DB9 vs PlanetScale
DB9 and PlanetScale are both managed PostgreSQL-compatible databases, but they solve different problems. PlanetScale optimizes for operational reliability — high availability, read replicas, and connection pooling. db9 database optimizes for instant provisioning, AI agent integration, and built-in application-layer extensions.
This page is for developers evaluating both platforms. It covers the key differences honestly so you can choose the right one.
At a Glance
Section titled “At a Glance”| Capability | DB9 | PlanetScale Postgres |
|---|---|---|
| PostgreSQL version | Custom engine (pgwire v3) | Standard PostgreSQL v17 |
| Storage engine | TiKV (distributed KV) | EBS or NVMe Metal |
| Provisioning | Synchronous, under 1 second | Not documented |
| Scale-to-zero | No (always on) | No (always on) |
| High availability | No | Yes (1 primary + 2 replicas, 3 AZs) |
| Connection pooling | No built-in pooler | Yes (PgBouncer add-on) |
| Read replicas | No | Yes (add-on) |
| Branching | Full data copy, async | Schema-only copy from backup |
| Free tier | Yes (no signup, up to 5 databases) | No (14-day trial, $5/mo minimum) |
| Native embeddings | Yes (built-in embedding() function) | No |
| Document chunking | Yes (built-in CHUNK_TEXT() for RAG) | No |
| HTTP from SQL | Yes (built-in http extension — GET, POST, PUT, DELETE, PATCH, HEAD) | No |
| File system in SQL | Yes (built-in fs9 extension) | No |
| Parquet import | Yes (built-in read_parquet() + COPY FORMAT parquet) | No |
| Agent onboarding | Yes (db9 onboard for Claude, Codex, etc.) | No (MCP server available) |
| MCP integration | No | Yes (16 tools, hosted) |
| Extensions | 9 built-in | Curated (vectorscale, TimescaleDB, pg_strict) |
| LISTEN/NOTIFY | No | Yes (standard PostgreSQL) |
| Transaction isolation | REPEATABLE READ (SERIALIZABLE not supported — returns error) | Full SERIALIZABLE |
When to Choose DB9
Section titled “When to Choose DB9”Instant database provisioning at scale. DB9 creates databases synchronously in under a second. This makes it practical to provision a database per user, per agent task, or per CI run without waiting. The SDK’s instantDatabase() creates and returns a ready-to-use connection in a single call.
AI agent workflows. DB9 is designed for AI agents. The db9 onboard CLI installs skills for Claude Code, OpenAI Codex, Opencode, and other agents. Anonymous accounts let agents create databases without signup flows. Built-in embedding() generates vector embeddings from text directly in SQL — no external embedding API needed.
Application-layer extensions. DB9 includes extensions that run application logic inside the database:
http— make HTTP requests from SQL (GET, POST, PUT, DELETE, PATCH, HEAD)fs9— read and write files from SQL, query file contents as tables; FUSE mount for local filesystem accessembedding()— generate text embeddings natively (default model: text-embedding-v4, 1024 dimensions)CHUNK_TEXT()— split documents into overlapping chunks for RAG pipelinesread_parquet()— import Parquet files directly in SQLpg_cron— schedule recurring SQL jobs- Full-text search with Chinese tokenizer support (jieba/zhparser)
These are compiled into DB9 and available without installation. PlanetScale does not offer equivalent built-in HTTP, file system, native embedding, document chunking, or Parquet import capabilities.
No-signup developer experience. Developers can install the CLI and create a database without creating an account. Anonymous accounts support up to 5 databases and can be upgraded later with db9 claim. PlanetScale requires a paid account ($5/mo minimum) after a 14-day trial — there is no permanent free tier.
When to Choose PlanetScale
Section titled “When to Choose PlanetScale”Production workloads requiring high availability. PlanetScale deploys every database with one primary and two replicas across three availability zones by default. Automatic failover handles node failures without application changes. DB9 does not offer built-in multi-AZ replication or automatic failover.
Read-heavy workloads. PlanetScale supports read replicas as an add-on, letting you scale read capacity independently. DB9 does not support read replicas.
Connection-heavy applications. PlanetScale offers a PgBouncer-based connection pooling add-on for applications that open many short-lived connections (serverless functions, high-concurrency web apps). DB9 does not include a built-in connection pooler.
MCP-first agent integration. PlanetScale provides a hosted MCP server with 16 tools for database management, schema exploration, and query execution. If your agent workflow is built around MCP rather than CLI onboarding, PlanetScale has a more mature MCP integration. DB9 focuses on CLI-based agent onboarding via db9 onboard.
Standard PostgreSQL compatibility. PlanetScale runs standard PostgreSQL v17 with full support for LISTEN/NOTIFY, SERIALIZABLE isolation, and a curated set of extensions including vectorscale, TimescaleDB, and pg_strict. DB9 does not support LISTEN/NOTIFY or SERIALIZABLE isolation, and its extension set is limited to 9 built-in options.
