AI Platforms Synthetic data for real engineering workflows Deterministic, relationship-safe generation

Generate production-shaped test data without touching production records.

Create realistic synthetic datasets from schemas, metadata, and prompts with foreign-key integrity, repeatable seeds, audit logs, and environment-ready workflows for staging, QA, demos, and CI.

Schema awareDeterministic outputsPII-aware workflowsAudit-ready controlsCI friendlyFintech and SaaS readyproduct fit Repeatableseeded dataset runsForeign-key saferelational generationPolicy-basedteam access controlsBuilt-inquality reports

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Product details

What it does

MockMetrics Data Generator helps engineering teams create realistic non-production data for development, QA, staging, demos, and automated test workflows. Start from a schema, sampled metadata, or a structured prompt to generate believable records with foreign-key integrity, business-rule consistency, repeatable seeds, and environment-specific outputs. Teams use it to replace risky production copies, speed up onboarding, refresh staging safely, and keep CI pipelines supplied with useful synthetic datasets.

CategoryAI Platforms

Pricing modelFree tier available, paid plans from $29/month, with team and enterprise options for CI, governance, and recurring environment refreshes.

Best forDevelopers, QA teams, platform engineers, product teams, and technical founders who need realistic synthetic data for staging, regression testing, demos, and secure non-production workflows.

Feature set

Key product features

Schema-based dataset generation

Prompt-based data creation for quick starts

Relationship-aware records for linked tables

Deterministic seeds for repeatable test runs

Business-rule consistency across tables and fields

PII detection and sensitive-field warnings

Production-safe synthetic replacement workflows

Environment targeting for local, staging, and CI

Refresh schedules and rollback-ready dataset versions

Referential integrity validation and quality reports

Template library for common app and vertical schemas

Role-based access and approval controls

API access for repeat workflows and automation

Audit logs and source-data exclusion controls

Add exportable handoff and reporting workflow

Narrow to the highest-value buyer segment

Use cases

Where it helps

Seeding a staging database without using customer data

Creating realistic demo datasets for SaaS products

Generating repeatable QA records for regression cycles

Accelerating local development setup for new engineers

Refreshing test environments on a schedule with deterministic outputs

Creating synthetic fintech, healthcare-adjacent, and B2B SaaS datasets

Validating edge cases, null rates, and error distributions before release

Automating test data generation inside CI and release workflows

How it works

From schema to safe staging in a clear rollout path

MockMetrics is built for implementation, not one-off fake data exports. Teams can connect structure, choose controls, generate datasets, validate quality, and refresh environments on schedule.

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Map your schema or metadata Start from tables, field definitions, sampled metadata, or prompts. Detect relationships, constraints, and sensitive fields before generation begins.

Apply rules and ownership Choose deterministic seeds, edge-case presets, approvals, and team permissions so every dataset matches your workflow and governance model.

Generate, validate, and refresh Produce synthetic datasets with integrity checks, quality reports, rollback-ready versions, and scheduled refreshes for staging or CI pipelines.

Built for faster onboarding

Start with ready-made templates instead of building every rule from scratch

The template library helps teams launch quickly with practical starting points for common app structures, test scenarios, and regulated-adjacent workflows.

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App schema starters Use ready-made templates for users, subscriptions, invoices, events, tickets, orders, and account hierarchies.

QA scenario presets Generate happy-path records, edge cases, null-heavy datasets, failed payments, expired trials, and date-sensitive scenarios.

Vertical-ready packs Begin with synthetic dataset templates for B2B SaaS billing, healthcare-adjacent scheduling, and fintech-style transactions.

Why teams trust it

More than realistic values: controls, proof, and team accountability

Engineering teams need synthetic data that is believable, governed, and easy to defend across QA, platform, and security conversations.

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Audit-friendly activity records Track who created datasets, which rules were used, where outputs were sent, and when refreshes were run.

Sensitive-data safeguards Flag likely PII fields, apply synthetic replacements, and keep source-data exclusion controls visible during setup.

Shared team ownership Use role-based permissions, approvals, and environment-level access so platform, QA, and dev teams can work together safely.

Buyer proof

Show the value before rollout with savings and workflow impact

The page experience makes implementation and payoff easy to understand for engineering leads, QA managers, and platform teams.

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Time-saved calculator Estimate how much setup time your team can save by replacing manual seed scripts and ad hoc data prep with reusable generation flows.

Fewer risky production copies Compare synthetic generation against cloning live records into staging or demo environments when privacy and governance matter.

Clear plan fit by team size Choose a plan for experiments, recurring team workflows, or governed enterprise environments with approvals and audit controls.

Pricing

Commercial packaging

Editable pricing cards exported directly in the product catalog JSON.

Starter

For small projects, early evaluation, and lightweight development environments.

Prompt and schema-based generation
Basic synthetic datasets and saved templates
Limited deterministic runs
Starter QA scenario presets
Community-style self-serve setup

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Recommended

Pro

$29/mo

For individual developers and small product teams that need repeatable, realistic test data.

Higher generation limits
Deterministic seeds for repeatable tests
Relationship-aware and business-rule-safe outputs
PII field detection and synthetic replacement warnings
API access and reusable workflow templates

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Team

Custom

For shared staging, QA, and CI workflows across engineering teams.

Scheduled environment refreshes
Role-based access and approval controls
Quality reports and referential integrity checks
Template library for app and vertical datasets
Audit logs, ownership visibility, and rollout playbook

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FAQ

Buyer questions

Can I generate data from a schema or sampled metadata?

Yes. You can start from a schema, structured table definitions, sampled metadata, or prompt-based instructions and generate realistic synthetic datasets from there.

How does deterministic generation help QA and CI?

Deterministic seeds let your team recreate the same dataset patterns across runs, which is useful for regression testing, CI pipelines, debugging, and release validation.

Does MockMetrics help avoid exposing sensitive production data?

Yes. The workflow is built around synthetic generation, PII detection warnings, and source-data exclusion controls so teams can populate non-production environments more safely.

What does the implementation playbook include?

It gives teams a rollout path with setup steps, admin configuration guidance, workflow examples, launch checklists, and success milestones for staging, QA, and demo use cases.

Are there ready-made templates for onboarding?

Yes. The template library includes common app models, reusable workflow presets, and scenario packs for subscriptions, billing, orders, support, and other recurring product patterns.

Can multiple teams share ownership and approvals?

Yes. Team plans support role-based access, approval steps, manager visibility, and ownership controls so engineering, QA, and platform teams can collaborate on dataset workflows.

Which industries are a strong fit?

The product is especially useful for B2B SaaS teams, healthcare-adjacent software teams, fintech-adjacent workflows, and enterprise QA groups that need realistic data without using live customer records.

How can buyers estimate the value before rollout?

The platform supports payoff-oriented evaluation with time-saved examples, avoided manual setup work, and clearer before-and-after workflow comparisons for staging, QA, and demo preparation.

Next step

See how MockMetrics fits your staging, QA, or CI workflow

Talk through your schema, environment targets, governance needs, and rollout plan to find the right setup for your team.

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