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RandomAPI VS DDL to Data

Compare RandomAPI VS DDL to Data and see what are their differences

RandomAPI

Easily generate fake data for populating your mockups and testing your applications.

Rating
0 reviews
Alternatives Is it good?
DDL to Data

Turn SQL schemas into realistic test data in seconds. Perfect for testing, demos, and development.

Rating
0 reviews
Pricing
$19 / Monthly (500 API calls/month 50,000 rows/month)
Alternatives Is it good?
HTMLCSS to Image API
Capture website screenshots, render HTML/CSS, or create templated graphics. Render images or PDFs. Use MCP with AI, no-code automation, or the REST API. No browsers needed. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, RandomAPI seems to be more popular. It has been mentioned 1 time since March 2021.

social mentions
1 vs 0
APIs popularity
100% vs 0%
alternatives listed
8 vs 5

Base details

Website, pricing, platforms and company facts side by side.

RandomAPI
DDL to Data
Website randomapi.com ddltodata.com
Pricing
Official pricing
$19 / Monthly (500 API calls/month 50,000 rows/month) Official pricing
Company — Startup from the United States · 2025
Listed in
APIs Random Generator Database Tools Geolocation +1
Developer Tools API Tools QA Tools Testing Test Data Generator +2

About RandomAPI and DDL to Data

In their own words, as submitted to SaaSHub.

RandomAPI
DDL to Data

No description of RandomAPI yet.

DDL to Data is a developer tool that automatically generates realistic test data from SQL schemas. Simply paste your CREATE TABLE statement and get back JSON data with smart type detection—column names like "email" produce real email formats, "phone" produces phone numbers, etc. It supports...

Read more about DDL to Data

Features and specs

What each product offers, as listed by its team.

RandomAPI 0 features
DDL to Data 6 features

No features have been listed yet.

  • Smart Type Detection
    Generates realistic data based on column names (email, phone, address, etc.)
  • Database Support
    PostgreSQL, MySQL, SQLite
  • Foreign Key Support
    Generates consistent, referentially-intact relational data
  • Response Format
    JSON, SQL, CSV, PARQUET, XLSX
  • Free Tier
    100 API calls + 5,000 rows/month
  • Schema Storage
    Save and reuse schemas for repeat generation

Analysis

An editorial look at what each product does well and who it suits.

RandomAPI
DDL to Data

Overall verdict

  • RandomAPI is a solid, reliable tool for generating realistic mock and test data quickly, making it a good choice for developers who need placeholder datasets without building their own generators.

Why this product is good

  • Generates realistic random data such as names, emails, addresses, and phone numbers on demand
  • Offers customizable data formats including JSON, CSV, XML, and more
  • Free tier available for basic usage, making it accessible for testing and prototyping
  • Simple API interface that is easy to integrate into development workflows
  • Supports templating and custom lists for more tailored data generation

Recommended for

  • Developers needing mock data for testing and prototyping
  • QA engineers populating databases with sample records
  • Front-end developers building UI demos with placeholder content
  • Students and hobbyists learning API integration
  • Teams that want quick fake data without writing custom generators

Overall verdict

  • I don't have verified, up-to-date information about ddltodata.com specifically, so I can't confirm its quality, reliability, or legitimacy. Based on the name, it appears to be a tool that generates sample/mock data from DDL (Data Definition Language) schema statements, but I have no firsthand or sourced data on user reviews, security practices, or company reputation for this exact site.

Why this product is good

  • Tools that convert DDL schemas into sample data can be useful for quickly populating test databases without manual data entry
  • If it works as implied by the name, it could save developers time during testing, prototyping, or QA environments
  • Such tools often support multiple SQL dialects (e.g., MySQL, PostgreSQL, SQL Server), which can be convenient for cross-platform projects
  • Web-based converters typically don't require software installation, lowering the barrier to entry for quick tasks

Recommended for

  • Developers or QA testers needing to quickly generate mock data for database testing
  • Small teams prototyping database schemas who want sample data without writing custom scripts
  • Users who have verified the site's legitimacy and reviewed its privacy/security policies for handling any schema information
  • Anyone willing to independently confirm the tool's accuracy and data handling practices before uploading real schema files

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
RandomAPI
DDL to Data
100% 100%
APIs
0% 0%
0% 0%
QA Tools
100% 100%
100% 100%
Random Generator
0% 0%
0% 0%
API Tools
100% 100%

Questions & Answers

As answered by people managing RandomAPI and DDL to Data.

What makes your product unique?

DDL to Data's answer:

No LLM, no prompts, no AI costs. DDL to Data uses deterministic pattern-matching — not machine learning — to generate realistic test data from your SQL schema in milliseconds. It's fast, predictable, and won't hallucinate. Column named "email" produces an email, "phone" produces a phone number. Same schema, same structure, every time. Plus it handles foreign key relationships to generate referentially-intact data across multiple tables.

Why should a person choose your product over its competitors?

DDL to Data's answer:

Unlike AI-powered tools, DDL to Data has zero token costs, sub-second response times, and deterministic output, critical for CI/CD pipelines. Unlike Faker libraries, it requires zero configuration: paste your CREATE TABLE and get intelligent, type-aware data without writing any setup code. It also supports multiple output formats (JSON, CSV, SQL, Parquet, Excel) and can seed data directly into your PostgreSQL database.

How would you describe the primary audience of your product?

DDL to Data's answer:

Backend developers, QA engineers, and DevOps teams who need realistic test data for database testing, seeding dev environments, CI/CD pipelines, and product demos. Particularly useful for teams who want a reliable, no-config utility that just works, without adding AI dependencies to their infrastructure.

What's the story behind your product?

DDL to Data's answer:

Every new project meant the same tedious ritual: write the schema, then manually create arrays of fake emails, phone numbers, and timestamps. Over and over. It struck me that the schema already contains everything needed to generate realistic data, column names are semantic. "email" means email, "created_at" means timestamp. So I built an API that does the obvious thing automatically, without any AI complexity

Which are the primary technologies used for building your product?

DDL to Data's answer:

FastAPI (Python) backend with PostgreSQL and SQLAlchemy. Next.js 14 frontend with TypeScript and Tailwind CSS. Hosted on AWS with Docker containers, and CircleCI for CI/CD.

Who are some of the biggest customers of your product?

DDL to Data's answer:

Currently in public beta and growing organically. Early adopters include indie developers and small engineering teams using it for local development and automated testing pipelines.

User comments

Share your experience with using RandomAPI and DDL to Data. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

RandomAPI 1 mention
DDL to Data 0 mentions
  • 4 free APIs you should know about
    When developing an application, you have to implement user authentication. This is where randomapi.com comes in. The API provides you with information about random fake users. This includes names, genders, emails, phone numbers, and... - Source: dev.to / about 4 years ago

Tracking DDL to Data since Dec 2025.

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