Fabricate Review: Key Features and Pros&Cons

Name: Fabricate
Author: Fabricate

What it is:Fabricate is an AI-powered full-stack app builder that generates production-ready React/TypeScript code with databases from natural language descriptions.
Best for:Individual developers and small teams, Growing engineering teams, Enterprises building AI software
Pricing:Starting from $20/mo
Rating:85/100Very Good
Expert's conclusion:Fabricate is best for developers/testers that require synthetic data quickly and realistically; however, be sure to confirm your company’s requirements for enterprise grade prior to implementation.

Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

Company Overview

The synthetic data generation tool known as "Fabricate" is an advanced tool that was created by Mockaroo and uses artificial intelligence (AI) to generate realistic relational datasets based on user input in the form of schemas, SQL statements, or natural language prompts. Fabricate was acquired by Tonic.ai on April 22, 2025, and is being used in conjunction with Tonic.ai’s privacy preserving data synthesizer. With Fabricate, developers can create synthetic relational datasets from scratch using a variety of input methods.

Acquired

📍San Francisco, CA

📅Founded 2018

🏢Subsidiary

TARGET SEGMENTS

Software DevelopersAI EngineersEnterprise TeamsTesting Teams

Key Metrics

👥

Hundreds (via Tonic.ai)

Customers

📊

2018

Founded

📊

San Francisco, Atlanta, New York, London

Offices

HIPAA(USA)GDPR(EU)PCI(Global)CCPA(USA)

Credibility Rating

85/100

Excellent

Fabricate has a strong reputation for its credible nature due to the fact that it was purchased by Tonic.ai, which provides enterprise compliance support. Additionally, Fabricate supports the use of private safe data creation, which is vital for many development workflows.

BREAKDOWN

Product Maturity80/100

Company Stability90/100

Security & Compliance95/100

User Reviews75/100

Transparency80/100

Support Quality85/100

TRUST SIGNALS

Acquired by Tonic.ai in 2025Trusted by Fortune 500 companiesHIPAA, GDPR, PCI, CCPA compliantUsed by healthcare networks and financial institutions

Company History

2018

Company Founded

Fabricate is a company that was formed after the offices of San Francisco, Atlanta, New York, and London were opened by the Mockaroo Team.

2025

Acquired by Tonic.ai

On April 22, 2025, Tonic.ai purchased Fabricate and began incorporating it into their synthetic data generator. Mark Brocato joined the development team at this time.

Key Features

✨

Schema-First Data Generation

Fabricate generates real world relational synthetic database instances directly from the schema definitions.

✨

Natural Language Prompts

Fabricate creates synthetic data using plain english instruction which makes it easy for both technical and non-technical people to utilize.

✨

SQL with Embedded Guidance

Fabricate allows users to issue SQL queries while providing guidance for the user to create very specific synthetic data.

✨

Edge Case Simulation

Fabricate can generate large amounts of statistical data to create rare scenarios, correct bias, and to test data.

✨

Greenfield Development

Fabricate is designed specifically for new applications where there are no production data sets available because of privacy issues.

✨

AI-Powered Engine

Fabricate utilizes Large Language Models (LLMs), and a rules engine to create high volume datasets that appear realistic.

✨

Privacy Compliance

Fabricate provides support for HIPAA compliant healthcare data sets and all regulated environments.

Tech Stack

Infrastructure

Cloud-based (via Tonic.ai)

Technologies

AI/LLMsSQLSchema Processing

Integrations

Tonic.ai PlatformDevelopment PipelinesAI/ML WorkflowsTesting Environments

AI/ML Capabilities

AI-powered engine using LLMs and rules engine for schema-first, natural language, and SQL-driven synthetic relational data generation

Inferred from product description in Tonic.ai acquisition press release; specific technical details not publicly detailed

Use Cases

Software Developers

Fabricate can help accelerate the development of greenfield applications and functional testing with realistic synthetic data when production data is not available.

