Tonic.ai alternative for document training data

What Tonic.ai is

Tonic.ai positions itself as synthetic data for software and AI development, and as of mid-2026 ships three products (per its docs):

• Tonic Structural. Test data management for databases: de-identify, subset, and synthesize structured and semi-structured production data using masking, tokenization, generalization, scrambling, and format-preserving encryption.
• Tonic Textual. De-identify, redact, and synthesize unstructured data and files — including PDFs, DOCX, images, and spreadsheets. The documented workflow scans your files for sensitive values, then redacts or replaces them, returning output in the same format.
• Tonic Fabricate. Generation from scratch: relational data, free text, and mock APIs, described from a chat prompt or modeled from connected databases. Acquired by Tonic.ai in April 2025 from the creator of Mockaroo.

(A fourth product, Tonic Ephemeral, no longer appears in Tonic’s current product lineup or docs.)

What Tonic.ai is genuinely good at

• Database coverage. Structural connects to PostgreSQL, Oracle, Salesforce, IBM Db2, Redshift, Snowflake, Databricks, MySQL, SQL Server, MongoDB, BigQuery, S3, and more — while preserving referential integrity across databases. If your test-data problem lives in a database, this is a mature, well-trodden path.
• Compliance maturity. Tonic.ai maintains a SOC 2 report through independent audit, documents HIPAA Safe Harbor de-identification workflows, and offers self-hosted deployment (per its trust center). For enterprise procurement in financial services and healthcare, that track record is a real asset.
• Privacy workflows over existing files. If the task is “we have 50,000 real contracts with PII and we need safe versions,” Textual’s detect-redact-replace pipeline is built for precisely that, across many file formats.

If those are your problems, use Tonic.ai. Seriously — the rest of this page is about a problem they don’t claim to solve.

Where the boundary is for document AI training data

Training a document extraction model needs filled, rendered pages paired with ground-truth labels: per-word bounding boxes, field IDs, entity types, and structured JSON targets. Measured against that need, three gaps appear:

• De-identification starts from data you must already have. Structural and Textual transform existing records and files. If you don’t have a corpus of real W-2s or claims — or can’t get clearance to touch one — there is nothing to de-identify. Generation from a population model needs no source data at all.
• No advertised layout-labeled training output. Textual’s documented output is your files with sensitive values redacted or replaced, in the same format. Neither the Textual nor Fabricate documentation advertises bounding-box annotations, FUNSD/Donut-style labels, or document-understanding training datasets. Fabricate can export free text as PDF, DOCX, and EML files — but its documented focus is application data and mock APIs, not labeled form renders.
• Sales-led pricing for the flagship products. As of June 2026, Structural and Textual are custom-priced via sales (Fabricate, to its credit, has a self-serve tier at $0 and $29/month). SymageDocs publishes every tier and starts with 250 free credits — you can evaluate end-to-end without a call.

Dimension by dimension

Tonic.ai capabilities and pricing verified against tonic.ai product pages, docs, and pricing page, June 2026. Sources: docs.tonic.ai, tonic.ai/pricing.

What “document-shaped” looks like in practice

Here is the SymageDocs workflow end to end: no source database, no redaction pass, no annotation queue. One API call produces rendered healthcare claims with the labels a training loop consumes directly.

from symagedocs import Client

client = Client(api_key="sk_live_...")

# No production data required: every document is filled from a
# simulated identity. FUNSD-style per-word ground-truth labels
# are always included in the bundle — no need to request them.
job = client.generate.create(
    form_id="cms_1500_02_12",
    quantity=1_000,
    seed=42,
    output_formats=["pdf_typed", "png_typed"],
    degradation_profile="scanned",
)

client.generate.wait(job.job_id)

# One zip bundle: rendered PDFs, page images, and annotations
# with bounding boxes and field labels for every value on
# every page.
client.generate.download(job.job_id, format="dataset", path="./cms1500_dataset.zip")

When to use each

The honest pattern: plenty of teams could sensibly run both — Tonic.ai for safe copies of what exists, SymageDocs for the document corpus that doesn’t. If your pipeline starts at a CMS-1500, a W-2, or a 1040, those pages show exactly what generated output looks like. For training strategy across model families, see synthetic training data for document AI.

Frequently asked questions

Is SymageDocs a replacement for Tonic.ai?

For most Tonic.ai workloads, no — and we'd rather say that plainly. If you need to de-identify a production PostgreSQL database for staging, subset it, and keep referential integrity intact, Tonic Structural is built for exactly that and SymageDocs does not do it. The overlap is narrow and specific: teams who need document-shaped synthetic data — filled forms rendered as PDFs and images with ground-truth labels — for training or testing document AI. That is what SymageDocs is purpose-built for.

Doesn't Tonic Textual handle documents and PDFs?

Yes — Tonic Textual ingests files including PDFs, DOCX, images, and more, and its documented workflow is to detect sensitive values in your existing files and redact or replace them, returning output in the same format. That is a privacy workflow over documents you already have. It is different from generating new filled forms from scratch with per-word bounding boxes and entity labels for model training, which is the SymageDocs workflow. Tonic's documentation does not advertise layout-labeled training-data output.

What about Tonic Fabricate — doesn't it generate data from scratch too?

It does. Fabricate (which Tonic.ai acquired in April 2025 from the creator of Mockaroo) generates relational data, free text, and mock APIs from scratch, and can export documents as PDF, DOCX, and EML files. The difference is the target: Fabricate's documented focus is application databases and test environments. SymageDocs generates filled regulated forms — W-2s, 1040s, CMS-1500s — rendered typed or handwritten, with the bounding-box and entity labels a document AI training pipeline consumes directly.

Do I need production data to use SymageDocs?

No, and that is the core architectural difference. De-identification starts from real records, so you need production data access — and the compliance review that comes with it — before you can produce anything. SymageDocs documents are filled from simulated identities, so there is no source dataset, no PII in the pipeline at any stage, and no de-identification step to validate.

How does pricing compare?

As of June 2026, Tonic Structural and Tonic Textual are quote-based (custom pricing via sales), while Tonic Fabricate has a self-serve tier ($0/month with $10 of monthly credits, $29/month with $25 of credits, plus pay-as-you-go). SymageDocs publishes all tiers: 250 free credits to start, with self-serve plans from $79/month (billed annually) — no sales call required at any self-serve tier.