PostgreSQL
The default operational database for every business system we build.
PostgreSQL sits under nearly every operational system we build. The reason is not novelty — it is that business systems are relational, they need genuine constraints and transactions, and they need to answer reporting questions years after the code that wrote the data was last touched. Postgres does all three, reliably, for decades.
It has also quietly absorbed capabilities that used to require separate systems: JSONB for semi-structured data, full-text search, geospatial through PostGIS, time-series through TimescaleDB, and vector similarity through pgvector. For a great many applications this means one database instead of four, which is a substantial reduction in operational surface.
The decision we defend most often is choosing it over a document database. A schema-less store feels faster in week two and considerably slower in year two, when every query must defend against six historical shapes of the same record. We have been called in to unwind that more than once.
Where it fits
What PostgreSQL genuinely gives us
Constraints that actually hold
Foreign keys, checks and unique constraints enforced by the database mean bad data cannot enter regardless of which application wrote it.
Transactions you can rely on
Multi-table operations either complete or do not, which is non-negotiable for anything touching money or stock.
One database, several jobs
JSONB, full-text search, PostGIS, TimescaleDB and pgvector reduce the number of moving parts a small team must operate.
Portable and unowned
Runs on any cloud, on-premise, or on a laptop, with no licence and no vendor able to change the terms.
Schema decisions with the longest half-life
The database schema outlives every other decision in a system. Frameworks get replaced twice before the core tables change. So we spend disproportionate time on it: correct normalisation with deliberate denormalisation where reporting demands it, natural versus surrogate keys chosen consciously, and audit columns on every table that matters.
Every business-critical table carries who changed what, when, and what the previous value was. This is not a compliance nicety; it is what allows a disputed figure to be reconstructed eight months later without an argument.
Performance work that actually moves the needle
Almost every slow Postgres system we inherit has the same three problems: missing indexes on foreign keys and filter columns, N+1 query patterns from an ORM, and connection pool settings left at defaults. Fixing those usually accounts for most of the available improvement.
Beyond that, we read query plans rather than guessing, add partial and composite indexes where the access pattern justifies them, partition tables that have grown past comfortable maintenance size, and put reporting on a read replica so an analyst's query cannot slow down order entry.
How PostgreSQL projects usually go wrong
These are the failure modes we look for first when we are called in to rescue somebody else's implementation.
PostgreSQL questions we get asked
Including where we would recommend something other than PostgreSQL. Call +91 70033 91355 and you will get the same answer from an engineer.
PostgreSQL for anything with complex relationships, analytical queries, or a need for advanced types. MySQL is entirely capable and remains sensible where an existing team knows it well or where a platform such as WordPress or a specific hosting arrangement assumes it. For a new business system with no constraint, we choose Postgres — the constraint handling, query planner and extension ecosystem are meaningfully stronger.
Almost certainly. We run Postgres instances with hundreds of millions of rows serving transactional workloads without difficulty. The techniques that matter are partitioning, indexing discipline and a read replica for reporting. The point where you genuinely outgrow it for analytics is where a warehouse belongs anyway — and that is a different tool, not a bigger version of the same one.
Yes, and the licence saving is usually the driver. The work is mostly in translating stored procedures and handling semantic differences — empty string versus null, date behaviour, collation. We run both systems in parallel with row-level reconciliation until they match exactly, then cut over with a tested rollback. Typical cutover downtime on our recent migrations has been under five minutes.
Services built on PostgreSQL
Technologies we pair it with
Tell us what is slowing your business down.
A 30-minute call with a senior engineer — not a salesperson. You leave with an architecture sketch and an honest cost range, whether or not you hire us.
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