PlanetScale vs Amazon Aurora (Postgres)
Why people compare these: Teams compare PlanetScale and Aurora when deciding between a MySQL-compatible serverless workflow and an AWS-first managed Postgres baseline.
The real trade-off: MySQL-compatible serverless workflow vs AWS-aligned managed Postgres baseline—operating model and ecosystem fit dominate.
Common mistake: Choosing based on perceived scalability without aligning on MySQL vs Postgres compatibility and workflow needs.
At-a-glance comparison
PlanetScale ↗
Serverless MySQL platform (Vitess-based) evaluated when teams want MySQL compatibility plus modern workflows and horizontal scaling patterns.
- ✓ MySQL-compatible relational option with modern workflows
- ✓ Often evaluated when teams want serverless model and scaling patterns
- ✓ Can be a strong fit for teams preferring MySQL ecosystem
Amazon Aurora (Postgres) ↗
AWS flagship Postgres-compatible managed relational database, typically evaluated when teams want a managed Postgres core aligned to AWS infrastructure patterns.
- ✓ Strong AWS ecosystem alignment for production relational workloads
- ✓ Managed relational foundation versus self-managed Postgres
- ✓ Common enterprise choice when already standardized on AWS
Where each product pulls ahead
These are the distinctive advantages that matter most in this comparison.
PlanetScale advantages
- ✓ MySQL-compatible dev-first workflow
- ✓ Serverless model designed for modern teams
- ✓ Scaling patterns aligned to the platform model
Amazon Aurora (Postgres) advantages
- ✓ AWS-first managed Postgres-compatible baseline
- ✓ Aligned with AWS governance and tooling
- ✓ Strong fit for AWS-native architectures
Pros & Cons
PlanetScale
Pros
- + You prefer MySQL compatibility and want a dev-first workflow
- + You want a serverless operating model and modern environment workflows
- + You can validate limits and production constraints early
Cons
- − Not Postgres; ecosystem and features differ from Postgres-centric stacks
- − Operational constraints and limits must be validated
- − Migration and data model decisions still carry switching costs
- − Platform coupling can create switching cost if you adopt platform-specific workflows deeply
- − You must validate production constraints early to avoid rework later
- − Not a fit if your stack is deeply Postgres-centric and you need Postgres compatibility
Amazon Aurora (Postgres)
Pros
- + You’re Postgres-oriented and want an AWS-aligned managed baseline
- + You need an infra-first operating model aligned to AWS governance
- + You can own migrations and schema governance
Cons
- − Operating model still requires governance and performance discipline
- − Switching costs increase as you depend on cloud ecosystem adjacency
- − Cost drivers can be non-obvious without careful monitoring
- − Migration and schema governance remain team-owned (managed doesn’t mean hands-off)
- − Performance tuning and capacity planning still matter for production OLTP workloads
- − Observability and incident response ownership remains critical for database reliability
Which one tends to fit which buyer?
These are conditional guidelines only — not rankings. Your specific situation determines fit.
- → Pick PlanetScale if MySQL compatibility + dev-first workflow is primary.
- → Pick Aurora if Postgres compatibility + AWS ecosystem alignment is primary.
- → Compatibility and workflow decide more than claims about scale.
- → The trade-off: dev workflow + MySQL vs AWS-aligned managed Postgres baseline.
Sources & verification
We prefer to link primary references (official pricing, documentation, and public product pages). If links are missing, treat this as a seeded brief until verification is completed.