QA Testing Teams

Fabricate will enable the creation of edge cases, rare scenarios, and high volume test data sets to allow for complete testing of a wide range of scenarios without any risk of violating a customer's privacy.

AI/ML Engineers

Fabricate can be utilized to power the fine tuning of LLMs, train models, and augment data with privacy safe and statistically rich synthetic data.

Healthcare Data Teams

Fabricate can be used to generate HIPAA compliant synthetic data sets for development and analysis in regulated environments.

NOT FORReal-Time Production Systems

It is not acceptable for a live production data replacement where you need to replicate production data exactly.

NOT FORNon-Technical Business Users

Though natural language makes it easier, limited as a stand-alone solution without schema or SQL knowledge of your technology,

Pricing

Pricing information with service tiers, costs, and details
☐Service	$Cost	ℹDetails	🔗Source
Pro	$20/mo	Complete coding agent workspace for individuals and small teams. 10M Factory Standard Tokens +10M bonus, dedicated compute, up to 2 team members ($5 per additional), desktop/web/mobile app, cloud & local agents, integrations, analytics dashboard.	factory.ai/pricing
Max	$200/mo	Everything in Pro plus 100M Factory Standard Tokens +100M bonus, 5 seats cap ($5 per additional), early access to new features.	factory.ai/pricing
Enterprise	Custom quote	Everything in Max plus unlimited team members, custom messaging/token limits, advanced repository permissions, enterprise scale codebase analysis, audit logging, SSO/SAML/SCIM, on-premise deployment options, compliance reporting.	factory.ai/pricing

Pro$20/mo

Complete coding agent workspace for individuals and small teams. 10M Factory Standard Tokens +10M bonus, dedicated compute, up to 2 team members ($5 per additional), desktop/web/mobile app, cloud & local agents, integrations, analytics dashboard.

factory.ai/pricing

Max$200/mo

Everything in Pro plus 100M Factory Standard Tokens +100M bonus, 5 seats cap ($5 per additional), early access to new features.

factory.ai/pricing

EnterpriseCustom quote

Everything in Max plus unlimited team members, custom messaging/token limits, advanced repository permissions, enterprise scale codebase analysis, audit logging, SSO/SAML/SCIM, on-premise deployment options, compliance reporting.

factory.ai/pricing

Competitive Comparison

Feature	Fabricate	Make	Tonic.ai	Craftly.AI
Core functionality	AI coding agent workspace	AI automation scenarios	AI data generation	AI content crafting
Pricing (starting price)	$20/mo	10k credits/mo (price varies)	$29/mo	$29/mo
Free tier availability	No (mentioned in competitor)	Yes	Yes ($10 credits)	Yes (trial)
Enterprise features (SSO, audit logs)	Yes (SSO/SAML, audit logging)	Yes (advanced security)	—	Custom
API availability	—	Yes
Integration count	Expanded first-party	Enterprise app integrations	—	100+ frameworks
Support options	Enterprise program	24/7 Enterprise	—	Onboarding/training
Security certifications	Audit logging/compliance	Advanced security features

Core functionality

FabricateAI coding agent workspace

MakeAI automation scenarios

Tonic.aiAI data generation

Craftly.AIAI content crafting

Pricing (starting price)

Fabricate$20/mo

Make10k credits/mo (price varies)

Tonic.ai$29/mo

Craftly.AI$29/mo

Free tier availability

FabricateNo (mentioned in competitor)

MakeYes

Tonic.aiYes ($10 credits)

Craftly.AIYes (trial)

Enterprise features (SSO, audit logs)

FabricateYes (SSO/SAML, audit logging)

MakeYes (advanced security)

Tonic.ai—

Craftly.AICustom

API availability

Fabricate—

MakeYes

Tonic.ai—

Craftly.AI—

Integration count

FabricateExpanded first-party

MakeEnterprise app integrations

Tonic.ai—

Craftly.AI100+ frameworks

Support options

FabricateEnterprise program

Make24/7 Enterprise

Tonic.ai—

Craftly.AIOnboarding/training

Security certifications

FabricateAudit logging/compliance

MakeAdvanced security features

Tonic.ai—

Craftly.AI—

Competitive Position

vs Make

Fabricate is designed to help AI coding agents and full stack application builders for developers while Make is focused on no-code automation applications. The pricing model for Fabricate is based on compute (tokens) while the pricing model for Make is based on credits. However, Fabricate's model allows for more developer-centric usage.

