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Best Programming Languages to Learn

Best Programming Languages to Learn
Hostman Team
Technical writer
Infrastructure

Anyone who decides to get into programming faces an important question: which language should they learn first? For a beginner unfamiliar with software development's nuances, trying to answer this alone can easily lead to mistakes.

Choosing hastily comes with a risk, as you might fall into the sunk cost trap. You could end up picking a language that doesn't align with your desired direction, whether it's mobile development, game dev, or systems programming. Relearning another language later may render much of your time and effort wasted.

So, it is of course much better to make the right decision early. And for this, you need at least a general understanding of how each language works: its specifics, features, areas of application.

In this article, we’ll present both complex and beginner-friendly programming languages, to help  beginners make an informed choice.

Why It’s Important to Choose the Right Programming Language

There are several reasons why it's crucial to study the features of each language at the very beginning and pick the most suitable one:

  • Task suitability. Every language has its own strengths. One might be better suited for a specific set of tasks than another. Your chosen development field heavily affects your tech stack, especially in today's world, where specialization is becoming more prominent. The days when operating systems, programs, and games were all written in one language are gone. Now, there's a tool for everything.

  • Community support. Any popular programming language has an active community, extensive documentation, and plenty of frameworks and libraries. However, more complex languages (like C++) can be harder to work with regarding libraries and documentation. You should take this into account.

  • Career prospects. Learning a high-demand language opens more job opportunities and makes it easier to find projects that align with your interests and specialization.

  • Scalability and performance. Some tasks require special features from a language, like efficient memory management or support for parallel computing. Sometimes, these factors are critical.

So, clearly understanding which language to start learning can help avoid many future issues, and at best, guide you into an exciting direction and a successful developer career.

Python: The Best Starting Point

Python is a high-level, interpreted programming language with dynamic typing.

Dynamic typing means the variable type is determined at runtime and can change. This adds flexibility but increases the chance of errors. Static typing means a variable's type is set at compile time and can't change. Type errors are caught earlier.

For example, in a dynamically typed language, you could first assign the number 7 to a variable and later assign a string like "Hello, World" to that same variable. In a statically typed language, this would cause a compile-time error.

Interpreted languages execute code directly without first converting it to machine code. Compiled languages, on the other hand, convert high-level code into machine instructions, making them generally faster.

Python was initially created by Dutch programmer Guido van Rossum in 1991. Today, it is maintained by the global Python Steering Council and the nonprofit Python Software Foundation.

Simple Syntax

Python’s key feature is its use of indentation and colons instead of curly braces to define code blocks:

if True:
	print("One block of code")
else:
	print("Another block of code")

This simplifies the language and makes the code more visually readable, especially in Object-Oriented Programming:

class Butler:
	def __init__(self, name):
		self.name = name

	def hello(self):
		print(f"The butler of this mansion welcomes you — {self.name}!")

butler = Butler("Alfred")
butler.hello()
# Output: The butler of this mansion welcomes you — Alfred

Python aims to be both clear and functional, using as few syntax elements as possible (like braces or semicolons).

Versatile Applications

Thanks to its clean syntax and line-by-line execution, Python can be used in a wide variety of fields:

  • Web Development. Building the backend of web apps, handling user requests (RESTful APIs), and generating dynamic web pages.

  • Machine Learning. Processing and analyzing large datasets, building ML models, and creating neural networks. It’s also widely used in scientific computing across physics, biology, and engineering.

  • Automation. As a scripting language, Python is used to automate routine tasks, manage servers, and streamline DevOps workflows.

Despite its power and use in large-scale infrastructure and serious applications, Python remains the most beginner-friendly programming language.

Large Community and Documentation

Python is used globally across industries and research, resulting in a massive community of developers, engineers, and scientists.

Regular conferences like PyCon, EuroPython, and PyData foster idea-sharing and collaboration.

Online platforms like StackOverflow and Reddit host extensive discussions on Python coding nuances.

The official documentation provides detailed language syntax, standard libraries, and step-by-step guides with examples, covering even the most basic topics.

JavaScript: The Language of Web Development

JavaScript is a high-level, interpreted programming language with dynamic typing. It was developed in 1995 by Brendan Eich at Netscape.

