Pick / avoid summary (fast)
Skim these triggers to pick a default, then validate with the quick checks and constraints below.
- ✓ 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
- ✓ 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
- × Not Postgres; ecosystem and features differ from Postgres-centric stacks
- × Operational constraints and limits must be validated
- × Operating model still requires governance and performance discipline
- × Switching costs increase as you depend on cloud ecosystem adjacency
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CheckCompatibility and workflow decide more than claims about scale.
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The trade-offdev workflow + MySQL vs AWS-aligned managed Postgres baseline.
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
What breaks first (decision checks)
These checks reflect the common constraints that decide between PlanetScale and Amazon Aurora (Postgres) in this category.
If you only read one section, read this — these are the checks that force redesigns or budget surprises.
- Real trade-off: MySQL-compatible serverless workflow vs AWS-aligned managed Postgres baseline—operating model and ecosystem fit dominate.
- Operational model and ownership: Define your scaling path (single region vs multi-region resilience)
- Ecosystem alignment vs portability: Identify integration gravity (identity, networking, observability)
Implementation gotchas
These are the practical downsides teams tend to discover during setup, rollout, or scaling.
Where PlanetScale surprises teams
- 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
Where Amazon Aurora (Postgres) surprises teams
- 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
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 and 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
Keep exploring this category
If you’re close to a decision, the fastest next step is to read 1–2 more head-to-head briefs, then confirm pricing limits in the product detail pages.
FAQ
How do you choose between PlanetScale and Amazon Aurora (Postgres)?
Choose PlanetScale when you want MySQL compatibility with a serverless/dev-first workflow and your application fits that model. Choose Aurora when you want an AWS-aligned managed Postgres-compatible baseline and your stack is Postgres-oriented. The decision is compatibility + workflow vs AWS-aligned production operations.
When should you pick PlanetScale?
Pick PlanetScale when: 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.
When should you pick Amazon Aurora (Postgres)?
Pick Amazon Aurora (Postgres) when: 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.
What’s the real trade-off between PlanetScale and Amazon Aurora (Postgres)?
MySQL-compatible serverless workflow vs AWS-aligned managed Postgres baseline—operating model and ecosystem fit dominate.
What’s the most common mistake buyers make in this comparison?
Choosing based on perceived scalability without aligning on MySQL vs Postgres compatibility and workflow needs.
What’s the fastest elimination rule?
Pick PlanetScale if MySQL compatibility + dev-first workflow is primary.
What breaks first with PlanetScale?
Choosing MySQL compatibility when your org is actually Postgres-centric (mismatch costs later). Production constraints that weren’t validated early (limits, operational expectations). Cost predictability once usage grows without guardrails.
What are the hidden constraints of PlanetScale?
Validate production constraints and cost drivers early. Switching costs exist if data model and workflow become coupled. Have an explicit exit plan if requirements later force a different operating model.
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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.