Neon vs PlanetScale: Serverless SQL Compared (2026)

Neon and PlanetScale both sell "serverless SQL," but they solve different problems. Neon is serverless Postgres with scale-to-zero and copy-on-write branching. PlanetScale is MySQL on Vitess, engineered for massive horizontal scale with a schema-change workflow borrowed from GitHub's internal tooling. Picking between them is less about benchmarks and more about which SQL dialect you want and which failure mode you can live with.

This piece is part of our serverless and edge databases guide. If you want a wider field, see best serverless databases.

The core split #

Neon separates storage from compute. Your data lives in a multi-tenant storage layer, and compute nodes spin up on demand to serve queries. When traffic stops, compute scales to zero and you stop paying for it. Branching is copy-on-write, so a new branch of a production-sized database is created almost instantly without copying bytes.

PlanetScale takes a different bet. It runs MySQL behind Vitess, the sharding and connection-management layer that kept YouTube's database alive. There is no scale-to-zero. Your cluster stays warm and ready, and the design goal is handling enormous read and write volume across shards. Schema changes flow through a deploy-request workflow that applies online DDL without locking tables.

So the one-line summary: Neon optimizes for developer velocity and cost efficiency at low-to-medium scale. PlanetScale optimizes for staying up and fast when your MySQL workload gets genuinely large.

SQL flavor and what it implies #

This is the decision most teams skip, then regret. Neon is Postgres, so you get JSONB, rich extensions like PostGIS and pgvector, array types, and a stricter, more standards-oriented planner. PlanetScale is MySQL, which is fine but different: no CHECK constraints historically enforced the same way, different JSON handling, and a large ecosystem of MySQL-native ORMs and tools.

If your team already runs Postgres or wants pgvector for AI features, Neon removes friction. If you have a MySQL-shaped app or team muscle memory, PlanetScale meets you where you are. Do not treat the dialect as interchangeable. Migrating queries, stored procedures, and type assumptions between the two is real work.

Scaling model #

Neon's autoscaling adjusts compute up and down based on load, and drops to zero when idle. That makes it excellent for preview environments, internal tools, and spiky traffic where you would otherwise pay for idle capacity.

PlanetScale scales horizontally through Vitess sharding. When a single MySQL instance can no longer hold your write volume, you shard, and Vitess routes queries across the shards. This is the model you reach for when you are past the point where vertical scaling or read replicas cut it. The tradeoff is that you keep capacity warm and pay for it continuously.

Branching and the dev workflow #

Both offer database branching, but the workflows differ.

Neon gives you instant branches. Fork production data into an isolated branch, run migrations or tests against it, then throw it away. Because branches are copy-on-write, this is cheap and fast, which makes per-pull-request database environments practical.

PlanetScale pairs branching with deploy requests. You create a branch, make schema changes, then open a deploy request that PlanetScale applies to production using online schema change, meaning no table locks and no downtime during the migration. For teams that ship frequent schema changes to a busy database, this workflow is a genuine feature, not a nicety.

yaml.yaml

# Illustrative CI step. Verify current CLI flags before adopting.
deploy-schema:
steps:
- run: pscale branch create app add-orders-index
- run: pscale deploy-request create app add-orders-index
# Review, then apply with zero-downtime online DDL
- run: pscale deploy-request deploy app 42

Cold starts and connection handling #

Neon can cold-start from zero. If a compute node has scaled down, the first query after idle pays a startup cost, usually well under a second but worth knowing for latency-sensitive paths. PlanetScale stays warm, so there is no cold-start penalty by design.

Serverless functions open and close connections constantly, which overwhelms traditional connection limits. Both vendors address this. Neon ships a pooled connection endpoint and a serverless driver that speaks over HTTP/WebSocket, ideal for edge runtimes. PlanetScale offers a serverless driver and built-in connection pooling through Vitess.

javascript.javascript

// Neon serverless driver over HTTP, works in edge runtimes.
import { neon } from '@neondatabase/serverless';
const sql = neon(process.env.DATABASE_URL);
const rows = await sql`select id, email from users where id = ${userId}`;

Pricing models #

Neon bills largely on compute time and storage, and scale-to-zero means idle projects cost close to nothing. That is attractive for many small databases, staging, and hobby projects. PlanetScale prices around warm capacity, rows read/written, and storage, reflecting an always-on cluster. Both have moved plan structures more than once, so verify current: check the live pricing pages and model your actual query volume before committing.

Foreign keys and online DDL #

Historically Vitess did not support foreign key constraints across shards, so PlanetScale users often enforced referential integrity in the application. This has evolved, so verify current support for your sharding setup. Neon, being standard Postgres, gives you foreign keys with no caveats.

On DDL, PlanetScale's online schema change is a headline strength: altering a large table without locking it is exactly what you want on a hot production database. Neon runs standard Postgres DDL, and for zero-downtime migrations on large tables you would branch, test, and apply carefully, or reach for tooling like pg_repack.

Lock-in and migration #

Neon is Postgres wire-compatible, so moving to another Postgres host is comparatively low-friction, though you would give up branching and scale-to-zero. PlanetScale is MySQL wire-compatible, so you can move to another MySQL host, but you lose Vitess sharding and the deploy-request workflow, which may be load-bearing for you. In both cases the platform features, not the SQL, are the sticky part.

How to choose #

Answer these in order:

Postgres or MySQL: This alone often decides it. Existing stack and team skills win.

Scale shape: Spiky, many small databases, or cost-sensitive dev environments favor Neon's scale-to-zero. Sustained, very high write volume that needs sharding favors PlanetScale.

Schema-change cadence: If you ship frequent migrations to a busy production database, PlanetScale's deploy requests are a strong pull.

Edge and serverless: Both work, but Neon's HTTP driver and instant branching fit preview-per-PR workflows especially well.

AI features: Need pgvector in the same database as your app data? That points at Neon.

The call we'd make #

For most greenfield apps that want Postgres, cheap dev environments, and branch-per-pull-request workflows, we would reach for Neon. Scale-to-zero and instant branching genuinely change how a small team works, and Postgres compatibility keeps the exit door open.

When the workload is MySQL, the write volume is large or clearly heading there, and zero-downtime schema changes on a hot table are a recurring need, PlanetScale is the more serious choice. Vitess is proven at a scale most teams never reach, and the deploy-request workflow removes a real category of production risk.

Put simply: Postgres plus scale-to-zero plus branching for dev points to Neon. MySQL plus extreme scale plus zero-downtime schema changes points to PlanetScale. Whichever you pick, verify current pricing and feature specifics before you commit, because both platforms move fast.