Its name's similarity to Java was a marketing decision rather than a technical one. Java was extremely popular at the time, and the name helped boost interest in the new language.

The Core of Frontend Development

Modern browsers come with a built-in JavaScript engine to run scripts that manipulate the DOM (Document Object Model) to dynamically change a web page’s content:

<!DOCTYPE html>
<html lang="en">
<head>
	<meta charset="UTF-8">
	<title>DOM Manipulation</title>
</head>
<body>
	<div id="container">
		<p>This is the original text.</p>
	</div>
	<button id="changeBtn">Change Content</button>

	<script>
		const container = document.getElementById('container');
		const button = document.getElementById('changeBtn');

		button.addEventListener('click', function() {
			const firstParagraph = container.querySelector('p');
			firstParagraph.textContent = "Text changed!";

			const newParagraph = document.createElement('p');
			newParagraph.textContent = "A new paragraph was added to the DOM.";
			container.appendChild(newParagraph);
		});
	</script>
</body>
</html>

Thanks to JavaScript, developers can create interactive UIs for modern web apps. Scripts run directly in any browser, so no extra software is needed.

This makes JavaScript one of the most accessible programming languages for beginners.

A Rich Ecosystem

Web development with JavaScript is a whole industry on its own. There are countless libraries and frameworks for managing web app states, such as React and Vue.

But JavaScript isn’t limited to the client side. With Node.js, JavaScript can also run on servers.

That's why many JavaScript applications and libraries are isomorphic, meaning they work both on the front and backend.

Because of this flexibility, JavaScript is a solid first programming language, helping you become a Full Stack developer (handling both frontend and backend).

Java: Stability and Demand

Java is a high-level, object-oriented programming language with static typing.

It was developed in 1995 by Sun Microsystems (later acquired by Oracle), led by James Gosling.

Java is a compiled language. Its source code is compiled into intermediate bytecode, which is executed by the Java Virtual Machine (JVM).

Since JVMs are implemented for different operating systems, Java code is cross-platform and can run on any OS without recompilation. That’s why Java’s slogan is: "Write once, run anywhere."

Android Development

Android is an OS with many components written in different languages. While its kernel is in C and C++, app development libraries and APIs are Java-based.

This has made Java almost synonymous with mobile development, including both apps and games.

For example, the popular game Minecraft was written in Java and, almost immediately after its PC release in 2011, was added to the Google Play Market as a mobile version for Android.

High Performance

Unlike interpreted programming languages, Java uses JIT (Just-in-Time) compilation.

When an application is run, the bytecode is dynamically compiled into machine code so that frequently used code segments are optimized on the fly.

On one hand, Java delivers higher performance than interpreted languages, such as JavaScript or Python.

On the other hand, the indirect execution of bytecode is slower than direct execution of machine instructions in compiled languages like C or C++.

Java is quite a fast language, especially considering that it runs through a virtual machine to provide strong cross-platform compatibility.

Popularity in the Corporate Environment

Cross-platform capabilities, application portability, predictable behavior, stability, and security are key reasons why many companies prefer Java.

And of course, its rich ecosystem—libraries, frameworks, and tools—all contribute to simplifying and accelerating enterprise application development, maintenance, and updating.

In contrast to Python, Java uses a strict C-like syntax:

public class Greeter {
	private String name;

	// Constructor that takes a name for greeting
	public Greeter(String name) {
		this.name = name;
	}

	// Method that prints the greeting to the console
	public void greet() {
		System.out.println("Hello, " + name + "!");
	}

	public static void main(String[] args) {
		// If a command-line argument is provided, use it as the name. Otherwise, default to "World"
		String name = (args.length > 0) ? args[0] : "World";
		Greeter greeter = new Greeter(name);
		greeter.greet();
	}
}

C#: A Windows Developer's Alternative

C# is a high-level, object-oriented programming language with static typing. However, dynamic typing is also possible using the dynamic keyword.

The C# programming language first appeared in 2002. It was created by Microsoft under the leadership of engineer Anders Hejlsberg. 

Like Java, C# code is not compiled directly into machine instructions but into an intermediate representation called CIL (Common Intermediate Language) or simply IL.

During program execution, the CIL code is converted via JIT compilation into native machine code optimized for the target platform.

Used in .NET Development and Unity

.NET is a development platform created by Microsoft for building portable applications. It can be used to develop websites, mobile apps, games, neural networks, and cloud services.