Architecture Differences
Section titled “Architecture Differences”Storage
Section titled “Storage”DB9 uses TiKV, a distributed key-value store, as its storage engine. Each database gets an isolated TiKV keyspace. This architecture enables instant provisioning (creating a keyspace is fast) but means branching requires a full data copy.
PlanetScale uses either EBS or NVMe Metal storage depending on the plan. Storage is tightly coupled to compute nodes. The three-node default deployment (one primary, two replicas) provides durability through synchronous replication across availability zones.
Compute
Section titled “Compute”DB9 runs a custom SQL engine that parses, optimizes, and executes queries against TiKV. Each database has dedicated compute that is always running. There is no autoscaling or scale-to-zero.
PlanetScale runs standard PostgreSQL v17. Each database has fixed compute with always-on nodes. There is no autoscaling or scale-to-zero. The focus is on operational reliability through multi-AZ replication rather than elastic scaling.
Branching
Section titled “Branching”DB9 branches by copying all data from the parent database into a new TiKV keyspace. This is asynchronous — state progresses from CLONING to ACTIVE. Maximum 2 concurrent branch creations. Each branch is a fully independent database with a complete data copy.
PlanetScale branches by creating a schema-only copy from a backup of the source database. Data is not included — the branch starts with an empty dataset matching the source schema. This is lightweight but means branches cannot be used for data-inclusive testing without additional data loading.
Extension Comparison
Section titled “Extension Comparison”| Extension / Capability | DB9 | PlanetScale Postgres |
|---|---|---|
| pgvector (vector type + HNSW) | Built-in | Available (via vectorscale) |
| Native embedding generation | Built-in (embedding()) | Not available |
| Document chunking for RAG | Built-in (CHUNK_TEXT()) | Not available |
| HTTP requests from SQL | Built-in (http — 6 methods) | Not available |
| File system access from SQL | Built-in (fs9 extension) | Not available |
| FUSE filesystem mount | Built-in (db9 fs mount) | Not available |
| Parquet import | Built-in (read_parquet() + COPY) | Not available |
| Full-text search (Chinese/jieba) | Built-in (zhparser extension) | Standard PostgreSQL FTS |
| pg_cron (job scheduling) | Built-in | Not documented |
| TimescaleDB | Not available | Available |
| vectorscale | Not available | Available |
| pg_strict | Not available | Available |
| Custom / community extensions | Not supported | Curated set supported |
ORM and Driver Compatibility
Section titled “ORM and Driver Compatibility”Both platforms support standard PostgreSQL ORMs and drivers. DB9 has tested compatibility with:
| ORM / Driver | DB9 Test Results | PlanetScale Postgres |
|---|---|---|
| Prisma | 89/89 (100%) | Supported |
| Drizzle | 75/75 (100%) | Supported |
| SQLAlchemy | Tested (smoke + e2e) | Supported |
| TypeORM | 147/150 (98%) | Supported |
| Sequelize | 87/87 (100%) | Supported |
| Knex | 97/97 (100%) | Supported |
| GORM | Tested (smoke + e2e) | Supported |
Both platforms use pgwire v3 and work with any PostgreSQL-compatible driver (node-postgres, psycopg, pgx, JDBC).
Migration Between Platforms
Section titled “Migration Between Platforms”Moving from PlanetScale to DB9 or vice versa uses standard pg_dump / psql workflows. See the Migrate from PlanetScale guide for step-by-step instructions.
Key migration considerations:
- PlanetScale runs standard PostgreSQL v17; DB9 runs a custom engine — test query compatibility before migrating
- DB9 does not support LISTEN/NOTIFY or SERIALIZABLE isolation
- DB9 connection strings use a different format (
tenant_id.role@host:5433/postgres) - Extensions not in DB9’s built-in set will not be available
- PlanetScale does not include DB9’s application-layer extensions (HTTP, file system, native embeddings)
Summary
Section titled “Summary”DB9 is the better choice when you need instant database provisioning, AI agent integration, or application-layer SQL extensions (HTTP, files, native embeddings). It excels at multi-tenant patterns where each user or agent gets their own database, and its free anonymous tier removes friction for prototyping.
PlanetScale is the better choice when you need production-grade high availability, read replicas, connection pooling, or full PostgreSQL feature compatibility. It excels at operational reliability with its three-AZ default deployment and standard PostgreSQL v17 engine.
Both platforms are PostgreSQL-compatible and work with standard ORMs, drivers, and SQL. Neither supports scale-to-zero. The right choice depends on whether your priority is developer experience and AI-native features (DB9) or operational reliability and PostgreSQL completeness (PlanetScale).
Next Pages
Section titled “Next Pages”- DB9 vs Neon — comparison with Neon
- DB9 vs Supabase — comparison with Supabase
- Compatibility Matrix — detailed feature support
- Overview — what DB9 is and who it is for
- Why DB9 for AI Agents — the agent-native positioning