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TECHNOLOGY CAPABILITY

PostgreSQL for transactional SaaS and tenant-aware data models

We use PostgreSQL when products need relational integrity, migration discipline, and tenant isolation without premature warehouse complexity.

PostgreSQL data map

Schema, tenants, queries, and scale paths

1Schema design
2Tenant isolation
3Query paths
4Migrations
5Replicas / reports
RLSIndexesMigrationsTenantsBackups

ARCHITECTURE FIT

Where PostgreSQL fits in the stack

Authoritative transactional store; read models may diverge in CQRS setups.

Typical PostgreSQL placement

  1. Apps and APIs

    Write and read transactional paths

  2. PostgreSQL

    Primary relational store

  3. Redis (optional)

    Hot reads and sessions

  4. Search index (optional)

    Denormalized projections

  5. Warehouse (later)

    Analytics when volume demands

Common use cases

  • Multi-tenant SaaS

    RLS / tenant_id

  • Billing and orders

    ACID

  • Operational reporting

    Replicas

  • CQRS write store

    Command path

DELIVERY CAPABILITIES

What we build with PostgreSQL

Data layer decisions that survive tenant growth and schema change.

  • Schema and migrations

    Outcome

    Versioned migrations with rollback planning and review gates.

    • Flyway/EF
    • Review
    • Rollback

  • Tenant isolation models

    Outcome

    Row-level security, schema-per-tenant, or hybrid patterns.

    • RLS
    • tenant_id
    • Policies

  • Query performance

    Outcome

    Indexes, explain plans, and hot path tuning before scale pain.

    • Indexes
    • EXPLAIN
    • Pooling

  • Backup and recovery

    Outcome

    Backup strategy, PITR options, and restore drills.

    • Backups
    • PITR
    • Monitoring

STACK DECISIONS

Architecture decisions for PostgreSQL

When PostgreSQL is the right default, and when another store fits better.

When we recommend it

  • Transactional integrity and joins matter
  • Multi-tenant SaaS needs predictable query patterns
  • Team wants open-source RDBMS without vendor lock-in

When we do not recommend it

  • Unstructured document model dominates with weak relational needs
  • Extreme write volume needs specialized time-series or log stores first
  • Global multi-region active-active is primary constraint (evaluate carefully)

Alternatives to consider

  • MongoDB

    Flexible documents and rapid schema drift in early MVP

  • Managed SQL (Azure SQL)

    Enterprise mandates Microsoft-managed SQL

  • Event store + projections

    Audit/replay dominates over normalized CRUD

PROOF

Related case studies

Stack choices from production platforms, not generic vendor claims.

Related architecture

Designing tenant-aware data on PostgreSQL?

We can review your tenant model, query patterns, migration plan, and scale path before locking schema decisions.

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