Convex vs PlanetScale
Convex and PlanetScale both answer the same question: where does your data live? A reactive backend where you write TypeScript functions instead of SQL, and queries push updates to clients automatically. Managed MySQL built on Vitess, aimed at teams that expect serious scale and want schema changes without downtime. PlanetScale covers more of the ecosystem, so it survives a change of framework; Convex is the better fit while you stay where it is strongest.
Verdict
PlanetScale covers more of the ecosystem, so it survives a change of framework; Convex is the better fit while you stay where it is strongest.
Pick Convex if
- ✓Queries are reactive by default, so live-updating UI needs no subscription plumbing.
- ✓Backend functions are TypeScript sharing types with the frontend end to end.
- ✓Transactions and scheduling are built in, so background jobs need no extra service.
Pick PlanetScale if
- ✓Non-blocking schema changes reviewed like code before they hit production.
- ✓Vitess sharding means the growth path to very large tables already exists.
- ✓Deploy requests give schema migrations an approval workflow teams actually follow.
| Comparison | Convex | PlanetScale |
|---|---|---|
| Pricing shape | Free tier for small projects, then a per-developer plan with usage-based function calls and storage. | Paid plans priced by cluster size and usage, positioned at production workloads rather than hobby projects. |
| Frameworks | Next.js, React Native, Expo | Next.js, SvelteKit, Nuxt, Laravel, Rails, Django |
| In one line | A reactive backend where you write TypeScript functions instead of SQL, and queries push updates to clients automatically. | Managed MySQL built on Vitess, aimed at teams that expect serious scale and want schema changes without downtime. |
Pricing described qualitatively because published plans change often. Checked 2026-08-23. Confirm current terms on Convex and PlanetScale.
Convex
Strengths
- ✓Queries are reactive by default, so live-updating UI needs no subscription plumbing.
- ✓Backend functions are TypeScript sharing types with the frontend end to end.
- ✓Transactions and scheduling are built in, so background jobs need no extra service.
- ✓No separate API layer to write, since client calls functions directly.
Tradeoffs
- ✗Not SQL, so analytics tools and reporting stacks do not plug straight in.
- ✗The programming model is proprietary, making a later migration a rewrite.
- ✗Complex relational queries are more awkward than in a relational engine.
- ✗Smaller ecosystem, so fewer existing libraries assume Convex as the store.
PlanetScale
Strengths
- ✓Non-blocking schema changes reviewed like code before they hit production.
- ✓Vitess sharding means the growth path to very large tables already exists.
- ✓Deploy requests give schema migrations an approval workflow teams actually follow.
- ✓Insights surface slow queries without wiring up a separate monitoring stack.
Tradeoffs
- ✗Foreign key constraints were historically restricted, which shapes how you model data.
- ✗MySQL rather than Postgres, so Postgres-specific extensions and types are unavailable.
- ✗Priced for real workloads, so small projects subsidise capacity they never use.
- ✗The Vitess layer adds behaviour that differs subtly from stock MySQL.
Frequently asked questions
Is Convex or PlanetScale better?
Neither is better in the abstract. PlanetScale covers more of the ecosystem, so it survives a change of framework; Convex is the better fit while you stay where it is strongest. The wrong choice here is usually recoverable, so weight speed of decision over certainty.
What is the main drawback of Convex?
Not SQL, so analytics tools and reporting stacks do not plug straight in. The programming model is proprietary, making a later migration a rewrite.
What is the main drawback of PlanetScale?
Foreign key constraints were historically restricted, which shapes how you model data. MySQL rather than Postgres, so Postgres-specific extensions and types are unavailable.
Can you switch from one to the other later?
Usually, at a cost that grows with how much of your product leans on the database layer. Plan for it in the data model, not in the framework, and the switch stays survivable.