The .NET ecosystem includes:

  • Programming languages (C#, F#, VB.NET)
  • Libraries and frameworks (for databases, servers, graphics)
  • Runtimes

C# is the main language in the .NET ecosystem.

To some extent, Java and .NET can be seen as similar ecosystems. In Java, apps run on the JVM (Java Virtual Machine), while in .NET, they run on the CLR (Common Language Runtime). In both cases, code is compiled to bytecode, which is then executed on a virtual machine.

Moreover, Java and C# have syntactic similarities, as both are C-style languages.

Naturally, game development has also embraced C#. For instance, the popular Unity game engine uses C# as the primary scripting language for creating gameplay mechanics and scenarios.

Strong Microsoft Support

Microsoft plays a key role in the development of C#. This support includes the language itself, tooling, libraries, and infrastructure.

C# integrates well with other Microsoft products and is tailored to the Microsoft ecosystem, although it remains cross-platform. For example, the Visual Studio IDE is best optimized for the C# compiler.

A simple C# console application looks like this:

using System;

class Program
{
	static void Main()
	{
		Console.Write("May I have your name?: ");
		string name = Console.ReadLine();

		Console.WriteLine($"Welcome, {name}!");
	}
}

C and C++: For Systems Programming

C and C++ are compiled programming languages that are closely related. C++ is an extended, object-oriented version of the procedural C language.

C was created at Bell Labs by Dennis Ritchie in 1972, while C++ was introduced by Bjarne Stroustrup in 1983.

High Performance

Unlike Python, JavaScript, and Java, C and C++ do not require an interpreter or a virtual machine. Their code is compiled directly into processor instructions.

In other words, these languages are as close to the hardware as possible, allowing low-level control of system resources. That’s also why these languages are considered complex—manual control and lack of automation demand high programmer skill.

Memory Management

C and C++ give full control over computing resources. They do not include a garbage collector that automatically frees unused memory.

This reduces overhead but increases the risk of memory leaks.

Due to their performance and control, C and C++ are preferred for high-load computing, like OS kernels (Linux, Windows, macOS, Android), game engines (Unreal Engine), and financial systems.

In short, C and C++ remain the go-to languages when speed and efficiency are critical.

Foundation for Modern Languages

Originally developed for Unix-like OS development, C became the ancestor of many modern languages.

Its syntax is the foundation of many popular languages: C++, Java, C#, JavaScript, Go, Swift.

Example of simple C++ code using classes:

#include <iostream>
#include <string>

class Car {
private:
	std::string brand; 

public:
	Car(std::string carBrand) {
		brand = carBrand;
	}

	void showInfo() {
		std::cout << "Car brand: " << brand << std::endl;
	}
};

int main() {
	Car myCar("Toyota");
	myCar.showInfo();

	return 0;
}

Swift: For iOS Development

Swift is a modern high-level, statically typed language that is compiled into machine instructions.

Apple’s Official Language

Before Swift, Apple’s main language was Objective-C, dating back to the 1980s. Despite its power, it had outdated principles and lacked support for modern syntax and safe memory handling.

In 2014, Apple introduced Swift, a modern, safe, and convenient language aimed at improving code writing, safety, performance, and memory management.

In short, Swift was created as Apple’s new official language for iOS, macOS, watchOS, and tvOS development.

Simplified Syntax Compared to Objective-C

Objective-C:

NSString *name = @"John";
NSInteger age = 25;
NSArray *fruits = @[@"Apple", @"Banana", @"Orange"];

- (void)greet:(NSString *)name {
	NSLog(@"Hello, %@", name);
}

[self greet:@"Alice"];

Swift:

let name = "John"
var age = 25
let fruits = ["Apple", "Banana", "Orange"]

func greet(name: String) {
	print("Hello, \(name)")
}

greet(name: "Alice")

As a result, Swift has cleaner and more understandable syntax, which means faster development.

A Great Choice for Mobile Development

Swift is optimized for Apple’s custom chips. It’s the main language for developing native iOS applications and games.

Apple actively supports and develops the Swift ecosystem, and it is fully integrated into Xcode, Apple’s official IDE.

