Database scaling infrastructure for Postgres applications

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

As applications grow, PostgreSQL databases become the bottleneck—connection storms, read/write scaling limits, and traffic spikes cause outages. Scaling requires either expensive managed database services or complex in-house infrastructure work with code changes, database migrations, and operational complexity.

Solution concept

A network proxy that transparently handles connection pooling, sharding, load balancing, and cross-shard query coordination without requiring application code changes or database migrations. It sits between the app and database, rewriting queries on the fly to distribute load, aggregate results, and maintain consistency across shards.

Who has it

Backend teams and platform engineers at companies running high-traffic PostgreSQL applications, from small startups to enterprises on self-hosted or managed Postgres (RDS, Azure, GCP).

How they handle it today

Today, teams scale databases manually: they modify application code to handle sharding logic, use external connection poolers like PgBounce or HAProxy, implement read replicas with in-app routing, and perform manual resharding during growth—a multi-week process that risks data loss.

Why now

PostgreSQL adoption is growing in high-traffic applications (Instacart, others), and managed databases are expensive at scale. Teams need faster, cheaper ways to scale without rewriting applications or waiting weeks for infrastructure changes. Postgres 16's logical replication improvements enable new resharding architectures.

How it could charge

Subscription or per-instance pricing based on connection count, query volume, or data throughput; or per-shard pricing for managed deployments. Could also charge per resharding operation or premium support for critical infrastructure.