NeonDB vs PlanetScale: Choosing Your Serverless Postgres Database in 2025

The serverless database landscape has evolved dramatically, and two strong contenders now compete head-to-head in the Postgres space: Neon and PlanetScale. With PlanetScale’s recent expansion from MySQL-only to supporting Postgres (announced July 2025, GA in September 2025), developers finally have two serious options for managed serverless Postgres. Here’s how they stack up.

The Big Picture

Both databases target developers building modern applications, but they approach the problem differently:

Neon pioneered serverless Postgres with its innovative separation of compute and storage, enabling true scale-to-zero capabilities. Now owned by Databricks (acquired May 2025 for ~$1B), Neon powers the new “Lakebase” offering while continuing to operate independently.
PlanetScale built its reputation on MySQL with Vitess (the battle-tested technology behind YouTube), and has now brought that operational excellence to Postgres with their proprietary operator and Metal infrastructure.

Scale-to-Zero vs Always-On

This is perhaps the most significant architectural difference between the two.

Neon scales down to zero when idle (after 5 minutes of inactivity by default). Your database literally stops running when not in use, and you pay nothing for compute during idle periods. This is excellent for development environments, staging branches, and applications with intermittent traffic. The tradeoff? Cold starts. When your first request hits after idle time, there’s a delay while compute spins back up—typically hundreds of milliseconds to a few seconds.

PlanetScale databases are always-on by design. Your compute is running 24/7, which means zero cold starts and consistent latency for every request. For production workloads where every millisecond matters, this is a significant advantage. The tradeoff is cost—you’re paying even when traffic is minimal.

	Neon	PlanetScale
Idle behavior	Scales to zero	Always running
Cold starts	Yes (hundreds of ms)	None
Best for	Variable/intermittent traffic	Consistent production loads

Regional Availability

Neon operates as a single-region database. Your data lives in one region, and that’s where your queries run. While this simplifies the architecture and keeps costs down, it means users far from your chosen region will experience higher latency.

PlanetScale supports read replicas in different geographic zones. You can deploy your primary in one region and add replicas closer to your users worldwide. For applications serving a global audience, this can dramatically improve read performance. PlanetScale’s architecture (inherited from Vitess) was designed for planet-scale distribution—it’s in the name.

Database Engine Heritage

Neon is Postgres-only, and that’s intentional. They’ve rebuilt the storage layer from scratch while keeping the compute layer as close to vanilla Postgres as possible. This means excellent extension compatibility, familiar tooling, and straightforward migration from existing Postgres databases.

PlanetScale started as a MySQL platform powered by Vitess and expanded to Postgres in 2025. Their MySQL offering has some notable constraints (no foreign keys due to Vitess limitations, for example), but their Postgres implementation runs “real Postgres” on their proprietary operator—not Vitess. They now support both MySQL and Postgres, giving teams flexibility if they need both.

Pricing Philosophy

Neon uses usage-based pricing with compute measured in CU-hours (Compute Unit hours, where 1 CU ≈ 4GB RAM). Since August 2025, they’ve moved to fully usage-based paid plans with a $5/month minimum on Launch tier. The free tier is generous—100 CU-hours/month (doubled from 50 in October 2025) and 0.5GB storage per project. Post-Databricks acquisition, they’ve dropped compute prices by 15-25%.

PlanetScale uses a more traditional pricing model based on instance size. Single-node databases start at $5/month, and highly-available 3-node clusters start at $30/month. They also offer Metal clusters with NVMe storage starting at $50/month for maximum performance. No free tier currently exists (they sunset it in 2024).

	Neon	PlanetScale
Minimum cost	$0 (free tier)	$5/month (single node)
HA production	Usage-based	$30/month (3-node)
Pricing model	Usage-based (CU-hours)	Instance-based

Developer Experience & Branching

Both platforms offer database branching, but the implementations differ significantly.

Neon’s branching is instant and storage-efficient thanks to copy-on-write (CoW) at the storage layer. Creating a branch doesn’t duplicate data—it’s virtually free until the branch diverges. This makes it practical to create a branch for every pull request, every developer, or every test run. Branches can also access point-in-time snapshots of your data.

PlanetScale’s branching comes from their MySQL heritage with deploy requests and schema change workflows. For Postgres, they’ve brought similar capabilities with development branches ($5/month each). Their branching emphasizes the schema migration workflow—create a branch, make changes, create a deploy request, and merge safely.

Performance & Architecture

Neon separates compute from storage, using a custom multi-tenant storage system (Safekeepers, Pageservers, and cloud object storage). This enables their serverless capabilities but adds a network hop between compute and storage. They’ve optimized heavily to minimize this impact.

PlanetScale offers Metal instances with locally-attached NVMe drives for workloads where latency is critical. They claim to consistently outperform competitors in benchmarks, even giving competitors 2x the resources. Their proxy layer provides connection pooling (PgBouncer-compatible) and query buffering.

Observability & Tooling

Neon provides monitoring dashboards, metrics (RAM, CPU, connections, database size), and on Scale tier, export to Datadog or any OTel-compatible platform. The focus is on serverless simplicity.

PlanetScale emphasizes operational excellence with Query Insights—detailed query-level analytics, p99 metrics, schema recommendations, and performance monitoring. Teams migrating from Neon have specifically cited this as a major upgrade for debugging and optimization.

Ecosystem Integration

Neon now integrates deeply with Databricks’ ecosystem (Unity Catalog, Lakebase), enabling seamless data sync between operational Postgres and the data lakehouse. This is compelling for teams already in the Databricks ecosystem or building AI/ML applications.

PlanetScale offers CLI tools (pscale), integrations with CI/CD pipelines, and straightforward migration tools. Their focus is on being the best database platform rather than building a broader ecosystem.

When to Choose Neon

You have variable or unpredictable traffic patterns
Cost optimization is critical (pay for what you use)
You’re building development/staging environments at scale
You want instant, cheap database branching for every PR
You’re in the Databricks ecosystem or building AI applications
You need a generous free tier for side projects

When to Choose PlanetScale

You need consistent, predictable latency (no cold starts)
Your application serves global users (read replicas)
You want detailed query insights and performance monitoring
You’re running high-throughput production workloads
You need the flexibility to use MySQL and Postgres
You prioritize proven operational reliability

The Bottom Line

Both Neon and PlanetScale are excellent choices for serverless Postgres in 2025. The decision often comes down to your traffic patterns and latency requirements:

Choose Neon if your workload has quiet periods and you want to minimize costs during idle time, accepting some cold start latency.
Choose PlanetScale if consistent performance is non-negotiable and you’re willing to pay for always-on compute.

For many teams, the best approach might be both: Neon for development and staging environments (leveraging free tier and cheap branching), and PlanetScale for production (leveraging always-on performance and query insights).

The serverless Postgres wars are just beginning, and developers are the winners.