drizzle-seed Alternative: Seedfast vs drizzle-seed

If you already use Drizzle ORM, drizzle-seed is the obvious first answer for filling a database with test data, and for a lot of cases it's the right one. It's the Drizzle team's own seeding package, it's free and open source, and it generates deterministic data straight from your Drizzle schema. So a drizzle-seed alternative is worth looking at only when you hit one of its edges: it works only on a Drizzle schema, its text columns get lorem-ipsum rather than meaningful values, its generator catalog is fixed, and circular foreign keys are a documented sore spot.

This is the head-to-head for the developer who started with drizzle-seed, likes generating from the schema, and is now asking a narrower question: can I get realistic, meaningful data across a database that isn't only a Drizzle project, with one command in CI and an integration my AI coding agent can call? That's where Seedfast and drizzle-seed diverge.

Both read a schema and generate relational data from it, so neither is "fake data" in the throwaway sense. drizzle-seed reads the schema from your Drizzle schema object and is a free, deterministic library you call from a TypeScript script. Seedfast reads the schema from the live Postgres database on every run, generates semantically realistic data from a natural-language scope, handles the foreign-key graph including nullable circular references, runs as one CLI command in CI, and exposes the same run as an MCP tool for AI coding agents. drizzle-seed is the better fit when you're inside a Drizzle project and want free, reproducible fixtures. Seedfast is the better fit when the data has to look real, the database isn't only a Drizzle app, or the seed has to run in a pipeline.

drizzle-seed does four things genuinely well: it's first-party and free, it generates straight from your Drizzle schema, it's deterministic, and it ships a catalog of real-data generators. A comparison that pretends the competitor is bad isn't worth reading, so if you're on Drizzle, reach for it first.

• It's first-party and free. drizzle-seed is maintained by the Drizzle team and published under Apache-2.0, so there's no cost and no third-party risk — it tracks Drizzle ORM directly (it requires a recent drizzle-orm).
• It generates straight from your schema. Point it at your Drizzle schema and call seed(db, schema); it picks default generators per column type and inserts rows with no configuration. By default it creates a small batch per table, and you raise that with a count.
• It's deterministic. drizzle-seed uses a seedable pseudo-random generator, so a fixed seed number reproduces the same rows on every run. That's the property you want for unit-test fixtures and bug repros, and it's something a model-driven generator doesn't give you for free.
• It has a solid built-in catalog. Generators like firstName, lastName, email, companyName, city, and valuesFromArray draw from real datasets, so names and places look plausible, and refine lets you override generators or set per-table counts.

If your project is a Drizzle app and you want free, reproducible fixtures with believable names, drizzle-seed already does the job. The rest of this page is about the cases it isn't built for.

Here's the concrete difference. drizzle-seed runs inside your TypeScript, against a Drizzle schema you import:

import { seed } from "drizzle-seed";
import { db } from "./db";
import * as schema from "./schema";

await seed(db, schema).refine((f) => ({
  users: { count: 100 },
}));

Seedfast runs as a command against a Postgres connection string. It reads the real schema and foreign-key graph and seeds the database in one step, with no schema object to import:

seedfast seed --scope "fintech app with 100 accounts, transactions, and varied balances"
  → Connected to PostgreSQL
  → Found 34 tables, 67 foreign keys
  → Generating data...
  → Done. Seeded 12,847 rows in 6.3s

The drizzle-seed call is part of your app's code and needs the Drizzle schema to exist. The Seedfast command needs only a connection string, so it works on any Postgres database — a Drizzle app, a Prisma app, a plain SQL schema, Supabase, Neon, or RDS — and it re-reads the live schema every run, so a new migration is picked up with nothing to reconfigure. The same invocation is exposed as an MCP tool, so an AI coding agent can run the seed directly.

The rows are workflow steps, not capability stars. The first row is a tie — both generate from a schema. The rows below it are where the two diverge: where the schema comes from, how realistic the values are, how cycles are handled, and how it runs in a pipeline.

Two rows are bolded because they're the honest concessions: drizzle-seed also seeds MySQL and SQLite, which Seedfast doesn't, and it's free and open source where Seedfast is a paid product. The rest of the table is where Seedfast pulls ahead: it reads any live schema, generates semantic data, resolves nullable cycles, and seeds in one CI command — the things a Drizzle-only library doesn't do.

The biggest structural difference isn't a feature, it's the coupling. drizzle-seed is an ORM-coupled seeder: it takes a Drizzle db instance and a Drizzle schema, and it has nothing to read without them. That's fine when the whole project is Drizzle. It stops being fine the moment the database isn't — a service written against Prisma or node-postgres, a schema managed by raw SQL migrations, or a database you don't own the ORM layer for.

Seedfast is database-coupled instead. It connects to Postgres and reads the live schema directly, so the ORM (or absence of one) doesn't matter. That also means it sees the schema as the database actually is after the last migration, not as a TypeScript object that may have drifted from it. If you want the broader picture of how seeders differ on this axis, the database seeder comparison ranks the approaches, and the best Postgres test data generator guide covers the field beyond these two.

drizzle-seed's people-and-places generators are good, but its catalog is fixed: refine only swaps in other built-in generators, so there's no API to register a custom one of your own. Columns it doesn't have a semantic generator for — descriptions, notes, product copy, domain-specific identifiers — fall back to lorem-ipsum text, random strings, or numeric filler. The data is structurally valid and reproducible, but a product_description reads as Latin filler and a free-text account_status column holds whatever the column type allows rather than a real status value.

