Components PGVector

Using Cake for PGVector

PGVector is an open-source Postgres extension for storing and querying vector embeddings. Cake integrates PGVector to enable hybrid search, recommendation, and RAG use cases using existing Postgres infrastructure.

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Cake cut a year off our product development cycle. That's the difference between life and death for small companies

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President, Altis Labs

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Cake cut a year off our product development cycle. That's the difference between life and death for small companies

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How it works

Add AI vector search to Postgres with PGVector on Cake

Cake operationalizes PGVector to enable AI teams to add vector search capabilities to Postgres—helping combine relational filtering with similarity search for hybrid AI applications while simplifying deployment and scaling.

Hybrid vector-relational querying

Store and query embeddings alongside structured data in Postgres for richer AI applications.

Orchestrated deployment and scaling

Run PGVector as part of Cake’s automated, policy-driven infrastructure layer.

Compliance and governance built in

Apply security, audit logging, and resource control to PGVector workloads with Cake.

Frequently asked questions about Cake and PGVector

What is PGVector?

PGVector is an open-source extension for PostgreSQL that adds native support for storing and querying vector embeddings for AI applications.

How does Cake integrate with PGVector?

Cake integrates PGVector into AI pipelines by automating deployment, scaling, and connecting Postgres vector search to model training, inference, and RAG workflows.

What AI use cases benefit from PGVector on Cake?

PGVector supports AI use cases such as hybrid search (combining relational and vector queries), recommendation systems, and semantic search in Cake environments.

Does Cake help govern and secure PGVector deployments?

Yes—Cake applies governance, security, and audit logging to PGVector deployments to ensure secure and compliant AI workflows.

Can PGVector be used alongside other AI tools in Cake?

Absolutely—PGVector works seamlessly alongside other AI components in Cake, including model training, embedding generation, and search pipelines.

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