Best PostgreSQL test data tools in 2026

The best PostgreSQL test data tool depends on which job you are actually hiring for. If you want a full, foreign-key-intact dataset that matches your live schema and production distributions with no configuration, Weavori is the strongest self-serve choice: one command, a real CLI, and a flat $15/month. If you want individual values inside unit tests, use Faker. If you want to mask a real production dump rather than generate fresh rows, use Greenmask or pg_dump. If you need SOC 2 or HIPAA paperwork and sixteen database engines, Tonic.ai is the enterprise answer. This post walks the whole field and says which tool wins each scenario, including the ones Weavori loses.

"Test data" is where a team spends its first afternoon on a new schema and its hundredth hour a year later. The tool that fills a Postgres database determines whether your integration tests exercise real query plans, whether staging ever holds production PII, and whether the fixtures survive the next migration. Most roundups sort by brand name. This one sorts by the underlying mechanism, because the mechanism is what decides whether the data is trustworthy.

Quick picks

Pricing is as verified in mid-to-late 2026 and changes often, so treat the numbers as order-of-magnitude and confirm on each vendor's site before you budget.

What actually separates these tools

Before the names, the four axes that decide fit. Every tool below is scored against them.

1. Does it read your schema? A tool that introspects the database knows your tables, foreign keys, enums, constraints, and value frequencies. A tool that does not makes you describe all of that by hand, in code or config, and re-describe it every time the schema moves.
2. Is referential integrity guaranteed or your problem? Parent-before-child ordering either happens by construction or it does not. When it does not, you get dangling foreign keys and joins that silently return nothing, which is worse than no data because tests pass when they should fail.
3. Does the data match production's shape? Uniform random values look fine in a screenshot and mislead a query planner. Sampling the database's own pg_stats frequencies is what makes a status column stay 70 percent active, which is the difference between data that looks real and data that behaves real under load.
4. Where does it run, and what does it cost? A browser form, a library inside your tests, a CLI in CI, and a governed platform are four different products wearing the same name. Pricing model (flat, per-seat, credit-metered, per-row cap) decides what happens at scale.

The most common mistake is judging a test data tool on the number of data types it offers. That matters for a flat demo file. It is close to irrelevant for a relational schema, where the hard part is the relationships, not the values.

Schema-driven generators

These read the database and produce a whole dataset. This is the category that solves the relational problem, and it is where Weavori lives.

Weavori

Weavori is a closed-source CLI, distributed through npm, that connects to any PostgreSQL database (or parses pasted CREATE TABLE statements with no database at all), introspects the schema, resolves the foreign-key dependency graph, and streams referentially intact rows into a target database or to stdout. There is no config file, no DSL, and no per-column mapping: semantic rules infer a generator from column names, and value frequencies come from pg_stats.

The properties that matter for a test environment: foreign keys are correct by construction, distributions match production for any column whose values repeat, cross-column coherence (temporal ordering, conditional nullability, CHECK constraints) is enforced per row, and --seed replays a run byte-for-byte so a failing CI job can be reproduced exactly. It ships an MCP server with five tools (introspect, estimate, generate, doctor, sync), so Claude, Cursor, or any MCP client can drive the whole workflow. Generation is client-side, so source data never leaves your infrastructure, and licenses validate offline for air-gapped runners.

Pricing is a permanent free tier of 2,000 rows a month with unlimited runs, and Pro at a flat $15/month for 1,000,000 rows, with a 14-day full-Pro trial and no credit card. It is PostgreSQL-only by design. If you need MySQL, Snowflake, or MongoDB from one tool, that is a real gap and a reason to look at an enterprise platform instead.

Try it against your own schema in one command:

In-code libraries

These generate values, not datasets. You own the loops, the insert order, and the relationships. They are free, they are excellent at their actual job, and they are the wrong tool when the ask is "fill this 14-table schema realistically."

