DEX vs CEX: Choosing the Best Solution for Your Crypto Exchange

Maciej Zieliński

08 Nov 2021
DEX vs CEX: Choosing the Best Solution for Your Crypto Exchange

What are the differences between DEX and CEX? Which one suits your needs and business assumptions best? Read this article before you make that crucial decision.

In the article you learn about:

  • Different types of crypto exchanges
  • What is a centralized exchange?
  • Pros and cons of centralized exchanges
  • How does decentralized exchange work?
  • Liquidity pools and Automatic Market Maker
  • Pros and cons of decentralized exchanges

Crypto Exchanges

The cryptocurrency exchange is an indispensable part of cryptocurrency trading, which may be considered as one of the most important financial trends of this century. Hence more and more investors are becoming interested in launching their own exchange. The fundamental decision that has to be made at the begging is choosing the model of crypto trading that will meet our requirements. 

In this article, we'll take a closer look at centralized and decentralized exchanges, which will hopefully enable you to examine which of them is right for your project in the crypto space.

Centralized Exchange
Centralized Exchange

Centralized Exchange

Most big cryptocurrency exchanges you know are probably centralized. Coinbase, Binance, BKEX, or Upbit - you are familiar with them. Thanks to higher liquidity and lower transaction fees they remained the first choice of crypto traders for a long time.

Times have changed and players such as decentralized exchange Uniswap or Sushi Swap have gained significant importance in the crypto market. Yet, in many cases, centralized exchanges still remain the most suitable solution. What's their backbone?

Centralization of an exchange

Centralization of the exchange essentially means that each trade there takes place with a centralized intermediary. The centralized exchange has its order book, where every order is recorded and validated. All the data is stored and exchanged between exchange servers. Additionally, transactions and user information go through a centralized security process.

To access the exchange, users have to sign up by providing their bank details and personal data. This is the part of KYC and AML practices, which aim to prevent money laundering and have to be followed by every centralized exchange. 

Buying cryptocurrencies on a centralized exchange

Trading on centralized exchanges generally seems simple. You just need to choose coins and confirm the transaction. The exchange will show the funds you acquired in your account. Then you can trade them for other digital assets. The price of each coin on the site is based on an order book.

However, users don't really hold their funds. The exchange works here as a custodian of the customer’s funds. Furthermore, trades don't occur on the blockchain. Instead, they take place only within the exchange's database.

Centralized exchange - pros and cons
Centralized exchange - pros and cons

Pros of Centralized Exchanges

User-friendliness

Generally, centralized exchanges have a more user-friendly platform. They give users easy and uncomplicated access to crypto trading. This is especially relevant for crypto newcomers.

High liquidity

Since the whole process doesn't take place on a blockchain, the network nodes don't have to be updated in real-time, hence trading speed is usually much higher than on DEX.

Lower transaction fees

The trading fee on CEX is fixed, and they tend to be lower than on decentralized exchanges for the same reason as stated above.

Trading with fiats

Unlike DEXs, centralized exchanges allow users to buy crypto for fiat currencies.

Cons of Centralized Exchanges

More legal regulations

Centralized exchanges are limited by strict government requirements. For example, high standards of the identity verification process have to be respected.

Centralized entity

From a technical point, when you deposit funds on a centralized exchange, you lose control over it. The exchange puts your funds into integrated wallets controlled by it.

Additionally, CEXs extract users' private keys. They will no longer be your keys. Therefore, if you want to withdraw your money, the exchange has to sign the transaction on your behalf.

This is an important reason why many traders migrate to decentralized exchanges.

Risk of leaking private user data

Due to KYC procedures, crypto users provide extensive data. As such information is fragile, a security dilemma is present even before one starts trading. This may lead to the lack of users’ trust, especially when we talk about smaller, less-known exchanges.

Decentralized Exchange
Decentralized Exchange

Decentralized Exchange

In many ways, decentralized exchanges are similar to centralized ones. However, the differences are more than substantial. In decentralized exchanges, trade essential relies on a blockchain (most often Ethereum or the Binance Smart Chain). Trading between users is conducted using smart contracts - orders are executed on-chain. As a result, the exchange doesn't take control of users' assets during the entire process.

