Salesforce test data comes from three places: copying it out of production, generating it synthetically, or a mix of both. This page compares the native option and real commercial tools on the dimensions that decide whether your sandbox data is actually usable.
The unnamed options below are real, widely-used commercial products, not composites. Their capabilities are taken from each vendor’s public documentation as of the review date shown, and cells we could not verify publicly say so. We anonymize the names because this page is about capabilities, not vendors.
capabilities reviewed July 2026, from vendor-published documentation.
| Dimension | Sandbox copy (Salesforce) | Option B | Option C | Syntharil |
|---|---|---|---|---|
| Data source | A direct copy of production records — Partial Copy sandboxes hold 5 GB of production or sample data, Full sandboxes hold all of it | Copies production data into up to five destination sandboxes at once — not synthetic generation | Synthetic by default; an optional plugin can additionally query and reference existing org records while generating new ones | Synthetic generation, or a masked copy of production via replication |
| Realism & coherence | Real production records, so realism comes from being real data — no generation or validation feature is documented | Varies / not documented — beyond being a copy of real production records, no distinct realism or validation feature is described | Fake-data generators and formulas produce schema-valid, realistic-looking values per object; cross-field coherence beyond that: Varies / not documented | Schema-valid values; persona-anchored generation keeps records coherent across objects |
| Relationship handling | Templates auto-include a child object’s required parent object(s) so referenced records aren’t left orphaned | Data sets automatically maintain parent-child relationships, with a control to skip specific relationships | Load order is inferred automatically from table dependencies, with an explicit reference syntax linking generated records | Parents created first; children reference real IDs — lookup chains work out of the box |
| PII exposure | Production data, PII included, lands in the sandbox unmasked as part of the copy unless a separate masking product runs afterward | A per-field "Set Random Value" data-set property can scramble a field’s value in the destination org to obfuscate sensitive information | No production PII by default since data is synthetic; if a recipe queries existing org data, masking or filtering of what’s returned: Varies / not documented | None for synthetic data; deterministic masking in transit for replicated data |
| Custom objects & fields | Templates target inclusion at the object level — both standard and custom objects can be checked into a template | A data set is a reusable chain of instructions identifying which Salesforce object data to copy from a source org to a destination org | Recipes target specific objects via YAML object declarations, with per-field fake-data generators, formulas, and static values configured individually for each object | Driven by the org’s live schema — custom objects, fields, picklists, required flags |
| Per-object targeting | Object-level checkboxes only; whether records within a selected object can be filtered by criteria: Varies / not documented | Admins select up to 25 related objects per data set, with query filters, record limits, and relationship-skipping to seed only the data needed; no fixed per-object record cap is documented | Record volume is controlled via run options (e.g. run until records loaded) and configurable batch sizes; no fixed platform-imposed record-count cap is documented | Choose objects and record counts per run; per-field overrides (skip, fixed, pattern, range, AI) |
| Setup effort | An admin creates a sandbox template in Setup, then selects that template when creating or refreshing a Partial Copy or Full sandbox | Build a reusable data set (objects, relationships, filters) once, then run it from a Seed Sandbox wizard in minutes per run | Install the framework’s command-line tool (Python and Git prerequisites), then author a YAML recipe and run it from the command line — developer/CLI-oriented | Connect the org, pick objects and counts, run — no packages to install |
| Licensing shape | Partial Copy and Full are separately licensed sandbox add-ons bundled with a set number of Developer sandboxes, not unlimited on every edition | Tiered subscription with a base per-user license, additional seats as an add-on, and optional à la carte modules | Free and open source (BSD-3-Clause) — no vendor subscription | SaaS subscription with a free tier |
Salesforce’s native mechanism for populating a non-production org — Partial Copy or Full sandboxes, which copy an admin-selected (or complete) subset of actual production records into the sandbox as part of the copy/refresh itself.
Where it wins: Native to Salesforce with no external connector or install; data is always schema-valid and relationally consistent because it’s real production data, and templates auto-include required parent objects to avoid orphaned records.
Trade-offs: Production data, including any PII it contains, is copied into the sandbox unmasked unless a separate masking product is run afterward; Partial Copy is capped at 5 GB and 10,000 records per selected object; targeting is object-level only; and Partial Copy and Full are distinct add-on licenses, not available by default in every edition.
A Salesforce DevOps vendor’s sandbox-seeding feature that copies data from a source org into up to five destination sandboxes at once, using reusable "data set" definitions of which objects and relationships to move.
Where it wins: Automatically maintains parent-child relationships during seeding, can scramble a field’s value in the destination org to obfuscate sensitive information, supports query filters, record limits, and relationship-skipping for fine-grained targeting, and seeds up to five environments from one reusable data set.
Trade-offs: An external product requiring its own connected-app setup and reusable data-set configuration rather than a native in-org feature; licensed as a separate tiered subscription with per-user seats on top of Salesforce’s own sandbox licensing; no documented per-object record cap means volume is whatever the admin’s filters allow.
An open-source recipe-driven synthetic data generator, embedded in a Salesforce automation framework, that creates fake records with inferred relationships and loads them into a connected org.
Where it wins: Free and open source; generates unlimited-by-default synthetic data with automatically inferred load order and explicit relationship references, so no production PII is exposed by default; per-object recipes give fine-grained control over generated field values.
Trade-offs: Setup and recipe authoring are developer/CLI-oriented — Python tooling, YAML recipes, command-line execution — rather than a point-and-click admin experience; cross-field realism beyond individual fake-data providers: Varies / not documented; and if a recipe queries existing org data, masking or filtering of what that query returns: Varies / not documented.
Syntharil generates schema-valid synthetic data — realistic names, valid picklists, connected relationships, optional AI-generated field values — and can also seed a sandbox with a masked, filtered copy of production when only real data will do.
Where it wins: You need realistic, relationship-correct data on demand in any org you own, without waiting on a sandbox refresh or exposing production PII.
Trade-offs: Synthetic data is generated, not copied — native features Salesforce computes itself (field history, Chatter) aren’t part of a seeding run.
See what the generator produces on the test data generator page →, or walk through every approach in 9 ways to generate realistic Salesforce test data →.
It depends on what "realistic" must mean for you: schema-valid values are table stakes; coherent personas and connected relationships are what make data genuinely testable. Compare tools on relationship handling and coherence, not record counts.
Synthetic data has zero PII risk and unlimited shape control; production copies match reality exactly but need masking to be safe. Many teams use both: synthetic for development, a masked filtered copy for UAT.
Salesforce’s own sandbox templates explicitly document support for checking custom objects into a template; the commercial tools above configure data object-by-object. Syntharil generation is driven by the org’s live schema, so custom objects and fields are covered automatically, including their picklists and required flags.
The generator has to create parents before children and point child lookups at the real IDs it just created. Tools that load flat CSVs per object leave you stitching relationships manually.