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PlanetScale AI vs Neon AI

This comparison was retrieved by database IDs selected from the catalog.

PlanetScale AI favicon

PlanetScale AI

PlanetScale

Neon AI favicon

Neon AI

Neon

Overview
Rating
—
—
Category
Developer Tools
Developer Tools
Subcategory
AI Serverless MySQL Platform for Scale
AI Serverless PostgreSQL Platform
Company
PlanetScale
Neon
Status
Active
Active
Launch year
2023
2023
Tags
databasesaimysqlserverlessscalingdeveloper-tools
databasesaipostgresqlserverlessbranchingdeveloper-tools
Pricing
Starting price
FreeBest value
Free
Pricing model
Freemium
Freemium
Free plan
No
Yes
Free trial
✓
✕
Pricing notes

Free plan (discontinued 2024, Hobby $39/month). Scaler $39/month. Scaler Pro $99/month. Enterprise custom.

Free plan (0.5 GB storage). Launch $19/month. Scale $69/month. Business $700/month. Enterprise custom.

Capabilities
Best for

AI-optimised serverless MySQL for high-traffic applications

Non-blocking schema changes without downtime

Database branching for safe schema development workflow

Horizontal MySQL scaling with Vitess sharding

AI query performance analysis and optimisation

Serverless PostgreSQL for cloud-native developmentDatabase branching for preview environments and CI/CDAI-powered query optimisationAutoscaling database without idle compute costDeveloper-experience-first managed Postgres
Target audience
Backend and full-stack web developersDatabase architects at high-scale applicationsDevOps teams managing MySQL infrastructureEngineering teams needing schema change safetyStartups scaling from single MySQL to sharded architecture
Frontend and full-stack web developersStartups using Vercel or Netlify deploymentBackend developers building serverless applicationsDevOps teams managing developer databasesAI application developers needing pgvector
AI type
ML Platform
LLM Assistant
Modalities
DataCode
DataCode
Technical
Model provider
PlanetScale
Neon
Model names
—
—
API available
✕
✓
Open source
✕
✕
Deployment
SaaS
SaaS
Platforms
WebCLIAPI
WebCLIAPI
Integrations
GitHubVercelNetlifyPrismaAPI
VercelNetlifyGitHubPrismaDrizzleAPI
Team collaboration
✕
✕
Trust & security
Security

SOC 2 Type II. GDPR compliant. Enterprise data handling agreements.

SOC 2 Type II. GDPR compliant. Enterprise data handling agreements.

Privacy notes

Database data processed on PlanetScale cloud. Branching uses copy-on-write semantics for storage-efficient branch isolation.

PostgreSQL database data processed on Neon cloud. Branch databases use Copy-on-Write storage sharing unchanged data with parent branches.

Verdict
Pros
  • Square, GitHub, and Hubspot validate PlanetScale for high-scale web application MySQL
  • Non-blocking schema changes eliminate the database downtime that MySQL schema migrations traditionally require
  • AI query analysis and Boost caching reduce database load without manual query optimisation expertise
  • Vitess horizontal sharding provides the MySQL scaling path from single instance to billions of queries
  • Developer adoption and Vercel integration validate Neon as the serverless database for modern web development
  • Database Branching solves the longest-standing developer pain point — getting fresh database copies for development and testing
  • Scale-to-zero eliminates idle database compute cost for development and low-traffic applications
  • pgvector extension support enables AI vector similarity search on the same Postgres database
Cons
  • PlanetScale discontinued its free plan in 2024 — evaluate Neon (rank 886) or Supabase for free-tier database needs
  • Competing with Neon (rank 886) for serverless database market — Neon serves PostgreSQL users, PlanetScale serves MySQL users
  • Scaler at $39/month requires justification for applications not yet at scale requiring Vitess features
  • Business plan at $700/month is significant for teams needing production-grade features — evaluate tier requirements
  • Competing with PlanetScale, Supabase (covered), and traditional managed Postgres for developer database market
  • Scale-to-zero creates cold start on first query after idle — evaluate for latency-sensitive production workloads

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