Patterns for database-per-tenant architectures with RivetKit. Instead of one shared database with a tenant_id column on every table, each tenant gets its own Rivet Actor, and that actor owns the tenant’s entire dataset.

Starter Code

Start with the working example on GitHub and adapt it. The example stores each tenant’s dataset in JSON actor state and serves a React dashboard with live event updates.

Topic	Summary
Isolation	One `companyDatabase` actor per tenant, keyed by company name. Switching tenants swaps the entire dataset.
State	JSON actor state holding `employees` and `projects` arrays plus timestamps. No SQLite, no queues, no scheduling.
Realtime	Every write action mutates state, then broadcasts a typed event (`employeeAdded`, `projectAdded`) to all connected clients of that tenant.
Auth	None. The sign-in screen is cosmetic. Production guidance is in the security checklist.

The Isolation Model

The actor key is the tenant id. The client connects with useActor({ name: "companyDatabase", key: [companyName] }) and the actor reads c.key[0] in createState to seed that tenant’s dataset. This gives you:

One actor per tenant: companyDatabase[tenantId] addresses exactly one actor instance. Two tenants can never share an actor.
One dataset per tenant: All reads and writes go through that actor’s state, so there is no shared table with a tenant_id column to filter incorrectly. Cross-tenant leaks require constructing the wrong key, not forgetting a WHERE clause.
No key injection: Keys are arrays, not interpolated strings. key: [tenantId] cannot be escaped the way "tenant:" + tenantId string concatenation can. See Keys.

The example’s test (tests/per-tenant-database.test.ts) proves the isolation: data written to companyDatabase["Alpha Co"] never appears in companyDatabase["Beta Co"].

Choosing a State Backend

The example uses plain JSON actor state. The same key-equals-tenant model works with any actor state backend.

Backend	Use When	Docs	Working Code
JSON actor state	Small datasets, simple reads, whole dataset fits comfortably in memory. What the example uses.	State	GitHub
Actor SQLite (`rivetkit/db`)	Tables, indexes, SQL queries, larger-than-memory data, per-tenant relational schema.	SQLite	GitHub
SQLite + Drizzle	Typed schema, query builder, and generated migration files on top of actor SQLite.	SQLite + Drizzle	GitHub

With either SQLite option, every tenant gets its own embedded SQLite database, since the database is scoped to the actor and the actor is scoped to the tenant.

Migrations

The per-tenant example has no migrations because JSON state has no schema. When you adopt SQLite, migrations run per tenant database:

Raw SQL: db({ onMigrate }) runs your migration SQL inside a SQLite savepoint before the actor serves traffic. If onMigrate throws, all migration SQL rolls back atomically and the actor does not start. See SQLite.
Drizzle: drizzle-kit generates migration files from your typed schema, and db({ schema, migrations }) applies them when the actor wakes. See SQLite + Drizzle.

Because each tenant has its own database, migrations roll out per actor as each tenant’s actor wakes, rather than as one large migration against a shared database.

Tenant Id Must Come From Auth

The example’s sign-in is cosmetic: the client picks any company string and that string becomes the actor key, so any visitor can read and write any tenant’s data. Do not ship this. As a required production extension (not implemented by the example):

Derive the tenant id from a verified credential, such as a JWT claim, never from user input.
Validate the credential against c.key in onBeforeConnect (pass/fail) or createConnState (store the verified user on connection state). See Authentication and Connections.
Add per-action permission checks on top of connection-level auth. See Access Control.

Actors

Every write action follows the same mutate-then-broadcast shape: push the record into c.state, bump updated_at, broadcast the typed event, return the record. See Actions and Events.

Lifecycle

sequenceDiagram participant A as Tenant A client participant DA as companyDatabase A participant B as Tenant B client participant DB as companyDatabase B Note over A: authenticate and derive tenant id A->>DA: connect with key [tenantA] Note over DA: createState seeds company_name, employees, projects A->>DA: listEmployees() + listProjects() + getStats() A->>DA: addEmployee(name, role) DA-->>A: employeeAdded event B->>DB: connect with key [tenantB] Note over DB: separate actor, separate dataset B->>DB: listEmployees() DB-->>B: tenant B data only

In the example, the “authenticate” step is a free-text company picker. The rest of the flow matches the diagram: createState seeds the dataset on first creation, the dashboard loads with listEmployees, listProjects, and getStats, and every connected client of the same tenant receives employeeAdded and projectAdded events.

Security Checklist

The example ships with none of these. Apply all of them before production.

Tenant identity: Derive the tenant id from a verified JWT claim, never from a client-supplied string.
Connection validation: In onBeforeConnect or createConnState, verify the credential’s tenant claim matches c.key and reject mismatches.
Per-action authorization: Check the caller’s role before mutating actions (addEmployee, addProject), not just at connect time. See Access Control.
Input validation: Clamp name and role lengths and validate enums. The example only trims input and substitutes fallback defaults.
Key construction: Always pass the tenant id as an array element (key: [tenantId]). Never interpolate tenant ids into key strings, and never build keys from one tenant’s input to address another tenant’s actor.
Growth limits: As a recommended extension, cap or paginate the employees and projects arrays. The example lets them grow unboundedly in JSON state; move to SQLite when the dataset outgrows memory.

Database per Tenant

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