Backend & APIs

PostgreSQL - the database powering modern applications and AI workloads

Database architecture, schema design, query optimisation, and vector search with PostgreSQL and pgvector for applications that need reliability, performance, and AI readiness.

✦Production-ready

◈Backend & APIs

◉We build with it

Plan this build Get Pricing for My Project

PostgreSQL

How we use it

PostgreSQL is our default database for every project requiring a relational data model. We use it with Prisma ORM for TypeScript projects and SQLAlchemy for Python. For AI workloads, we use pgvector for in-database vector search - eliminating the need for a separate vector database.

Best fit for

SaaS data architecture

AI vector search

High-traffic transactional systems

Why now

PostgreSQL has achieved a historic milestone: it is now the most-used database among professional developers, with 55.6% of respondents in the 2025 Stack Overflow Developer Survey - up from 48.7% in 2024, the largest single-year expansion in its history, opening a 15-percentage-point gap over MySQL. The AI wave has accelerated this growth: pgvector, PostgreSQL's vector similarity search extension, pre-installed on virtually every major hosted provider as of mid-2025, means teams building RAG systems and LLM-powered features no longer need a separate vector database like Pinecone - they can run embeddings queries alongside transactional queries in the same PostgreSQL instance. PostgreSQL 18, released September 2025, added native UUID v7, asynchronous I/O for high-load workloads, and virtual generated columns.

What's included

Capabilities

API architecture & endpoint design

Database schema & integration

Authentication, RBAC & security hardening

Third-party integrations & webhooks

Performance optimisation & caching strategy

Fit analysis

Is this right for you?

Honest breakdown of where PostgreSQL shines — and where it doesn't. Pick the right tool.

When to choose this

Right fit scenarios

You are building a SaaS product with relational data - users, organisations, billing, permissions - and need ACID transactions, foreign key enforcement, and a schema you can evolve safely

You are building AI-powered features that require vector similarity search - semantic search, recommendations, RAG pipelines - and want to run embeddings queries in the same database as your transactional data

Your application needs complex querying - multi-table joins, aggregations, window functions, full-text search - that a document database like MongoDB handles awkwardly

You need geospatial capabilities - distance queries, polygon intersections, location-based filtering - via PostGIS, which is the most mature geospatial extension available in any database

You want a self-hosted, open-source database with no per-row or per-request pricing, so your infrastructure costs scale predictably as your data volume grows

When to choose this

Right fit scenarios

You are building a SaaS product with relational data - users, organisations, billing, permissions - and need ACID transactions, foreign key enforcement, and a schema you can evolve safely

Your application needs complex querying - multi-table joins, aggregations, window functions, full-text search - that a document database like MongoDB handles awkwardly

You need geospatial capabilities - distance queries, polygon intersections, location-based filtering - via PostGIS, which is the most mature geospatial extension available in any database

You want a self-hosted, open-source database with no per-row or per-request pricing, so your infrastructure costs scale predictably as your data volume grows

Honest limitations

Not the best fit if…

Pure document storage workloads where the data is deeply nested, schema-less, and frequently changes shape - MongoDB or a document store is better suited for genuinely unstructured data

Time-series data at massive scale - dedicated time-series databases like TimescaleDB (which is a PostgreSQL extension) or InfluxDB are more efficient for very high write rates of time-stamped metrics

Globally distributed applications requiring multi-region writes with sub-10ms latency in all regions - CockroachDB or PlanetScale handle distributed SQL more natively

Teams that need a fully managed, zero-schema database with no migrations - Firebase or DynamoDB might fit teams where schema rigour creates more friction than value

Services

Built into our workflow

Development

Web Design & Development

Websites that work as hard as you do

Learn more

Strategy

AI Automation

Do more with the same team

Learn more

Development

DevOps & Hosting

Infra that never sleeps, never fails

Learn more

Common questions

You're probably wondering

Why PostgreSQL over MySQL?

PostgreSQL has a richer feature set: better support for JSON and JSONB, more complete SQL standard compliance, superior support for complex queries with CTEs and window functions, the pgvector extension for AI workloads, PostGIS for geospatial, and a more active development community. The 2025 Stack Overflow Survey shows PostgreSQL at 55.6% adoption vs MySQL at 40.5% among professional developers - the gap is accelerating in PostgreSQL's favour.

What is pgvector and do I need it for AI features?

pgvector is a PostgreSQL extension that adds vector similarity search - the ability to store embeddings (numerical representations of text, images, or any data) and query for the most semantically similar results. It is the foundation of RAG (Retrieval-Augmented Generation) systems. If you are building any feature that involves semantic search, document Q&A, recommendations, or LLM-powered chat over your data, pgvector lets you do this inside PostgreSQL without spinning up a separate vector database like Pinecone.

How do you use PostgreSQL with Next.js or Node.js?

We use Prisma ORM as our default for TypeScript projects - it provides type-safe database access, automatic migration generation, and an excellent developer experience. For performance-critical queries or complex SQL, we use raw SQL via Prisma's queryRaw. For Python projects, we use SQLAlchemy with asyncpg for async access. We always use connection pooling (PgBouncer or Supabase pooler) in production.

How do you manage PostgreSQL migrations in production?

We use Prisma Migrate for TypeScript projects - migrations are generated automatically from schema changes, version-controlled in Git, and applied as part of the CI/CD pipeline before deployment. We never run migrations manually against production. For Python projects, we use Alembic. Every migration is reviewed in a pull request, tested against a staging database that mirrors production, and applied atomically.

Can PostgreSQL handle the scale of a serious SaaS product?

Yes. Instagram, Shopify, GitHub, and Notion all run on PostgreSQL. The practical limits for a single PostgreSQL instance are far beyond what most SaaS products ever reach. For very high traffic, we implement read replicas for analytics and reporting queries, connection pooling to handle connection limits, and query optimisation (indexes, EXPLAIN ANALYZE) to keep p99 latencies under 50ms.

How do you back up and recover PostgreSQL databases?

We configure automated daily base backups plus continuous WAL (Write-Ahead Log) archiving to S3, enabling point-in-time recovery to any second within the retention window (typically 30 days). We test restores quarterly on a separate instance to verify the backup is actually usable. For business-critical systems, we configure a warm standby replica in a separate availability zone with automatic failover.

What is the difference between Supabase and a self-hosted PostgreSQL?

Supabase is a managed backend-as-a-service built on PostgreSQL - it adds auth, real-time subscriptions, storage, and auto-generated APIs on top of Postgres. It is excellent for prototypes, MVPs, and small-to-medium SaaS products where managed infrastructure reduces operational burden. Self-hosted PostgreSQL on AWS RDS or a VPS gives you more control over configuration, cost, and data residency. We choose based on team size, compliance requirements, and expected data volume.