PlanetScale vs Neon vs Turso
Comparison of three serverless Postgres/SQLite platforms: PlanetScale (MySQL Vitess), Neon (Postgres with branching), Turso (libSQL/SQLite at the edge).
Why
These 'serverless database' platforms offer a connection string, branching/preview DBs, and scale-to-zero pricing. PlanetScale is MySQL (Vitess, no FK constraints); Neon is full Postgres with copy-on-write branches; Turso is SQLite-based (libSQL) with replicas at the edge for ultra-low latency reads. the cloud infrastructure market reached $330B in 2024, growing 22% year-over-year (Synergy Research).
The top 3 providers (AWS, Azure, GCP) account for 68% of public cloud, but tier-2 providers (Cloudflare, DigitalOcean, Railway) win on developer experience and price transparency.
When
Use Neon for full Postgres features (FK, extensions, triggers) and branching. Use PlanetScale for MySQL workloads or extreme write scale (Vitess shards).
Use Turso for read-heavy edge apps (SQLite replicas near users) and embedded/local-first apps. All have a free tier.
How
Example
Common Problems
- ⚠PlanetScale has no foreign key constraints (Vitess limitation) — enforce referential integrity in app code or use Neon (full Postgres) if you need FKs.
- ⚠Neon compute autosuspend causes cold starts — the free tier suspends after idle; use a connection pooler (PgBouncer) and accept ~300ms cold start, or keep it warm with a cron ping.
- ⚠Turso SQLite lacks some Postgres features (stored procedures, advanced types) — if you need them, use Neon; Turso is best for simple schemas and read-heavy edge.
Best Practices
- ✓Use database branching (Neon/PlanetScale) for preview environments — each PR gets an isolated DB with the production schema, merged on merge.
- ✓For Turso, create replicas in your top user regions so reads hit a local SQLite (sub-10ms); writes go to the primary and sync back.
- ✓Use a connection pooler (PgBouncer for Neon, Hyperdrive for PlanetScale) to avoid exhausting connections from serverless functions.
FAQ
What is PlanetScale vs Neon vs Turso and what problem does it solve?
Comparison of three serverless Postgres/SQLite platforms: PlanetScale (MySQL Vitess), Neon (Postgres with branching), Turso (libSQL/SQLite at the edge). It lets developers deploy, store, and scale without self-managing infrastructure — a core reference for cloud service selection.
How do I get started with PlanetScale vs Neon vs Turso in 5 minutes?
Two steps to run it: (1) Create a database on the platform's CLI or web.; (2) Get a connection string (Neon/Turso) or a branch + connection (PlanetScale).. Validate on the free tier first, then wire into production.
What is the #1 pitfall with PlanetScale vs Neon vs Turso?
The most frequent issue is: PlanetScale has no foreign key constraints (Vitess limitation) — enforce referential integrity in app code or use Neon (full Postgres) if you need FKs.. Run a pre-launch checklist item by item, and add policy/config scanning in CI.
What are the best practices for PlanetScale vs Neon vs Turso?
Best practices for PlanetScale vs Neon vs Turso include: Use database branching (Neon/PlanetScale) for preview environments — each PR gets an isolated DB with the production schema, merged on merge.; For Turso, create replicas in your top user regions so reads hit a local SQLite (sub-10ms); writes go to the primary and sync back.; Use a connection pooler (PgBouncer for Neon, Hyperdrive for PlanetScale) to avoid exhausting connections from serverless functions.. The first one — "Use database branching (Neon/PlanetScale) for preview environments — each PR gets an isolated DB with the production schema, merged on merge." — should be a team default baked into your IaC templates.
How do I control cost and avoid bill surprises with PlanetScale vs Neon vs Turso?
Three ways to prevent overspend: (1) tag every resource with env/team and enable billing alarms; (2) set budget thresholds to trigger Slack/email alerts; (3) review idle resources in Cost Explorer regularly and clean them up. Most PlanetScale vs Neon vs Turso options offer a free tier — validate within free quota before scaling.