoft.SemanticKernel.Connectors.SqlServer.

What it is good for:

• Teams already running SQL Server 2025 for their relational workload - one fewer service to operate
• Transactional integrity: you can update a row and its vector embedding in a single transaction, which matters for RAG indexes that must stay in sync with the source data
• Hybrid queries: VECTOR_SEARCH composes naturally with JOIN, WHERE, and ORDER BY in one statement - useful for filtered semantic search (e.g., "find semantically similar tickets for this customer")
• Enterprise SQL Server licenses already in budget

-- Example: hybrid search combining filters with semantic similarity
SELECT TOP 5 t.id, t.title, VECTOR_DISTANCE('cosine', t.embedding, @queryVec) AS dist
FROM support_tickets t
WHERE t.customer_id = @customerId
ORDER BY dist ASC;

What it is not good for:

• Greenfield teams without an existing SQL Server footprint - Qdrant will cost less and scale more predictably
• Very high vector dimensionality (>2048) - SQL Server's ANN implementation is strong but Qdrant's HNSW index has better query throughput at that scale
• Cost-sensitive cloud setups - a SQL Server 2025 instance costs more than a Qdrant container on a small VM

The trade-off that bit us was update latency. When you use savechanges to update both the relational row and its embedding vector, SQL Server 2025 rebuilds the vector index segment for that partition synchronously. At high ingest rates (thousands of document chunks per minute), this causes write amplification you do not see with Qdrant.

Qdrant

Qdrant is a purpose-built, open-source vector database written in Rust. It uses HNSW (Hierarchical Navigable Small World) indexing and supports filterable payloads, meaning you can combine ANN search with metadata filters in a single query without a separate SQL join.

What it is good for:

• Production workloads at any scale - Qdrant handles hundreds of millions of vectors with consistent sub-50ms p99 latency
• Multi-tenant architectures - separate collections per tenant, or payload filtering within a shared collection
• High ingest throughput - Qdrant's async indexing decouples writes from index updates
• When you want the most complete feature set: sparse+dense hybrid search, quantization, snapshots, distributed mode

What it is not good for:

• Adding another service to a team that already struggles with infrastructure overhead - it requires running a container (or using Qdrant Cloud)
• Scenarios requiring strong transactional consistency between vector and relational data - Qdrant has no cross-store transactions

Decision Framework: Which Store Should You Choose?

This is how I reason through it with teams:

Question	sqlite-vec	SQL Server 2025	Qdrant
Zero infra setup needed?	Yes	No	No
Existing SQL Server license?	No	Yes	No
Need transactional consistency with relational data?	No	Yes	No
Scale beyond ~500k vectors?	Poor	Good	Excellent
High write concurrency (>100 writes/sec)?	Poor	Fair	Excellent
Multi-tenant with per-tenant isolation?	Poor	Fair	Excellent
Budget-constrained cloud deployment?	Best	Worst	Good

The practical decision rule I use:

1. Always start with sqlite-vec for local dev and tests - the zero-friction setup pays off every day
2. Use SQL Server 2025 in production if all three are true: you already run SQL Server 2025, your vector data is tightly coupled to relational rows (and you need transactional updates), and your scale stays under ~10M vectors per collection
3. Use Qdrant in production for everything else - especially multi-tenant SaaS, high write throughput, or scale beyond what SQL Server's ANN handles comfortably

When to Use All Three (The Hybrid Strategy)

The Microsoft.Extensions.VectorData abstraction makes a hybrid deployment practical:

• sqlite-vec: local dev + CI integration tests
• SQL Server 2025: staging (if SQL Server is already in the staging stack)
• Qdrant: production

The zero application-code changes required for this pattern is the argument for adopting Microsoft.Extensions.VectorData from day one, even if you start with sqlite-vec only. Defer infrastructure decisions; do not bake them into your application.

Is My Existing EF Core/SQL Server Stack Good Enough?

A common question from teams on EF Core + SQL Server: "Can we just store vectors in SQL Server without upgrading to 2025?"

Before SQL Server 2025, teams used varbinary(max) to store serialised float arrays and performed cosine similarity in application code - which means loading all candidate vectors into memory and computing distance in C#. That is fine for under a few thousand vectors. Above that, query latency grows linearly with collection size. SQL Server 2025's VECTOR_SEARCH with an ANN index is a genuine step change - not a marginal improvement.

If you cannot move to SQL Server 2025 yet, start with Qdrant. Do not try to make SQL Server do what it was not designed to do before the 2025 release.

Internal Links

• The RAG Pattern in ASP.NET Core: When to Use It and How
• What's New in .NET 10 AI Integration: Microsoft.Extensions.AI and IChatClient

FAQ

What is the easiest vector store to get started with in a .NET AI app? sqlite-vec. It requires zero infrastructure - just add the NuGet package and point it at a file path. It works with Microsoft.Extensions.VectorData using the same API you would use against Qdrant in production, so switching later requires only a DI registration change.

Can I use pgvector with Microsoft.Extensions.VectorData in .NET? Yes. The Microsoft.SemanticKernel.Connectors.Postgres connector supports pgvector. It is a strong alternative to Qdrant if your team runs PostgreSQL already, with similar reasoning to the SQL Server 2025 case - consolidate on infrastructure you already operate.

Is Qdrant free for production use? Qdrant is open-source (Apache 2.0) and free to self-host. Qdrant Cloud offers a managed tier with a free cluster (1GB storage). For most .NET production workloads, self-hosting on a single VM or a small Kubernetes deployment is cost-effective.

How does SQL Server 2025 vector search compare to pgvector? Both use ANN indexes (SQL Server uses a proprietary implementation, pgvector uses HNSW and IVFFlat). SQL Server 2025 has tighter T-SQL integration and enterprise support, which matters if you are on a Microsoft-aligned stack. pgvector on PostgreSQL is more mature for pure vector workloads and has a larger community of benchmarks and tooling.

What vector dimension should I use for my embeddings? This depends on your embedding model. text-embedding-3-small (OpenAI) produces 1536 dimensions. nomic-embed-text via Ollama produces 768. Larger dimensions capture more semantic nuance but cost more storage and compute. For most support-desk or document RAG use cases, 768-1536 dimensions hit the right balance. Declare the dimension in your VectorStoreRecordCollection definition so the provider creates the correct index.

Does sqlite-vec support filtered vector search? Yes, but with limitations. sqlite-vec supports pre-filtering (standard SQL WHERE reducing candidates before ANN search) and post-filtering. Pre-filtering can hurt recall significantly at high filter selectivity. Qdrant's payload-indexed filtering is purpose-built for this and maintains recall much better under tight filters.

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About the Author

I'm Celin Daniel, Co-founder of Coding Droplets. I've been building .NET and ASP.NET Core systems in production for 13+ years - APIs, distributed backends, enterprise platforms. Everything I write here comes from real shipping experience: patterns that held up, trade-offs that bit us, and lessons learned the hard way.

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