SeedBase, a self-serve Tonic.ai alternative for data masking and synthetic test data

Where Tonic.ai is genuinely strong

• A mature enterprise platform for de-identifying large, complex production databases, with masking, tokenization, generalization and format-preserving encryption.
• Broad native connector coverage across relational and NoSQL databases, warehouses and lakehouses, plus a separate product for unstructured free text.
• Battle-tested referential integrity and database subsetting, plus governance features big organizations need: role-based access control, audit trails, privacy reports and SSO.

If you are an enterprise with a data-platform team, compliance officers and a procurement process, Tonic.ai earns its place on the shortlist. The rest of this page is about the other case: a developer or a small team who wants working test data without that overhead.

How getting started with Tonic.ai actually works

Tonic.ai is built around a source database. The platform suite splits the job: Structural transforms sensitive production data into safe test data, Fabricate generates synthetic data from scratch or modeled on an existing database, and Textual redacts unstructured text. There is a self-serve entry plan with metered usage, but the Structural free trial requires a corporate email address (public domains like Gmail are blocked), self-hosting is reserved for paying or evaluating customers, and the enterprise feature set, broad connectors, SSO and governance, is quote-based and sales-led, typically reached through a 30-minute demo with a data-transformation specialist.

That is a sensible shape for a data-platform purchase. It is friction if you are a single developer who imported a schema this morning and wants seeded staging by lunch. SeedBase is built for that second person: any email, no card, schema in, data out.

Generate test data from the schema alone, no source database

Tonic's transformation workflows start from a source database you connect and de-identify. SeedBase can do that too, but it does not need it. Point it at a schema and it produces structurally correct, foreign-key-consistent rows, which is exactly what you want before any production data exists, or when you simply should not touch it:

// seed-sql.mjs, generate from a schema with no prod data
import { SeedbaseClient } from "@seedbase/client";
import { writeFile } from "node:fs/promises";

const client = new SeedbaseClient({ token: process.env.SEEDBASE_TOKEN });
const gen = await client.generate(process.env.SEEDBASE_PROJECT, { seed: 42, wait: true });
await writeFile("seed.sql", await client.download(gen.id, { format: "sql" }));

The schema can be a CREATE TABLE dump from PostgreSQL or MySQL, a Django models.py, a Prisma schema.prisma, or a live read-only connection. Output goes to SQL, CSV, JSON, or a direct push into your database, and because generation is deterministic by seed, the same schema yields the same rows on every run. The full SQL path is on the SQL test data page.

Tonic.ai vs SeedBase: where they differ

Format-preserving, join-consistent masking of production data

The de-identification core that Tonic Structural is known for, masking real values while keeping format and relationships intact, is also what SeedBase does for the self-serve case. SeedBase detects PII and replaces it with realistic, format-preserving values that stay join-consistent: an email or a customer id is masked to the same replacement everywhere it appears, so foreign keys and joins still line up after masking.

• Format-preserving: a masked email is still a valid-looking email, a masked phone still a plausible phone, so constraints and downstream parsing keep working.
• Join-consistent: the same source value maps to the same masked value across tables, so a masked orders.user_id still points at the masked users.id.
• FK-complete subsetting: pull a small, coherent slice of a large database for local development without orphaned rows or broken references.

If your masking need is a GDPR-conscious staging copy rather than a governed enterprise estate, the German-language walkthrough on DSGVO-Anonymisierung shows the masking flow end to end.

Switching from Tonic.ai to SeedBase

You keep what mattered, referential integrity and format-preserving de-identification, and drop the procurement and demo gate. The switch is short because SeedBase reads the schema you already de-identify.

1. Create a free account. Sign up at seedbase.dev with any email, no corporate domain and no credit card required, and grab one API key under Settings, API keys (it looks like dr_sk_...).
2. Import the same schema. Point SeedBase at the SQL dump, Django models.py, Prisma schema.prisma, or a live read-only database URL you already use as the source. It reads tables, columns and relations and builds the generation blueprint.
3. Pick generate or mask. For lower environments with no real data, generate FK-consistent rows from the schema. For a staging copy of real data, configure PII detection and format-preserving, join-consistent masking, plus FK-complete subsetting if you only need a slice.
4. Export or push. Download SQL, CSV or JSON, or push straight into a database. The output is language-agnostic, so a mixed-stack team shares one tool.
5. Pin a seed for CI. Pass seed: 42 so every run, local or in a pipeline, produces the same rows, which keeps snapshot tests stable and lets a failing test reproduce locally.

Deterministic test data in CI

A staging or CI dataset is only useful if it is repeatable. SeedBase generates a SQL file keyed off the seed, so the same input produces the same database on every run. Generate it in a pipeline step and load it before the suite:

# .github/workflows/test.yml
- run: node seed-sql.mjs
- run: psql "$DATABASE_URL" -f seed.sql

Because the data is deterministic by seed, a test that fails in CI reproduces locally with the same seed: 42. The Django path, including a pytest fixture that pulls rows directly, is on the Django test data page.

When to pick which

Pick Tonic.ai if you are de-identifying very large production estates with organizational requirements: governed access across a data org, SSO, audit trails, broad NoSQL and warehouse connectors, self-hosting, and dedicated support contracts. That is its home turf.

Pick SeedBase if you want results today: synthetic data from schema alone, GDPR-conscious format-preserving masking for staging, FK-complete subsetting, EU hosting, generation straight from your IDE, CLI or AI assistant, and a price you can read on a pricing page without a call.

Tonic.ai alternative: FAQ

For many developer teams, yes. Both keep referential integrity and both mask production data in a format-preserving, join-consistent way. The difference is the motion. Tonic.ai is an enterprise de-identification and synthesis platform with a sales-led, demo-driven path and a free trial that requires a corporate email. SeedBase is self-serve: sign up with any email, no credit card, generate FK-consistent data from the schema alone, and mask or subset real data when you need to.

No. SeedBase generates realistic, foreign-key-consistent data from the schema alone, whether that is a SQL dump, Django models.py, a Prisma schema, or a live database connection. Masking real data is available when you need it, but it is never required, so you can build a staging dataset before any production data exists.

Yes. SeedBase detects PII and applies format-preserving masking that stays join-consistent, so the same source value maps to the same masked value across every table and foreign key. It also does FK-complete subsetting, pulling a coherent slice of a large database without breaking referential integrity. The difference from Tonic is reach and procurement, not the core technique.

SeedBase publishes its prices: a free tier with no credit card, Pro at 19 euro per month, and Team at 79 euro per month. Tonic.ai has a self-serve entry plan and metered usage, but enterprise capabilities such as self-hosting, broad connectors, SSO and governance are sales-led and quote-based. With SeedBase the number you see is the number you pay.

In the EU, with no third-party scripts on the platform and no data transfer to third countries, which matters for GDPR-conscious teams. Tonic.ai offers US-centric cloud and a self-hosted option, but self-hosting is reserved for paying or evaluating customers.

Yes. Beyond the web app, SeedBase ships a CLI, Node, PHP and Python SDKs, a pytest plugin, VS Code and JetBrains plugins, and a hosted MCP server for AI assistants. Generation is deterministic by seed, so a SQL file produced in CI is byte-stable and a failing test reproduces locally with the same seed.

Yes. Create a free account, import the same schema you already de-identify (SQL dump, Django models, Prisma, or a live connection), generate FK-consistent data or configure format-preserving masking, then export SQL, CSV or JSON or push straight to a database. There is no procurement step and no demo gate to get to working data.