Supabase vs PlanetScale for AI Startups

Choosing between Supabase vs PlanetScale is a meaningful decision for AI startups in 2026. Both are managed database platforms that eliminate the pain of running your own database infrastructure. But they're built on different foundations with different strengths — and those differences matter acutely when your product involves embeddings, semantic search, and the complex data relationships that come with AI-native applications.

This comparison focuses specifically on AI startup needs: vector search, data modeling for LLM workflows, scaling characteristics, and total cost of ownership.

The Foundations: Postgres vs MySQL

The most important difference isn't a feature — it's the database engine underneath.

Supabase is built on PostgreSQL. You get a real Postgres database with full SQL, triggers, stored procedures, row-level security, and extension support. The pgvector extension turns your Postgres database into a vector store — meaning you can store embeddings and run similarity search in the same database as your application data. No separate vector database required for most early and mid-scale use cases.

PlanetScale was originally built on Vitess, a MySQL-compatible sharding layer developed at YouTube. In 2025, PlanetScale introduced PlanetScale Metal (direct MySQL with no Vitess overhead) and PlanetScale Postgres (a managed Postgres offering). The MySQL lineage still dominates their ecosystem, documentation, and community, though the Postgres offering is growing.

For AI startups evaluating vector search capabilities today, Supabase's pgvector integration is more mature, better documented, and more widely adopted.

Vector Search: The AI Startup Deciding Factor

If your product involves RAG pipelines, semantic search, recommendation systems, or any feature that stores and retrieves embeddings, vector search capability is a first-class requirement. See our explainer on what is RAG for context on why this matters.

Supabase + pgvector:

Install with CREATE EXTENSION IF NOT EXISTS vector; — it's built into every Supabase project
Store embeddings as vector(1536) or other dimensions in standard Postgres columns
Run approximate nearest-neighbor search with HNSW or IVFFlat indexes
Combine vector similarity with SQL filters in a single query: "find the 5 most similar documents that belong to this user and were created in the last 30 days"
Scales to millions of vectors within pgvector; migrate to a dedicated vector DB (Pinecone, Weaviate) when you need billions

PlanetScale:

No native vector search in the MySQL/Vitess product
PlanetScale Postgres (their newer offering) supports pgvector, but it's a younger product with less community support than Supabase's Postgres
Teams building AI features on PlanetScale's MySQL product need to add a separate vector database — increasing infrastructure surface area

For an AI startup where vector search is a core feature, Supabase's mature pgvector integration is a genuine competitive advantage at early and mid scale.

Data Modeling for AI Workflows

AI applications have characteristic data patterns: users generate documents, documents get chunked, chunks get embedded, embeddings link back to chunks and documents, conversation turns reference multiple documents. These are relational data with foreign key constraints.

Supabase (Postgres): Handles this naturally. You model it as a relational schema, write JOINs, use foreign keys for integrity, and query across entities in a single statement. This isn't a workaround — it's what Postgres was built for.

PlanetScale (MySQL): Also relational, but historically PlanetScale's Vitess layer disabled foreign key constraints for sharding compatibility. In 2024 they re-introduced foreign keys. For AI data models that rely heavily on relational integrity, this history matters less today, but Postgres's SQL dialect remains more expressive for complex queries.

Developer Experience and Ecosystem

Supabase:

Generous free tier (500MB database, pgvector included)
Supabase Studio — a polished GUI for your database with real-time query editor
Built-in auth, storage, edge functions, and real-time subscriptions in one platform
Strong TypeScript SDK with auto-generated types from your schema
Excellent Next.js integration — a common choice for AI product frontends

PlanetScale:

Pioneered database branching: create a branch of your production database for feature development, merge the schema change via a merge request. This workflow is genuinely powerful for teams with frequent schema changes.
Non-blocking schema changes — ALTER TABLE doesn't lock rows in production
MySQL ecosystem: Prisma, Drizzle, TypeORM all support it well
Less "all-in-one" than Supabase — no built-in auth, storage, or realtime

Scaling Model

Supabase uses a traditional vertical + read-replica scaling model. You scale up your Postgres instance, add read replicas for read-heavy workloads, and eventually migrate to larger tiers. For most AI startups, this is more than enough — Supabase Pro plans handle significant scale before you need to think about sharding.

PlanetScale was originally designed for horizontal sharding at YouTube scale. For startups that anticipate extreme write throughput (think: millions of writes per second), PlanetScale's Vitess lineage offers a path. But for the vast majority of AI startups, this level of scale is not the bottleneck — the AI inference costs and latency are.

The branching workflow remains PlanetScale's strongest differentiator for teams doing rapid schema iteration. If your team ships schema changes multiple times per week, the branch-and-merge workflow reduces deployment risk meaningfully.

Pricing Comparison (2026)

| Tier | Supabase | PlanetScale | |---|---|---| | Free | 500MB DB, pgvector, auth, storage | 5GB storage (Hobby — limited) | | Entry paid | $25/month (Pro) — 8GB DB | $39/month (Scaler) | | Compute | Separate compute add-ons | Included in plan | | Egress | $0.09/GB after free | $0.10/GB |

Supabase's free tier is more generous for AI startups getting started — pgvector is included at no extra cost, and the all-in-one platform means you're not paying separately for auth and storage.

When to Choose Supabase

Your product needs vector search now or in the near future
You want a single platform for database, auth, storage, and real-time
Your team is comfortable with Postgres SQL
You're building with Next.js or another JavaScript framework with strong Supabase SDK support
You want to avoid maintaining a separate vector database at early scale

When to Choose PlanetScale

Your team has deep MySQL expertise and existing tooling
Schema branching and non-blocking migrations are critical to your deployment workflow
You're building on MySQL and don't need vector search as a core feature
You anticipate extreme write-scale requirements and want Vitess's horizontal sharding

The Verdict for AI Startups

For AI-native startups in 2026, Supabase is the stronger default choice. The pgvector integration alone eliminates an entire infrastructure component for products that need semantic search. The all-in-one platform reduces the number of services you manage. And Postgres's SQL expressiveness handles AI data models more naturally than MySQL alternatives.

PlanetScale is an excellent database platform — but its strongest features (branching, non-blocking migrations, Vitess-scale horizontal sharding) are solving problems that most AI startups won't hit in the first two years. Vector search is a problem you'll hit in month one.

We covered the broader Supabase ecosystem in our Firebase vs Supabase comparison if you're also evaluating Firebase as an option.

Not sure which database fits your AI product? Our engineers have shipped on both. Book a 15-min scope call →

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Supabase vs PlanetScale for AI Startups