Data & APIs

PostgreSQL Development Services

We design PostgreSQL schemas that hold up under real transaction volume as well as the demo dataset.

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How we work

PostgreSQL is our default relational database for new projects, for good reason: strong consistency guarantees, mature replication, a rich extension ecosystem, and JSONB support that handles semi-structured data while keeping relational integrity across the rest of the schema. Our postgresql development services cover schema design and normalization, query and index tuning, and increasingly pgvector for embeddings, so a retrieval-augmented generation feature can sit in the same database as the rest of the application's data.

We design schemas around the queries an application actually runs, not around an abstract entity-relationship diagram. That means thinking about indexing strategy, when to denormalize for read performance, and how partitioning will affect a table once it passes a few million rows, decisions that are far cheaper to make early than to retrofit later.

Most of our Postgres work runs on managed services, Amazon RDS or Aurora, Google Cloud SQL, or a provider like Supabase, because the operational discipline of backups, patching and failover is not worth reinventing for most teams. We reserve self-managed Postgres for cases with specific extension or configuration requirements a managed service does not support.

Fit

When PostgreSQL is the right choice, and when it is not

Good fit

Your data is genuinely relational: orders reference customers, invoices reference orders, and you need joins and foreign key constraints to hold that together correctly.
You are handling financial, transactional or other data where strong consistency and ACID guarantees are not optional.
You want to add retrieval-augmented generation to a product and would rather query embeddings with pgvector than introduce a separate vector database.
Your data model mixes strict relational structure with some semi-structured fields, which JSONB columns handle without forcing the whole schema into a document shape.
You need mature tooling for replication, point-in-time recovery and migrations that has been battle-tested for decades.

Consider something else

A workload that is purely key-value lookups at very high throughput, such as session storage or a cache, is better served by Redis or DynamoDB.
Data that is naturally document-shaped, with few relationships and a schema that changes often between records, can be more comfortable to model in MongoDB.
Extremely high-volume time-series data, such as fine-grained IoT sensor readings, often benefits from a purpose-built time-series database or the TimescaleDB extension rather than plain Postgres tables.
Teams needing petabyte-scale analytical queries across huge datasets typically get better performance from a columnar warehouse like BigQuery or Redshift than from Postgres alone.

What we build

What we build with PostgreSQL

Schema design
Normalized relational schemas with foreign key constraints that enforce data integrity at the database layer rather than relying solely on application code to catch mistakes.
JSONB for semi-structured data
Columns that hold flexible, evolving fields, such as a product's variant attributes, alongside strictly typed relational columns for the data that needs constraints.
pgvector for retrieval
Vector columns and similarity search inside the same database as the rest of an application's data, avoiding a separate vector database for retrieval-augmented generation at moderate scale.
Full-text search
Built-in text search with weighted ranking, often sufficient for an application's search needs without introducing Elasticsearch or a dedicated search service.
Replication and failover
Streaming replication for read replicas and high availability, and logical replication for selectively syncing data between systems or into a data warehouse.
Partitioning and scaling
Table partitioning by date or tenant for tables that have grown past comfortable single-table size, keeping query performance predictable as data volume grows.

Ecosystem

Libraries and tools we pair it with

pgvector
PostGIS
TimescaleDB
PgBouncer
Prisma
Supabase
Amazon RDS and Aurora
pg_stat_statements

PostgreSQL Versus MongoDB for Transactional Data

The decision between PostgreSQL and MongoDB usually comes down to how relational the data actually is, not a general preference for one database family. Financial and transactional data, where an order references a customer, an invoice references line items, and totals must reconcile exactly, benefits from Postgres's foreign key constraints and multi-table transactions in ways that are awkward to replicate in a document database.

MongoDB's document model earns its place when data is naturally self-contained per record and relationships between documents are rare or shallow. We have migrated more than one project from MongoDB back to Postgres after reporting and reconciliation requirements made the document model harder to maintain than the relational schema it replaced would have been.

Using pgvector for Retrieval-Augmented Generation

pgvector adds a vector column type and similarity search functions directly to Postgres, which means an application can store embeddings for a document or chat message alongside the relational data that already describes it, and query both together in one SQL statement.

For most applications with up to a few million vectors, pgvector on a properly indexed table performs well and avoids the operational cost of running a separate vector database like Pinecone or a self-hosted Milvus cluster. Once vector count and query volume grow large enough that index build time and recall tuning become a genuine bottleneck, a dedicated vector database becomes worth the added complexity; we treat that as a scaling decision to revisit later, not a default starting point.

pgvector fits well up to roughly a few million vectors with proper indexing
Keeps embeddings joinable with existing relational data in one query
Revisit a dedicated vector database once index build time or recall becomes a bottleneck

Services

Services that use PostgreSQL

Industries

Where we use it

FAQ

Questions about PostgreSQL

Is PostgreSQL fast enough for a high-traffic application, or do we need to move to MongoDB for scale?

Postgres handles very high transaction volumes well when the schema and indexes are designed for the actual query patterns, and read replicas or connection pooling with PgBouncer handle most scaling needs before a database change is ever required. MongoDB's scaling model is different, not automatically faster, and switching databases rarely fixes performance problems that come from missing indexes or an unoptimized schema.

Can PostgreSQL handle both our transactional data and our AI search features?

For most applications, yes. The pgvector extension lets you store embeddings alongside your existing relational tables and query both together, so you avoid running and syncing a separate vector database for a typical retrieval-augmented generation feature. This holds up well until vector volume reaches a scale where a dedicated vector database's specialized indexing becomes worth the added operational overhead.

What is JSONB in Postgres, and when should we use it instead of a normal column?

JSONB stores semi-structured JSON data in a binary format that supports indexing and querying, unlike a plain text column. We use it for fields that genuinely vary between records, such as product attributes that differ by category, while keeping data with fixed structure and relationships in regular typed columns and foreign keys.

How difficult is it to migrate from MySQL to PostgreSQL?

Difficulty depends mostly on how much application code relies on MySQL-specific SQL syntax or functions. Schema and data migration itself is well supported by tools like pgloader, but query rewrites, especially around date functions, case sensitivity and some join behaviors, are usually where the real migration effort goes, along with a validation period comparing results between both databases.

Do we need a database administrator to run PostgreSQL in production?

Not for most applications running on a managed service like RDS, Aurora or Cloud SQL, which handle backups, patching and failover automatically. What still matters is someone reviewing slow queries, index usage and connection pooling periodically, which we typically set up as part of delivery along with alerting on the metrics that actually predict a performance problem.

Compare

Alternatives we also work with

MongoDBWe build on MongoDB when the data is truly document-shaped, and tell you when it is not.

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