Fabricate for teams developing AI-applications, Make for businesses looking to automate their processes without writing code.

vs Tonic.ai

Tonic.ai has a focus on generating/synthesizing data using AI. While Fabricate offers coding workspaces with frontier models. Tonic.ai charges by token which can reach a maximum of $525/month. Fabricate also offers free credits when signing up. However, Fabricate's Pro-tier is where the paid plan begins.

Tonic.ai for data teams who require synthetic data, Fabricate for software development automation.

vs Craftly.AI

Craftly is focused on creating AI content and has a word-based limit that begins at $29/month. Fabricate is focused on providing coding agents with token-based compute. Craftly has starter-tier plans, however, Fabricate has plans that scale to enterprise-sized codebases.

Craftly for Content Creators, Fabricate for Engineering Teams.

Pros Cons

Pros

Compute with Frontier Models -- Priority access to latest AI models.
Experience of Multiple Platforms -- Desktop/Web/Mobile Apps + Cloud/Local Agents.
Generous Token Allocations -- Bonus 10M+ tokens in Pro Plan.
Features for Team Collaboration -- Analytics Dashboard and Shared Seats.
Scalability for Enterprise -- SSO, Audit Logs, On-Premise Options.
Early Access to New Features -- In Max Plan for Faster Innovation.

Cons

There is no free tier for this service -- Requires paid signup like all other services with free credits.
Limits via Token-Based -- Shared Across Models May Limit Heavy Usage.
Costs For Additional Seat -- $5 Per Extra User Adds Up for Teams.
Only Custom Enterprise -- Transparent Large Scale Pricing Is Not Offered.
Factory Standard Tokens -- Value Unclear Exactly Across Different Models.
There are limited seating options available in the lower levels of the tiered seating system — Pro max 2 and Max max 5 (without additional tickets)

Best For

Individual developers and small teams — A pro plan at $20 per month offers a full coding workspace with tokens, as well as multi-platform access
Growing engineering teams — A max plan is designed to provide increased capacity and allow for collaboration via team seats
Enterprises building AI software — Custom limit options, single sign-on (SSO), audit logs, and on-premises options are all provided by the platform to support scalability and regulatory requirements
Teams needing multi-platform agents — Desktop/web/mobile plus cloud/local execution support is also offered to enable flexible development environments

Not Suitable For

Budget-conscious beginners — Free plans are not currently available. Consider either Tonic.ai (with $10 free credits) or Make’s free plan if you’re looking for a low-cost solution
Non-technical automation users — Coding agent technology is primarily used for developer-focused solutions. Use Make for no-code focused projects
Content or data generation only — While specialized for coding purposes, consider exploring Craftly.AI for content creation needs or Tonic.ai for data synthesis needs

Limits Restrictions

Tokens Pro: 10M Factory Standard Tokens +10M bonus/mo, shared across models
Tokens Max: 100M Factory Standard Tokens +100M bonus/mo
Team Seats Pro: Up to 2 ($5 per additional)
Team Seats Max: 5 cap ($5 per additional)
Team Seats Enterprise: Unlimited
Enterprise Custom Limits: Custom messaging and token limits

Security & Compliance

SSO IntegrationSingle Sign-On support for Enterprise plan.

SAML/SCIM ProvisioningEnterprise provisioning options available.

Audit LoggingActivity trails and compliance reporting for Enterprise.

Advanced Repository PermissionsEnterprise scale codebase permissions.

