---
title: "PostgreSQL Development Services: Kensink Labs"
description: "PostgreSQL schema design, search, and vectors from a senior lab. Tested migrations, observability, full source ownership at handoff."
source: "https://www.kensink.com/technologies/postgresql/"
canonical: "https://www.kensink.com/technologies/postgresql/"
---
★ PostgreSQL Technologies & Infrastructure 8-week engagement

POSTGRESQL · THE DEFAULT DATABASE

# Production Postgres. Schemas, indexes, migrations, observability.

PostgreSQL is the most capable open database in the world, and it does more than teams realize: JSON, full-text search, geospatial, and vectors. We design the schema like it matters, because it does.

PostgreSQL pgvector Prisma

[Start a conversation →](https://www.kensink.com/contact) [All technologies →](https://www.kensink.com/technologies)

Cycle

8 weeks · fixed price

Stack

PostgreSQL

Output

Production code + eval suite

Handoff

Full source ownership

\[THE SHORT VERSION\]

## One database can do more than your stack diagram assumes.

Most products do not need a pile of specialized data stores. Postgres handles relational data, JSON documents, full-text search, geospatial, and (with pgvector) similarity search. Fewer moving parts means fewer ways to fail. We add another store only when Postgres genuinely cannot do the job.

When it fits

-   Effectively any product that stores relational data
-   Apps that would otherwise reach for a separate search or vector store too early
-   Teams that want one database to operate, back up, and reason about

When it does not

-   Extreme write-throughput workloads better served by purpose-built stores
-   Pure caching, where Redis belongs in front

\[HOW WE BUILD IT\]

## How we build with PostgreSQL.

01

### Schema as a design artifact

We model the data, constraints, and indexes deliberately. A good schema prevents whole categories of bugs and keeps queries fast as you grow.

02

### Use what Postgres already has

JSONB, full-text search, and pgvector before adding a second system. Fewer moving parts, fewer failure modes.

03

### Migrations and backups from day one

Versioned migrations, tested rollbacks, and a real backup and restore drill. Boring, and the reason you sleep.

04

### Observability on queries

Slow-query logging and index health tracked, so performance problems surface before users feel them.

\[ WHAT YOU GET \]

## What the engagement leaves behind.

1 DB

Relational, search, and vectors

Indexed

Designed for the real queries

Tested

Migrations and restores

100%

Source ownership at handoff

\[COMMON QUESTIONS\]

## Questions we get asked.

Do I need a separate vector database?

Usually not. pgvector keeps embeddings next to your relational data, which simplifies the architecture for most RAG and search workloads. A dedicated vector store earns its place only at large scale or with specialized indexing needs.

Postgres or MySQL?

We default to Postgres for its feature depth: JSONB, full-text search, extensions, and stronger standards compliance. MySQL is a fine choice on stacks already built around it.

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\[RELATED\]

## Worth a look next.

[

Technologies

pgvector

Read more

](https://www.kensink.com/technologies/pgvector)[

Design Patterns

Multi-tenant SaaS

Read more

](https://www.kensink.com/patterns/multi-tenant-saas)[

Languages

Python

Read more

](https://www.kensink.com/languages/python)

APPLIED K-FRAMEWORK

## Bring the problem.  
We’ll bring the build.

Senior engineers, eval suite at handoff, full source ownership. Sprint, program, or ongoing. We shape the engagement to the work.

[Start a conversation →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework)