weavori vs faker
Weavori vs Faker
The standard fake-data library
Faker is the ubiquitous fake-data library: it generates individual values (names, emails, addresses) inside your code, and it does that job brilliantly. It is not schema-aware. Relationships, foreign keys, constraints, and distributions are all your responsibility — you decide what to generate, in what order, with what shape. Weavori reads your PostgreSQL schema and generates the entire dataset — FK-intact, distribution-true — in one command. Faker and Weavori answer different questions: 'give me a value' vs 'give me a dataset'.
Pricing verified July 2026 · Open-source library (Python / JS) · Faker website ↗
At a glance
| Dimension | Weavori | Faker |
|---|---|---|
| Category | Schema-driven CLI | Per-value library |
| Unit of generation | Whole dataset from the schema | Individual values in code |
| Schema awareness | Full Postgres introspection | None |
| FK integrity | By construction | Manual |
| Distributions | pg_stats sampling | Uniform random |
| Setup | One command | Install + write generator code |
| CI | First-class CLI | Library inside your test code |
| Cost | Free 2K rows/mo; Pro $15/mo | Free (MIT) |
How they compare
A closer look at the dimensions that actually change the decision — and the honest trade-offs in each.
Unit of generation
weavori
Weavori generates a dataset: every table filled, foreign keys resolved in dependency order, output streamed into a target database or to stdout. You never enumerate columns — the schema does.
faker
Faker generates one value at a time, called from your code. For a 14-table relational schema you write the loop, the ordering, and the relationship logic yourself — that's the seed script problem in another form.
Bottom line: For single values and in-code fixture data, Faker is the right tool. For a schema's worth of relational data, Weavori removes the script entirely.
Integrity and realism
weavori
Weavori samples pg_stats so value frequencies match production — 70% active stays 70% active — and enforces cross-column coherence: temporal ordering, conditional nullability, CHECK constraints.
faker
Faker produces plausible individual values with uniform randomness. It has no concept of a foreign key, a CHECK constraint, or a production distribution.
Bottom line: If your tests depend on data that behaves like production — and query plans do — Weavori's distribution sampling matters. Faker suffices for plausible-looking unit-test values.
Automation and CI
weavori
Weavori is a CLI with API keys, plain/JSON output, standardized exit codes, and a schema cache — designed to run in pipelines and be driven by AI assistants via its MCP server.
faker
Faker runs wherever your code runs, which is genuinely convenient for unit tests. But it cannot populate a database for you, and there is no pipeline-native interface.
Bottom line: Use Faker inside your tests for isolated fixtures. Use Weavori in CI when the pipeline needs a fresh, production-shaped database.
Which one fits your team?
choose weavori when
- Relational datasets where foreign keys must stay intact
- Dev, staging, and CI databases that need production-shaped data
- Teams that don't want to write or maintain seed scripts
- Pipeline and AI-assistant-driven generation
choose faker when
- Single values inside unit tests and fixtures
- In-code data generation with full programmatic control
- Language-native pipelines that want zero new dependencies
- Deterministic small fixtures
Frequently asked questions
The questions we hear most from teams evaluating Weavori against Faker.
Try Weavori against your own schema
Connect any PostgreSQL database and see the plan before a single row is written.
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Also compare: Tonic.ai · Mockaroo · How realistic test data is made