Components Postgres

Using Cake for Postgres

PostgreSQL (Postgres) is an advanced open-source relational database. Cake integrates Postgres as a core data layer for AI applications—making structured data management secure, scalable, and automated.

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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 Postgres

What is Postgres?

Postgres is an open-source relational database known for reliability, extensibility, and performance, widely used for storing structured data in enterprise applications.

How does Cake integrate with Postgres?

Cake integrates Postgres as a managed data layer for AI projects, automating provisioning, scaling, and security while connecting Postgres to model pipelines.

What AI use cases benefit from Postgres on Cake?

Postgres is commonly used for storing training datasets, feature stores, metadata, and serving data for AI models within Cake-managed environments.

Does Cake help secure and govern Postgres databases?

Yes—Cake enforces encryption, role-based access, and audit logging for Postgres deployments to help meet security and compliance requirements.

Can Postgres work alongside other AI components in Cake?

Yes—Postgres works seamlessly with other components in Cake, including feature stores, vector databases, and orchestration tools in AI pipelines.

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