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Moralis
MetaMask.io
Phantom
Ethereum
One Click Crypto: AI + DeFi
thirdweb is an ecosystem of SDKs, dev tools, and dashboards that help teams build and manage web3 apps. Deploy custom or pre-built contracts to ETH, MATIC, AVAX, & more.

Mockaroo
Generate Data
Snaplet
Random Data
TestDataHub
Turn SQL schemas into realistic test data in seconds. Perfect for testing, demos, and development.

Which is more popular?
Based on our record, thirdweb seems to be more popular. It has been mentioned 30 times since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | thirdweb.com | ddltodata.com |
| Pricing | ||
| Company | 2021 | Startup from the United States · 2025 |
| Listed in |
In their own words, as submitted to SaaSHub.


⛏️ Build NFTs, DAOs, marketplaces, tokens, and more. Leverage ERC721A or ERC1155, incorporate features such delayed reveal, signature mint, and token-gating. Deploy custom smart contracts seamlessly & safely. Aggregate all your contracts in an on-chain registry using Release. Leverage web3...
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...
What each product offers, as listed by its team.


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


Overall verdict
Why this product is good
Recommended for
Overall verdict
Why this product is good
Recommended for
Walkthroughs and reviews on video.
How to Get Started with Web 3.0 | thirdweb review
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How often each product is chosen within a category, 0–100% relative to the other.


As answered by people managing thirdweb and DDL to Data.
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.
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.
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.
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
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.
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.
Share your experience with using thirdweb and DDL to Data. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


Winners will be selected by panels of experts supported by their respective teams: Paul Gadi (OP Guild and Arcadia), Andrew Cooper (Avalanche), and Juan Rivera Perez (Thirdweb). - Source: dev.to / about 2 years ago
The burn process is fully on-chain and performed by the Burn To Earn smart contract. This simple contract has been audited by Thirdweb and will be made public later on. - Source: dev.to / over 2 years ago
I also added a Web3 wallet from ThirdWeb so users in the future will be able to connect their Bat Cowls to the application. Great integration and has the ability to specify wallets and restrict usage to particular blockchains. - Source: dev.to / over 2 years ago
Tracking DDL to Data since Dec 2025.
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