Faker

Faker (JavaScript and Python, MIT) is the standard library for a single plausible value: a name, an email, an address, called from inside your code. It has no concept of a schema, a foreign key, or a production distribution. For unit-test fixtures and in-code generation, it is close to unbeatable, and it pairs naturally with a schema-driven tool rather than competing with one. See the full Weavori vs Faker breakdown.

drizzle-seed

drizzle-seed is Drizzle's official seeding package. It reads your Drizzle TypeScript schema, generates deterministic data through a seedable pseudo-random generator, and lets you shape each table with a refine callback. It cannot read the database: realism comes from generator defaults plus the refinement code you write and maintain. For a Drizzle project that wants a populated database in a few lines, it is a great fit. For data that must match live production statistics, it is the wrong axis. See Weavori vs drizzle-seed and the Drizzle seed data guide.

prisma db seed

prisma db seed is a runner, not a generator: you write prisma/seed.ts and Prisma executes it. As of Prisma ORM v7 it no longer runs automatically on migrate reset. That is fine for the data an app needs to boot (a default currency, an admin user) and expensive for a realistic dataset, because every row's logic is script you own. See Weavori vs prisma db seed and the Prisma seed data guide.

Anonymizers and real-data copiers

A different job wearing a similar name: these start from your existing production rows instead of generating fresh ones. If the requirement is "make real data safe," this is the category. If it is "produce data that never contained a real person," it is not.

pg_dump

PostgreSQL's own backup tool copies real rows exactly, which is the highest possible fidelity and the highest possible exposure: every dump that leaves production carries PII into dev, staging, or a CI artifact, and a dump goes stale the moment the schema changes. Reach for it when you must reproduce a bug against literal records. See Weavori vs pg_dump.

Greenmask

Greenmask is an open-source Postgres CLI (Apache-2.0) that anonymizes and subsets a real production dump into a test database, with S3 support and per-column transformation rules you declare in config. High fidelity to genuine value patterns, but it needs production data to mask and manual rule setup. See Weavori vs Greenmask.

PostgreSQL Anonymizer

PostgreSQL Anonymizer (ODbL) is an extension that masks and substitutes data inside the database. It is fast and works in place, but it requires installing an extension and DBA-level access to the server, a constraint a client-side tool avoids. It is a good fit when a DBA owns the instance and in-place masking is exactly what the pipeline wants.

A note on the word "anonymize." A substitution that is consistent and unique per original value is pseudonymization, not irreversible anonymization, because the mapping can be rebuilt from a key you hold. That is usually what you want for staging (anything weaker breaks joins), but if a legal obligation requires data that can never be mapped back, check the requirement against what consistent substitution actually provides rather than trusting the flag name.

Enterprise test data management platforms

For large, regulated organizations, the question is less "generate rows" and more "govern data across environments, engines, and auditors."

Tonic.ai

Tonic is an enterprise synthetic data platform: multiple products, strong compliance credentials (SOC 2, GDPR, HIPAA, BAA/DPA), broad source coverage across sixteen or more databases including Snowflake and BigQuery, and a newer agentic AI Data Agent. It is a genuinely good answer when your constraint is a compliance binder or multi-engine breadth. It is a poor answer for a developer who wants a database in the terminal in two minutes: pricing is credit-metered ($0.17-$0.37 per LLM turn on Fabricate, with Structural behind contact sales) and its self-serve shape is a web platform, not a CLI. See Weavori vs Tonic.ai.

K2view and Delphix

These are full TDM suites for masking, virtualization, and AI synthesis across an enterprise. They are sales-led, on-premises or VPC, priced and staffed for governance teams, and they compete with each other rather than with self-serve tools. If that sentence describes your procurement process, they are on your shortlist; if it does not, they are not.

The do-nothing option: hand-rolled SQL

Plenty of teams skip all of the above and write INSERT ... SELECT generate_series(...) with random(). It is free, it is instant for one table, and it silently rots. It has no foreign-key awareness, so a child row can point at a parent that was never created; no distribution awareness, so every status is uniform; and no schema awareness, so a migration means editing the script and hoping you caught every column. This is the exact pain the schema-driven category exists to remove, and it is why "we already have a seed script" is the most common objection to any tool on this list, and the one worth testing hardest.

When Weavori is the wrong choice

An honest roundup names the losers, including itself.