Go: A Modern Language from Google

Go, or Golang, is a high-level, statically typed programming language designed with concurrency in mind. It was developed in 2007 by Google engineers Robert Griesemer, Ken Thompson, and Rob Pike.

Google created Go to address speed, concurrency, and development convenience issues found in other languages, like:

  • C++: Powerful but complex
  • Java: Bytecode execution is slower than native
  • Python: Simple but slow due to interpretation

As a company focused on cloud services, Google made Go with server-side development in mind.

Simplicity and Minimalism

Go has automatic garbage collection, a simple syntax, and convenient abstractions, but it's not a classical OOP language.

There are no classes, no this keyword, no method/operator overloading.

Instead, Go uses structs with methods attached to them:

package main
import "fmt"

type Person struct {
	Name string
	Age  int
}

func (p Person) Greet() {
	fmt.Println("Hi, my name is", p.Name)
}

func main() {
	person := Person{Name: "Anna", Age: 35}
	person.Greet()
}

Go minimizes complexity and accelerates development by unifying syntax:

  • No classes/inheritance: use structs and interfaces instead.
  • No method/operator overloading: one name per method.
  • No exceptions: use explicit error checks

Exceptions are errors thrown by an application during execution that can be caught and handled by user-written code without terminating the program.

At first glance, such simplification may seem to limit the programmer’s capabilities. However, in reality, a strict definition of application logic provides greater flexibility in possible implementations and solutions.

This is likely why Go, along with Python and JavaScript, is considered one of the best programming languages for beginners.

High Performance

Go code is compiled to machine instructions. The lack of heavy features like overloading and exceptions makes Go programs high-performing.

The garbage collector is optimized for minimal delays.

Instead of OS level threads, Go uses goroutines—lightweight threads that use only a few kilobytes of memory and can be spawned in large numbers.

Great for Server-Side Development

Compiled code, concurrency support, and minimal syntax make Go ideal for backend development.

Built-in packages for web servers, networking, databases, and encoding (like net/http, database/sql, encoding/json) allow out-of-the-box server app development.

Which Programming Language Should You Choose First?

So. When choosing a programming language, several important factors should be taken into account:

  • Development field. In what area will you be working? Web applications, systems programming, game development? And if it’s game development, what kind? Mobile or desktop? Or maybe even console games?

  • Future tasks. The tasks you’ll face will depend on the area of development. But the type of tasks can also vary. For example, web development includes both frontend and backend. In game development, there’s engine development and gameplay mechanics creation.

  • Entry threshold. Depending on personal aptitudes and existing skills, learning a specific programming language will be different for every developer. For instance, effective use of C and C++ requires deeper computer science knowledge: understanding memory management, algorithm complexity, and mathematical computations.

The showcased languages can be compared across several key parameters:

Language

Syntax Complexity

Execution Model

Type System

Year Released

Official Site

Primary Field

Additional Knowledge

Python

Low

Interpretation

Dynamic

1991

python.org

Data Analysis

Machine Learning, Big Data

JavaScript

Low

Interpretation

Dynamic

1995

-

Web Development

Layout, Network Protocols

Java

Medium

Compilation

Static

1995

java.com

Mobile Development

Android, DevOps

C#

Medium

Compilation

Static

2002

microsoft.com

Desktop Development

.NET

C/C++

High

Compilation

Static

1972 / 1985

isocpp.org

Systems Programming

Mathematics

Swift

Medium

Compilation

Static

2014

swift.com

Mobile Development

macOS, iOS

Go

Medium

Compilation

Static

2012

go.dev

Servers, Microservices

RESTful APIs, Containerization

Learning the syntax of a specific language is best done with the help of books. 

You can clarify various details through tutorial articles and videos. And when problems arise during learning or work tasks, you can ask questions on platforms like StackOverflow.

Ultimately, the choice always comes down to personal preference. In the long run, it’s better to pursue something you are actually interested in rather than just something profitable. Otherwise, you'll just burn out.

