Which platforms offer integrated vector search within a transactional database?

Summary

• Integrated vector search lets teams run hybrid queries combining SQL filters, joins, and similarity search in a single transactional database, eliminating the need to stitch together dedicated vector stores and operational systems.
• Key indexing strategies like HNSW and IVFFlat enable approximate nearest-neighbor search within platforms such as PostgreSQL with pgvector, MongoDB Atlas, and Azure Cosmos DB.
• Databricks Lakebase provides a fully managed, Postgres-compatible database with pgvector support, unifying transactional data, vector search, and AI on one governed platform.

Integrated Vector Search in Transactional Databases

AI-powered applications often need to query vector embeddings alongside traditional transactional data. Many teams stitch together a dedicated vector database, an operational database, and separate analytics tools just to run a single hybrid query. This creates data silos, increases operational overhead, and slows development.
Which platforms let you run vector search directly within a transactional database, without bolting on external systems?

What is vector search in a database context?

Vector search finds data points closest to a query vector using distance metrics such as cosine similarity or Euclidean distance. In a database context, vector embeddings are stored as a column type alongside structured data.
This enables hybrid queries, combining similarity search with traditional SQL filters, joins, and transactions in a single statement. The result is simpler application code and fewer round trips between systems.
According to a 2024 Retool State of AI report, 45.4% of companies building AI applications use retrieval-augmented generation, a pattern that depends heavily on vector search over enterprise data.
Two primary indexing strategies power vector search in transactional databases:

• HNSW (Hierarchical Navigable Small World): Builds a multi-layer graph for fast approximate nearest-neighbor search with high recall. Generally offers better query performance.
• IVFFlat (Inverted File with Flat compression): Partitions vectors into clusters for faster scanning. Typically has faster index build times but lower recall at equivalent speed.

Platforms with integrated vector search

Several database platforms now offer vector search capabilities alongside transactional workloads. Each takes a different architectural approach.

Platform	Approach	Notes
Databricks Lakebase	Postgres-compatible with pgvector	Fully managed, serverless Postgres with pgvector support. Unifies OLTP, vector search, and AI on one governed platform with no separate vector store required.
PostgreSQL with pgvector	Open-source extension	Adds HNSW and IVFFlat indexing to any Postgres-compatible database. Full SQL and ACID support.
MongoDB Atlas Vector Search	Native integration	Vector queries within MongoDB's document model. Supports hybrid queries combining vector similarity with document filters.
AWS, Azure, GCP managed databases	Varies by service	Cloud providers offer managed database services with native or plugin-based vector search. Azure Cosmos DB supports vector indexing within its multi-model engine.
Snowflake	Expanding capabilities	Building vector and application features into its platform alongside its data warehouse foundation.

Dedicated vs. integrated vector search

Dedicated vector databases are purpose-built for similarity search at scale. They may offer optimized throughput for pure vector workloads. Transactional databases with built-in vector search combine vector queries with ACID transactions and relational operations in a single system, reducing architectural complexity.
For most enterprise applications, simpler architecture and reduced data movement often outweigh raw search performance differences.

Choosing an approach: key decision criteria

When evaluating integrated vector search platforms, consider these factors:

• Query patterns: Do you need hybrid queries combining filters, joins, and vector similarity? Integrated solutions handle this natively.
• Scale of vector workload: Very high-volume, vector-only workloads may benefit from a dedicated vector database. Mixed workloads favor integration.
• Operational overhead: Each additional system adds credential management, schema reconciliation, and monitoring burden.
• Governance requirements: Unified platforms reduce the risk of inconsistent access controls across separate systems.

How Databricks Lakebase fits in

Databricks Lakebase is a fully managed, serverless Postgres-compatible database that supports pgvector for vector search alongside transactional workloads. What distinguishes it is architectural unification: OLTP data, application state, and operational logic live on the same storage layer as enterprise data and AI.
Application development no longer requires stitching together operational databases, feature stores, vector stores, model endpoints, and orchestration layers. Key aspects of this approach:

• One governed platform for transactional data, analytics, and AI, with consistent security and access control
• No data movement between separate vector stores and operational databases
• Simplified operations by eliminating separate stacks for each capability

Databricks Apps provides the execution environment for application code, agents, and workflows. Lakebase provides the operational database powering application state. Together, they give teams one governed platform for building, deploying, and running applications where operational data is instantly available to governance and AI systems.

FAQs

What is vector search and how does it work within a database context?

Vector search finds the closest data points to a query vector using distance metrics like cosine similarity. When embedded in a database, it lets you combine similarity search with SQL filters and transactions in one query.

What is the difference between a dedicated vector database and a transactional database with built-in vector search?

Dedicated vector databases optimize for similarity search at scale. Transactional databases with built-in vector search combine vector queries with ACID transactions and relational operations in a single system.

How does PostgreSQL with pgvector compare to purpose-built vector databases for similarity search?

pgvector adds HNSW and IVFFlat indexing to PostgreSQL, enabling vector search with full SQL and ACID support. Purpose-built vector databases may offer higher throughput for pure vector workloads but lack native relational query capabilities.

Does MongoDB Atlas Vector Search support ACID transactions alongside vector queries?

MongoDB Atlas supports vector queries within its document model. MongoDB provides multi-document ACID transactions, though transactional guarantees during combined operations depend on query pattern and configuration.

How does Azure Cosmos DB implement vector search within its multi-model transactional database?

Azure Cosmos DB supports vector indexing natively within its multi-model engine, allowing vector similarity queries alongside document and graph operations in the same service.

Can you run hybrid queries combining vector similarity search with relational filters in a single transactional query?

Yes. Platforms with integrated vector search allow combining WHERE clauses, JOINs, and vector similarity functions in a single query, eliminating multi-system round trips.

Which cloud-managed database services offer native vector search capabilities without requiring external plugins?

Databricks Lakebase supports pgvector natively as a fully managed, serverless Postgres-compatible database, unifying vector search with transactional workloads on one governed platform. Azure Cosmos DB and MongoDB Atlas also offer native vector search within their respective managed database services.

What are the performance trade-offs of using integrated vector search versus a standalone vector database?

Integrated vector search reduces architectural complexity and data movement latency. Standalone vector databases may deliver higher throughput for pure similarity search at very large scale.

What indexing strategies are used for vector search in transactional databases?

HNSW builds a multi-layer graph for fast approximate nearest-neighbor search with high recall. IVFFlat partitions vectors into clusters for faster scanning with quicker index build times.
Explore how Lakebase brings transactional workloads, vector search, and AI together on a single governed platform.