Cross-chain exchanges are a very promising novelty on the DEX market. Yet, most of them operate only on one blockchain - most often Ethereum or the Binance Smart Chain.

How can a decentralized exchange handle trading?

How DEX handles trading
How DEX handles trading

On-chain order book

There are decentralized exchanges where every transaction is written into a blockchain. This means that every order, as well as cancellation or alteration, is handled on-chain.

Without a doubt, this is the purest approach to decentralization. There is absolutely no third party involved at any stage of trading. Everything is extremely transparent. Unfortunately, there are vital downsides as well.

The on-chain order book is far less practical than the other two options. Firstly, because every node on the blockchain records the order, placing it requires paying a fee. Furthermore, users have to wait until the miner adds necessary data to the chain. This translates to high costs and poor liquidity.

Off-chain order book

Off-chain order books are a bit more centralized than their counterparts. But they are also far more practical. In this model, orders are hosted elsewhere and only the final transaction is settled on the blockchain. Moreover, you can still benefit from non-custodial storage.

As orders aren't stored on-chain, this approach is faster and less costly. Furthermore, it helps a better liquidity of trades to be achieved. However, it can encounter some of the security issues typical for CEXs.

Trading pairs 

Let’s use the example of Ether and Bitcoin to describe how trading pairs work in the order book model on DEX

If users want to trade their ETH for BTC, they need to find another trader willing to sell BTC for ETH. Furthermore, they need to agree on the same price. 

While in the case of popular cryptocurrencies and tokens, finding a trading pair shouldn’t be a problem, things get a bit more complicated when we want to trade more alternative assets. 

The vital difference between order books and automatic market makers is that the second one doesn’t require the existence of trading pairs to facilitate trade.

Automatic Market Maker AMM

Automated Market Maker (AMM) is a decentralized exchange protocol that relies on smart contracts to set the price of tokens and provide liquidity. In an automated market makers' model, assets are priced according to a pricing algorithm and mathematical formula instead of the order book used by traditional exchanges.

Essentially, they are autonomous trading machines that replace traditional order books with liquidity pools run by algorithms. According to many, it was the development of AMM that enabled the mass adoption of decentralized exchanges.

Dentralized exchange - pros and cons
Dentralized exchange - pros and cons

Pros of Decentralized Exchanges

No KYC

Most of the DEXs don’t have to follow KYC and AML requirements, because they don’t intermediate in transactions between parties. That’s why it’s often more convenient to build your own DEX than CEX. 

No third parties involved

The basic idea behind decentralized trading is removing third-party providers. All transactions take place in a peer-to-peer or peer-to-contract model. 

Full control of your assets

A decentralized exchange doesn't hold the user's assets or private keys. Therefore funds are under the user's control at every stage of trading.

More diversified crypto assets

On the DEX platform, trades of tokens that aren’t listed on CEXs are possible.

Cons of Decentralized Exchanges

Trading volume

The volume traded on CEXs is still much higher than that on DEXs. Liquidity is lower as well.

Higher fees

This is not an absolute standard, but when it comes to trading fees CEXs often offer better prices.

Convenience

A decentralized exchange is less user-friendly than a traditional one.

DEX vs CEX
DEX vs CEX

Developing DEX vs CEX

Crypto space develops at a tremendous speed, continuously bringing new possibilities. The number of people interested in crypto investments increases every day; hence the popularity of both decentralized and centralized exchanges has recently grown significantly. You need to bear this in mind when deciding to launch your crypto exchange.

Launching a crypto exchange requires not only experience of blockchain developers’ fluently using blockchain protocols but also a solid and well-planned business strategy. That's why choosing a technology partner with previous experience with both blockchain development and business consulting in the crypto field might be the optimal solution.

Do you want to gain more first-hand knowledge regarding building crypto exchanges? Don't hesitate to ask our professionals who will be happy to answer your questions.

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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series