Infrastructure

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Infrastructure

Best Midjourney Alternatives in 2025

Midjourney is one of the most popular AI networks for image generation. The service has established itself as a leader in the field of generative AI. However, the existence of a paid subscription and access limitations (for example, the requirement to use Discord or lack of support in certain regions) increasingly prompts users to consider alternatives. We have compiled the best services that can replace Midjourney,  from simple tools to professional solutions. Why Are Users Looking for a Midjourney Alternative? Midjourney is a powerful tool, but it has its drawbacks: Paid Access: Since March 2023, Midjourney has fully switched to a paid model, with a minimum subscription of $10 per month, which may be expensive for beginner users. Usage Limitations: A Discord account is required, and for users in some countries, access is restricted due to regional limitations. Complex Interface: Beginners may find it difficult to navigate working through the Discord bot. Fortunately, there are many apps like Midjourney that offer similar functionality and more user-friendly interfaces. We will review seven of the best Midjourney alternatives. For all the AI networks considered, we will generate an image using the following prompt: “Generate an image of the Swiss Alps.” Free Alternatives First, let’s look at Midjourney alternatives that can be used for free. Playground AI Playground AI is an AI network that works on modern generative models, including Stable Diffusion XL, and allows generating images from text prompts or editing existing images. A unique feature of Playground AI is the ability not only to generate an image from scratch but also to refine it within the same interface. Users can correct individual details, replace elements (for example, hands), perform upscaling to increase detail, or draw additional parts of the image on a special working field (canvas) with a seamless continuation of the image. 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Advantages: Free access with a limit (up to 150 tokens per day). Ability to train custom AI models. Wide choice of styles and customization tools. Support for generating textures and 3D objects. Convenient prompt handling: a built-in prompt generator helps beginners formulate queries, while experienced users can optimize prompts for better results. Image generated by Leonardo AI using the prompt “Generate an image of the Swiss Alps” Stable Diffusion Stable Diffusion is a modern text-to-image generation model that uses diffusion model technology. Developed by Stability AI in collaboration with researchers from LMU Munich and other organizations, the model was released in 2022 and quickly gained popularity due to its openness and high efficiency. Stable Diffusion can be accessed through many services, including DreamStudio, Stable Diffusion Online, Tensor.Art, and InvokeAI. Advantages: Multiple interfaces available. 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11 September 2025 · 7 min to read
Infrastructure