On-Premise DeploymentDeployment options for Enterprise customers.

Customer Support

Channels

Available via signupAgent-readiness Improvement Program for Enterprise

Specialized: Enterprise-specific automation cookbook and dedicated program
Business Tier: Enterprise features including dedicated support elements

Api Integrations

API Type: REST API at https://fabricate.tonic.ai/api/v1 for managing databases and generating data
Authentication: API key required for all requests
Webhooks: Not mentioned in available documentation
SDKs: Official Python library (github.com/TonicAI/tonic-fabricate-python), NPM client package (@fabricate-tools/client)
Documentation: Available at docs.tonic.ai/fabricate/fabricate-api-and-cli; covers data model, database management, data generation, workflows
Sandbox: Free cloud tool available for testing data generation
SLA: Not specified in public documentation
Rate Limits: Not specified in public documentation
Use Cases: Manage databases (create/update/retrieve), generate synthetic test data, run Data Agent workflows, load data into databases

Faq

What is the Fabricate API used for?

Fabricate API manages databases and creates synthetic data that can be utilized during testing and development. The Fabricate API uses both rule-based databases and AI-powered Data Agent work flows to generate realistic data from scratch.

How do I authenticate API requests?

An API key is required for each API call. Include your API key in your authentication header for each request made to https://fabricate.tonic.ai/api/v1 endpoints.

What SDKs are available for Fabricate?

Fabricate has a python library located at github.com/TonicAI/tonic-fabricate-python, and an NPM client package located at @fabricate-tools/client. Examples of how to utilize the Fabricate API can be found in the fabricate-api-examples GitHub repository.

Can Fabricate generate data for any database schema?

Yes, Fabricate will generate realistic synthetic data based off of your schema using AI Data Agents or rule-based configurations. In addition, Fabricate can provide value to your data by utilizing uploaded real samples across various value types and industries.

Is there a free version or trial?

Fabricate provides a cloud-based tool for creating data for free. For enterprise clients, Fabricate provides rule-based database capabilities in addition to advanced capabilities.

What formats can I export generated data to?

Fabricate allows for data export into a variety of formats such as loading to a database, and/or unstructured file populated with data created from the generated data.

How is Fabricate different from traditional test data tools?

Unlike other tools which rely on the existence of data, Fabricate creates data from scratch using AI. As a result, it can handle new schemas for features/product testing that do not have access to production data.

Where can I find API examples?

To see how to manage a database, generate data and execute a workflow, check out the Fabricate-API-Examples repository on GitHub.

Expert Verdict

Fabricate from Tonic.ai has an advantage in generating synthetic data for development and testing purposes with Data Agents based on Artificial Intelligence (AI) along with rule-based options. For companies that need realistic test data without having to have direct access to their production environment, Fabricate is a strong choice, although there are some holes in the documentation about Service Level Agreements (SLAs) and rate limits. The fact that it has a strong Software Development Kit (SDK), makes it well positioned for executing automated programs as part of a larger workflow.

Development teams looking to create synthetic test data for their new features
Quality Assurance (QA) Engineers who need synthetic data that is realistic and does not contain Protected Health Information (PII)
Companies that need to build mock APIs for Frontend/Backend development
Teams that work with custom Database schema's

!
Use With Caution

Enterprise teams that require detailed SLA/Uptime Guarantees
High volume production data generation scenarios
Organizations that require extensive Webhook support

Not Recommended For

General purpose automation platforms
Real time supply chain optimization
Manufacturing Execution Systems
Teams that require numerous SaaS Integrations that go far beyond just data generation

Expert's Conclusion

Fabricate is best for developers/testers that require synthetic data quickly and realistically; however, be sure to confirm your company’s requirements for enterprise grade prior to implementation.

