Postgres is boring. That is the highest compliment a production database can earn. While the internet argues about edge databases, vector stores, and the latest serverless replication scheme, we keep choosing Postgres for one reason: nothing else matches its combination of reliability, feature depth, and ecosystem maturity.
What Postgres gets right
Five things stand out:
- It does not lose your data. Postgres has 35+ years of bug-fixing on durability. The WAL is rock-solid. Replication just works.
- Real ACID transactions. Multi-row updates that are atomic across tables. Your billing logic does not need to invent its own consistency.
- JSONB. When you do need document-store flexibility, you have it inside Postgres — with proper indexes, with SQL, with transactions. You almost never need MongoDB.
- Row-level security. The single feature that makes multi-tenant SaaS sane to build. Enforce tenant isolation at the database level, not at the application layer.
- pgvector. Vector search is now a first-class Postgres capability. You do not need a separate Pinecone or Weaviate instance for most AI features.
The Postgres ecosystem in 2026
Postgres is no longer just a database — it is a platform. Supabase and Neon are serverless, branchable Postgres with auth and storage baked in. Render and Railway give you managed Postgres in 30 seconds. Prisma and Drizzle are excellent TypeScript ORMs that make Postgres feel native to your Node.js or Next.js app. pgvector turns it into a vector store. Citus turns it into a distributed database.
You can start small (a Supabase free tier) and scale to millions of users on the same database engine. That is not true of most alternatives.
When we still consider alternatives
We are not religious about it. There are cases where Postgres is not the right answer:
- Massive append-only workloads (logs, metrics, events at billions of rows per day): ClickHouse or TimescaleDB.
- Real-time chat / pub-sub at scale: Redis for the pub-sub layer, Postgres for the persistent state.
- Caching: Redis alongside Postgres, not instead of it.
- Vector search at huge scale (100M+ vectors): a dedicated store like Qdrant. For most apps, pgvector is fine.
What is conspicuously missing from this list: MongoDB. We have not started a new project on Mongo in five years. Every "we need flexible schema" use case is better served by Postgres JSONB.
Row-level security: the multi-tenant superpower
Building multi-tenant SaaS without row-level security (RLS) means every query in your app must filter by tenant_id. Forget one, leak another tenant's data. With Postgres RLS, the database itself enforces the filter — even if your application bug forgets to add the WHERE clause, the wrong tenant cannot see the data.
We build every multi-tenant SaaS on RLS. It is the single most important architectural decision for safety. See our SaaS development page for how we structure it.
The boring-tech argument
There is a famous essay by Dan McKinley called "Choose Boring Technology." The idea is that every team has a limited budget of innovation tokens. Spending one on a database means you cannot spend it on, say, the actual product. Postgres lets you spend all your tokens elsewhere.
The exciting tech failure mode is having a Mongo cluster, a Redis primary, a Cassandra read replica, and a vector store — and a four-person team trying to operate them all. Pick Postgres and that team can ship product features instead.
What we tell founders
When a founder asks us "what database should we use," the answer is almost always Postgres. The conversation usually goes:
- Founder: Should we use Mongo? I heard it scales.
- Us: What is your scale target for year 1?
- Founder: 1,000 customers.
- Us: Postgres can comfortably handle 100,000 customers. You will pivot before you hit Mongo's break-even.
At 10 million users, you may need to think harder about the database. But you should never make the decision in advance — pick boring Postgres, ship the product, and revisit only if you ever earn the right to.