Technology · Database
PostgreSQL
PostgreSQL · SQL · pgvector
PostgreSQL earns its place wherever being right matters more than being flexible. Constraints, transactions and joins are not overhead in a financial or eligibility domain — they are the specification, enforced by the database rather than hoped for in application code. It is also the least surprising database to hand to another team in five years.
How TrivialWorks uses it.
We reach for Postgres when the domain is relational at its centre: disbursement and welfare platforms, payment records, reporting-heavy back offices. It runs alongside MongoDB rather than against it — we have designed systems using both, each holding the data whose shape it actually suits. With pgvector it can also carry embeddings, which keeps some AI features from needing a separate data platform at all.
Decision guide
Should your project use PostgreSQL?
Practical selection guidance — the conversation we would have with you before writing a line of code.
When it’s the right choice
- Money, eligibility and anything audited — where a constraint in the database is cheaper than a bug in production
- Reporting and analytics as a core workload, not an afterthought: joins, window functions, aggregates
- Domains with genuinely relational entities, where duplicating data across documents would drift
- Teams that want SQL — the most portable, most hireable data skill there is
When it isn’t
- Rapidly evolving product data whose shape changes every sprint — a migration per iteration is a tax you feel
- Deeply nested document-shaped data read as a whole, where MongoDB models the access pattern more directly
- Simple key-value caching or queues, which Redis does far better and far cheaper
Best use cases
Where PostgreSQL makes practical sense.
Disbursement & welfare platforms
Eligibility rules, entitlement records and payment history, where the audit trail is as important as the balance.
Financial and ledger systems
Double-entry records and reconciliation, held together by transactions rather than by application discipline alone.
Reporting-heavy back offices
Operational reporting where every question is a join and the answer has to reconcile with the last one.
Multi-tenant SaaS data
Row-level security and per-tenant isolation enforced by the database, not reimplemented in every endpoint.
Technology pairings
Commonly paired with PostgreSQL.
Related services
Services that commonly use it.
Questions
PostgreSQL, asked straight.
PostgreSQL or MongoDB — which should we use?
Whichever matches how the data is actually read and how much correctness costs you. Ledgers, eligibility and audited records want Postgres' constraints; profiles, catalogues and evolving content want MongoDB's flexibility. We run both, sometimes in one system, and will show you the reasoning rather than a house preference.
Can PostgreSQL handle AI and semantic search?
Yes — the pgvector extension stores embeddings beside your relational data, so retrieval, semantic search and recommendations can ship without introducing a dedicated vector database. For larger or higher-throughput retrieval workloads a purpose-built store may still earn its keep, and we will say when that point arrives.
Can you migrate us from MySQL, SQL Server or Oracle to PostgreSQL?
Yes — that is a modernisation engagement. We migrate incrementally with the old system still serving traffic, moving schema and data in stages rather than betting the business on a single cutover weekend.
Thinking about PostgreSQL?
Send the requirement and you get back a functional specification — screens, data model, stack and an estimate — at no charge. If PostgreSQL is the wrong choice for it, that will be in there too.