Best For

Development teams looking to create synthetic test data for their new featuresQuality Assurance (QA) Engineers who need synthetic data that is realistic and does not contain Protected Health Information (PII)Companies that need to build mock APIs for Frontend/Backend development

Research Summary

Key Findings

Fabricate is an artificial intelligence (AI) powered synthetic data generation solution from Tonic.ai for testing/development and provides both Data Agent Chat and Rule-Based Databases. Includes a REST API with SDKs for Python/NPM for managing databases and generating data as part of a workflow. Is intended for developing/testing scenarios rather than real time operational supply chain solutions.

Data Quality

Good - detailed documentation from Tonic.ai docs and GitHub repositories. Note: Category mismatch (tool is data generation, not supply chain/manufacturing). Limited info on SLAs, rate limits, pricing.

Risk Factors

Misaligned product categories relative to supply chain customer expectations

Detailed enterprise features require contact with Sales Team

No publicly available SLA/uptime information

Gaps in documentation regarding Webhooks/Rate Limits

Last updated: February 2026

Additional Info

GitHub Resources

The active repositories that provide working code examples for integration are: • tonic-fabricate-python (Python SDK) • fabricate-api-examples (examples of using the API) • fabricate-tools/client (NPM package)

Free Tier Availability

Fabricate also provides a free cloud service to generate basic data; this allows individual developers or small groups of developers to try the service without any obligation to commit to purchasing.

Data Agent Workflows

An AI-driven conversational interface allows users to define and refine their data sets in a conversation. This is combined with rule-based configurations to enable the high degree of precision required in an enterprise environment.

YouTube Tutorials

Video demonstrations include creating mock APIs with Fabricate databases as well as the typical data generation workflows for developers.

Alternatives

•
Tonic Textual: Tonic.ai’s sister product for anonymizing existing production data. It works in conjunction with Fabricate by anonymizing real data versus creating new synthetic data. For example, if your production database contains a large amount of Personal Identifiable Information (PII) you would likely want to use anonymize to de-identify your data. (tonic.ai)
•
Mockaroo: Another popular synthetic test data generator with a very simple user interface (UI)/Application Programming Interface (API) compared to Fabricate. However, Mockaroo has many more formats and schemas available. Therefore, it would be better suited for one-off test data requirements. (mockaroo.com)
•
Synthetic Data Vault (SDV): An open-source library for generating tabular synthetic data. It is highly customizable for data scientists. However, the user will need to have strong Python programming skills. It is best for Machine Learning (ML) teams who require statistical fidelity. (sdv.dev)
•
Databook: An AI test data platform with schema inference. It uses a similar Data Agent approach as Fabricate. However, it includes much broader enterprise compliance capabilities. It may be a good choice for companies subject to strict regulatory requirements and requiring audit trail functionality. (databook.ai)
•
Gretel.ai: A commercial enterprise synthetic data platform that is designed to create private preserving ML datasets. It includes more sophisticated statistical models than Fabricate. It would be a good choice for companies whose primary focus is achieving a balance between data utility and data privacy. (gretel.ai)

AI-Driven Supply Chain Performance Improvements

30-50 %

Forecast Error Reduction

20-30 %

Inventory Carrying Cost Reduction

15-25 %

Production Throughput Increase

10-20 %

Operational Cost Reduction

5-20 %

Logistics Costs Reduction

Supply Chain Manufacturing KPI Benchmarks

On-Time Delivery Performance: 95%+
Perfect Order Rate (POR): 90%+
Inventory Turnover Ratio: 5-10 cycles
Production Cycle Time: Minimized by SKU
Cash-to-Cash Cycle Time: Minimized by product

AI & Machine Learning Capabilities

Neural Networks

Demand Pattern Recognition using Deep Learning and Multi-Variate Forecasting

Reinforcement Learning

Adaptive Decision-Making for Dynamic Inventory and Pricing Adjustments

Natural Language Processing

Automated Analysis of Supplier Communications and Order Processing

Agentic AI

Autonomous Agents for Procurement, Logistics Orchestration, and Real-Time Adaptation