• You need one tool across many engines (MySQL, SQL Server, Snowflake, MongoDB): pick Tonic. Weavori is PostgreSQL-only.
• A compliance auditor wants a SOC 2 or HIPAA binder and SSO and audit logs: that is an enterprise platform, not a $15 CLI.
• You need a single flat CSV of names for a UI mockup and no install: Mockaroo's browser form is faster than any CLI.
• You need one value inside a unit test: keep Faker; that is not Weavori's job.
• You must reproduce a bug against the literal rows that broke: copy them, with pg_dump or a masked slice, because generated data cannot recreate a specific real defect.
• You specifically want to mask your existing database in place, not generate a new one: Greenmask or PostgreSQL Anonymizer fits the shape of that task better.
• You want software you can read and self-host for free: Weavori is closed-source, so an OSS library or Greenmask matches that preference. Its free tier and offline license validation reduce, but do not remove, the trust ask.

How to choose, by scenario

A note on methodology

This is Weavori's own roundup, so assume bias and check the claims. Every competitor price and capability here was taken from the vendor's public site and recorded with a verification date on our comparison hub; none of it is scraped secondhand. Where a competitor is the better tool, that is stated plainly above. For a deeper, feature-by-feature view, the comparison hub and each /vs/... page carry the same data with citations.

FAQ

What is the best free tool for PostgreSQL test data?

For individual values and small deterministic fixtures, Faker, drizzle-seed, or prisma db seed are genuinely free (open source) and need no vendor. For a whole relational dataset, Weavori's permanent free tier generates 2,000 rows a month with unlimited runs and no per-file caps, which is enough to seed a real schema in CI without paying. "Free" also describes pg_dump and the extension-based anonymizers, but those copy or mask existing data rather than generating new rows.

Is synthetic data the same as anonymized or masked data?

No. Synthetic data is generated fresh from a schema; no production value ever appears, so there is nothing to leak. Anonymization and masking start from real rows and transform their sensitive columns; the structure and many patterns of the original survive. They solve different problems: generation removes the need to touch production data at all, masking makes a copy of production data usable. Some tools, including Weavori's sync --anonymize, offer masking as a separate command from generation.

Which test data tool is best for CI pipelines?

One with a real CLI and headless auth. Weavori runs from npx on Linux, macOS, and Windows, takes an API key, prints plain or json, returns standardized exit codes for pipeline branching, caches the schema between runs, and validates licenses offline for restricted-network runners, so it fits a pipeline without a UI or a phone home. In-code libraries like Faker and drizzle-seed also run in CI, but inside your test process and only for what your code asks them to build.

Can I just use Faker, or pg_dump, instead of a dedicated tool?

You can use both, for their actual jobs. Faker produces single values in code; it has no schema or foreign-key awareness, so filling a relational database means writing and maintaining that logic yourself. pg_dump gives exact rows but moves production PII into lower environments and goes stale when the schema changes. A schema-driven generator replaces the maintenance of the first and the privacy and freshness problems of the second, which is why teams reach for one once their fixtures stop being small.

Does it matter whether the data matches production distributions?

Yes, for anything that touches the query planner. PostgreSQL chooses execution plans from column statistics, so a CI database of uniform random values picks different indexes and joins than production, and a performance or load test run against it measures the wrong thing. A tool that samples pg_stats (Weavori) reproduces the skew, so a status that is 70 percent active in production stays 70 percent active in the replica. For pure referential unit tests it matters less; for performance, plan regressions, and realistic edge cases it is the whole point.

How much should PostgreSQL test data tooling cost?

Flat self-serve tools run from free to about $15 to $29 a month. Weavori is $15/month flat for a million rows with unlimited runs; Tonic's entry Plus plan is $29/month with credit-metered usage on top; Mockaroo bills annually from $60 to $7,500 by row caps. The cost that is easy to miss is maintenance: an in-code or hand-written approach is free to license and expensive in engineer-hours as the schema evolves, which is the line item a schema-driven tool trades against.

Get started

If you want a foreign-key-intact, production-shaped PostgreSQL dataset without writing config, install Weavori and point it at a database you control. The free tier needs no card, and the quickstart runs end to end in a few minutes.

Weighing it against a specific tool? The comparison hub has honest, cited breakdowns of every product on this page. Prototyping before a database exists? Generate from pasted DDL with no database. And for the mechanics of realistic data itself, read how to generate realistic test data for PostgreSQL.