Seedfast generates from a natural-language scope instead of a fixed catalog. You describe the data — "fintech app with accounts, transactions, and varied balances" — and it produces values that fit the columns' meaning, not just their types. That's the difference between data that compiles and data that looks like your product, which matters when you're demoing, screenshotting, or testing logic that branches on real-looking values. The generate test data with AI guide goes deeper on that workflow.

drizzle-seed's own documentation states it can't reliably infer references between tables when circular dependencies exist, and a reported issue (drizzle-orm #3635) documents the seeder looping on cyclical relations (as of June 2026). The workaround is manual: pick the related table by hand and break the cycle yourself.

Circular foreign keys — a users.default_org_id that points at orgs while orgs.owner_id points back at users — are exactly the shape that makes insertion order hard, because neither table can be inserted first without the other's rows. When one side of the cycle is nullable, Seedfast resolves the insert order automatically with a two-phase write: it inserts the parent with the back-reference left null, inserts the child, then fills the reference in a second pass, so the circular foreign key case seeds without hand-written SQL. When both sides are strictly NOT NULL with no deferral, no seeder can insert the cycle — that's true of drizzle-seed and Seedfast alike, and the deferred-constraint pattern stays the answer. If your schema has any nullable back-references, this is the row that decides it.

Run your first seed — 30-day free trial, about two minutes.

This one cuts the other way, and it's worth being straight about. drizzle-seed's determinism is a real advantage for one job: unit-test fixtures where you want the same rows every run so assertions are stable. A fixed seed number gives you that for free, and Seedfast doesn't try to be a bit-for-bit reproducible fixture generator.

Seedfast is built for the adjacent job: seeding a database with realistic, referentially-correct data for development, demos, staging, and integration tests, where "looks real and respects the schema" matters more than "identical bytes every run". If your need is deterministic fixtures inside a Drizzle test suite, drizzle-seed is the better tool and you may not need an alternative at all. If your need is realistic volume across a whole database, that's the Seedfast lane. The e2e test fixtures guide covers where each approach fits.

You're on Drizzle and want free, deterministic fixtures with believable names. That's drizzle-seed, and you probably don't need anything else. It's first-party, it's reproducible, and it generates straight from the schema you already maintain.

Your database isn't only a Drizzle app, the data needs to look real, your schema has circular foreign keys, or the seed has to run in a pipeline. That's the Seedfast lane. It reads any live Postgres schema, generates semantic data from a natural-language scope, handles the foreign-key graph including nullable circular references, runs as one CLI command in CI, and works as an MCP tool for AI coding agents — on a flat plan rather than per-row metering. It goes deep on Postgres where drizzle-seed spreads across three databases, and it's paid where drizzle-seed is free — the trade for the realism and the workflow.

For the wider field of Postgres generators beyond these two, the best Postgres test data generator comparison ranks tools on schema-awareness and CI fit, and the data seeding tools guide covers the regulated and compliance angle. If you're comparing the other alternatives, see the Snaplet Seed alternative (the discontinued schema-aware generator), the Tonic Fabricate alternative (the AI generation agent), and the Mockaroo alternative (the column-level web generator).

Yes. Seedfast reads the schema from the live Postgres database over a connection string, so it doesn't need a Drizzle schema object — it works on a Drizzle app, a Prisma app, a plain SQL schema, Supabase, Neon, or RDS. drizzle-seed is ORM-coupled: it operates on a Drizzle db instance and a Drizzle schema, so it only runs inside a Drizzle project. If your database isn't a Drizzle app, a database-coupled tool is the more direct fit.

Partly. drizzle-seed has good built-in generators for names, emails, cities, and companies, so those columns look plausible. But its catalog is fixed and there's no custom-generator API, so columns without a matching generator fall back to lorem-ipsum text, random strings, or numeric filler. Seedfast generates from a natural-language scope, so text and domain-specific columns get values that fit their meaning rather than placeholder filler.

Not well, by its own documentation. drizzle-seed notes it can't reliably infer references when circular dependencies exist, and a reported issue (drizzle-orm #3635) documents it looping on cyclical relations, so the workaround is to break the cycle manually. When one side of the cycle is nullable, Seedfast resolves the insert order automatically with a two-phase write — insert the parent with the back-reference null, insert the child, then fill it — so circular foreign keys seed without hand-written SQL. When both sides are strictly NOT NULL with no deferral, no seeder can, and the deferred-constraint pattern is the answer for either tool.

drizzle-seed is free and open source under Apache-2.0. Seedfast is a paid product with a 30-day free trial and flat paid plans with no per-row or per-token metering. The trade is that Seedfast adds semantic AI-generated data, a live-schema read on any Postgres, cycle handling, a one-command CI step, and an MCP integration — the things a free Drizzle-only library isn't built to do.

drizzle-seed runs as a library call inside your TypeScript app, so seeding in CI means running a script that imports your Drizzle schema and database client. Seedfast runs as one command that takes a connection string, so it drops into a pipeline as a single unattended step after a migration, and the same run is exposed as an MCP tool. The CI/CD database seeding guide covers the setup.

If what you want is a live-schema read on any Postgres, semantic data that looks real, cycle handling, and a CLI step in CI, that's the Seedfast shape. Seedfast connects to your PostgreSQL database, reads the schema and foreign-key graph, and generates connected, realistic data in one command — no ORM required, no schema to import, and a free 30-day trial to start. Run your first seed in about two minutes, or see pricing.

Related guides:

• Best Postgres Test Data Generator: the broader buyer comparison
• Database Seeder Comparison: how ORM seeders and standalone tools differ
• Circular Foreign Key Seed: the cycle case in depth
• Snaplet Seed Alternative and Tonic Fabricate Alternative: the other schema-aware alternatives