Predictive Analytics

The ability to make predictions (forecasting) of demand; to score risks; to detect anomalies in data;

Computer Vision

The quality of manufactured products and defects in manufacturing using inspection and detection;

AI Use Cases Across SCOR Supply Chain Framework

SCOR Phase	Primary Use Cases	Key Business Outcomes
PLAN	Demand forecasting, Inventory optimization, Supply risk mitigation, Scenario planning	30-50% forecast accuracy improvement, 20-30% inventory cost reduction
SOURCE	Automated procurement decisions, Supplier selection optimization, Supplier performance prediction	Faster sourcing cycles, improved supplier reliability
MAKE	Quality assurance automation, Warehouse management optimization, Predictive maintenance, Production scheduling	15-25% throughput increase, Reduced defects and downtime
DELIVER	Route optimization, Dynamic pricing, ETA prediction, Last-mile automation	Faster delivery times, improved customer satisfaction
RETURN	Automated returns processing, Reverse logistics optimization, Customer feedback analysis	Reduced return costs, improved recovery rates

Enterprise System Integration & Data Unification

ERP System Connectors

Built-in connectors for connecting to other software solutions such as SAP IBP, Oracle Cloud SCM, Microsoft Dynamics 365, NetSuite;

Manufacturing Execution System (MES) Integration

The capability to ingest data into the system in real-time from production, and provide feedback to the scheduling process;

Warehouse Management System (WMS) Connectivity

Visibility to all inventory, and the automated optimization of where items are placed or taken from inventory (put away), and the automation of inventory movements;

Transportation Management System (TMS) Integration

The ability to optimize routes, and view logistics in real time;

Data Layer Unification

The consolidation of data that has been stored in separate silos including procurement, production, inventory, and logistics;

Real-Time Data Streaming

Support for ingesting continuous data streams, and for API-based connectivity to older systems;

Supply Chain Security & Compliance Requirements

Data Encryption (at rest & in transit)TLS 1.2+, AES-256

Role-Based Access Control (RBAC)Granular permissions by function and organization level

SOX (Sarbanes-Oxley) ComplianceFinancial reporting controls and audit trails

ISO 9001 (Quality Management)Critical for manufacturing operations

ISO 27001 (Information Security)ISMS certification

GDPR ComplianceRequired if handling EU supplier/customer data

AI Model GovernanceExplainability, bias detection, and performance monitoring

Audit Logging3-7 year retention for compliance

Implementation Timeline & ROI Benchmarks

Total Implementation Duration: 6-18 months
ROI Breakeven Period: 6-12 months
Phase 1: Assessment & Data Integration: 2-4 months
Phase 2: Model Training & Validation: 1-3 months
Phase 3: Pilot Deployment: 2-4 months
Phase 4: Full Rollout & Optimization: 1-7 months
Typical First-Year Cost Savings: 10-20% operational reduction
Minimum Historical Data Required: 2-3 years

Supply Chain AI Vendor Evaluation Checklist

Benchmark Performance Documentation

Has the vendor demonstrated a 30-50% improvement in forecast accuracy with similar data from other industries? Ask for case studies, and ask for specific metrics used in the study.

Integration Scope & Pre-Built Connectors

Which ERP/MES/WMS/TMS systems do you have native connectors for? Ask for an architecture diagram of your integration.

Model Customization & Training

Can the algorithms for forecasting and decision-making be customized for use with different industries and SKUs? Ask about the process for customizing, and the estimated time it will take to customize.

Data Requirements & Quality Standards

How much historical data is needed? What is the minimum number of SKUs and transaction records that need to be inputted? Is there support for data cleansing?

Compliance Certification & Audit Reports

Ask for documentation of how the vendor supports compliance with regulations such as SOX, ISO 9001/27001, GDPR. Ask for documentation of any recent audits by third parties.

Scalability & Performance SLAs

Are there concurrent user limits? Are there limits on how many API calls can be made per minute? Does the vendor support multiple sites? Are there options for data residency?