Google AI Studio: Full Guide to Google’s AI Tools

Google AI Studio is a web platform from Google for working with neural networks. At the core of the service is the family of advanced multimodal generative models, Gemini, which can handle text, images, video, and other types of data simultaneously. The platform allows you to prototype applications, answer questions, generate code, and create images and video content. Everything runs directly in the browser—no installation is required. The main feature of Google AI Studio is versatility. Everything you need is in one place and works in the browser: you visit the site, write a query, and within seconds get results. The service allows users to efficiently leverage the power of Google Gemini for rapid idea testing, working with code or text. Additionally, Google AI Studio can be used not only for answering questions but also as a starting point for future projects. The platform provides all the necessary tools, and Google does not claim ownership of the generated content. You have access not only to a standard chat with generative AI but also to specialized models for generating media content, music, and applications. Let’s go through each in detail. Chat This is the primary workspace in Google AI Studio, where you work with prompts and configure the logic and behavior of your model. Chat Options At the top, there are tools for working with the chat itself. System Instruction The main configuration block, which defines the “personality,” role, goal, and limitations for the model. It is processed first and serves as a permanent context for the entire dialogue. The system instruction is the foundation of your chatbot. The field accepts text input. For maximum effectiveness, follow these principles: define the role (clearly state what the model is), define the task (explain exactly what the model should do), set the output format, establish constraints (prevent the model from going beyond its role). Example instruction: "You are a Senior developer who helps other developers understand project code. You provide advice and explain the logic of the code. I am a Junior who will ask for your help. Respond in a way I can understand, point out mistakes and gaps in the code with comments. Do not fully rewrite the code I send you—give advice instead." Show conversation with/without markdown formatting Displays text with or without markdown formatting. Get SDK Provides quick access to API code by copying chat settings into code. All model parameters from the site are automatically included. Share prompt Used to send a link to your dialogue with the AI. You must save the prompt before sharing. Save prompt Saves the prompt to your Google Drive. Compare mode A special interface that allows you to run the same prompt on different language models (or different versions of the same model) simultaneously and instantly see their responses side by side. It’s like parallel execution with a visual comparison. Clear chat Deletes all messages in the chat. Model Parameters In this window, you select the neural network and configure its behavior. Model Select the base language model. AI Studio provides the following options: Gemini 2.5 Pro: a “thinking” model capable of reasoning about complex coding, math, and STEM problems, analyzing large datasets, codebases, and documents using long context. Gemini 2.5 Flash: the best model in terms of price-to-performance, suitable for large-scale processing, low-latency tasks, high-volume reasoning, and agentic scenarios. Gemini 2.5 Flash-Lite: optimized for cost-efficiency and high throughput. Other available models include Gemini 2.0, Gemma 3, and LearnLM 2.0. More details about Gemini Pro, Flash, Flash-Lite, and others can be found in the official guide. Temperature: Controls the degree of randomness and creativity in the model’s responses. Higher values produce more diverse and unexpected answers, usually less precise. Lower values make responses more conservative and predictable. Media resolution: Refers to the level of detail in input media (images and video) that the model processes. Higher resolution allows Gemini to “see” and analyze more details, but requires more tokens for analysis. Thinking mode: Switches the model into a reasoning mode. The AI decomposes tasks and formulates instructions rather than outputting a result immediately. Set thinking budget: Limits the maximum number of tokens for the reasoning mode. Structured output: Allows developers and users to receive AI responses in predefined formats like JSON. You can specify the desired output format manually or via a visual editor. Grounding with Google Search: Enables Gemini to access Google Search in real-time for the most relevant and up-to-date information. 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Generate Media This section on the left panel provides interfaces for generating media: speech, images, music, and video. Gemini Speech Generator Converts text into audio with flexible settings. Use for video voice-overs, audio guides, podcasts, or virtual character dialogues. Tools include Raw Structure (scenario definition), Script Builder, Style Instructions, Add Dialog, Mode (monologue/dialogue), Model Settings, and Voice Settings. Main tools on the control panel: Raw Structure: Defines the scenario—how the request to the model for speech generation will be constructed. Script Builder: Instruction for dialogue with the ability to write lines and pronunciation style for each speaker. Style Instructions: Set the emotional tone and speech pace (for example: friendly, formal, energetic). Add Dialog: Add new lines and speakers. Mode: Choice between monologue and dialogue (up to 2 participants). Model Settings: Adjust model parameters, for example, temperature, which affects the creativity and unpredictability of speech. Voice Settings: Select a voice, adjust speed, pauses, pitch, and other parameters for each speaker. Image Generation A tool for generating images from a text description (prompt). Three models are available: Imagen 4 Imagen 4 Ultra Imagen 3 Imagen 4 and Imagen 4 Ultra can generate only one image at a time, while Imagen 3 can generate up to four images at once. To generate, enter a prompt for the image and specify the aspect ratio.  Music Generation A tool for interactive real-time music creation based on the Lyria RealTime model. The main feature is that you define the sound you want to hear and adjust its proportion. The more you turn up the regulator, the more intense the sound will be in the final track. You can specify the musical instrument, genre, and mood. The music updates in real time. Video Generation A tool for video generation based on Veo 2 and Veo 3 models (API only). Video length up to 8 seconds, 720p quality, 24 frames per second. Supports two resolutions—16:9 and 9:16. Video generation from an image: Upload a file and write a prompt. The resulting video will start from your image. Negative prompt support: Allows specifying what should not appear in the frame. This helps fine-tune the neural network’s output. App Generation Google AI Studio instantly transforms high-level concepts into working prototypes. To do this, go to the Build section. Describe the desired application in the prompt field and click Run. AI Studio will analyze this request and suggest a basic architecture, including necessary API calls, data structures, and interaction logic. This saves the developer from routine setup work on the initial project and allows focusing on unique functionality. The app generation feature relies on an extensive template library. Conclusion Google AI Studio has proven itself as a versatile platform for generative AI. It combines Gemini chat, multimodal text, image, audio, video generation, and app prototyping tools in one interface. The platform is invaluable for both developers and general users. Even the free tier of Google AI Studio covers most tasks—from content generation to MVP prototyping. Recent additions include Thinking Mode, Proactive Audio, and Gemini 2.5 Flash, signaling impressive future prospects.
10 September 2025 · 8 min to read
Infrastructure

Cloud vs Dedicated Server for E-commerce

If your online store is growing, sooner or later a key infrastructure question arises: cloud or dedicated server? Which one can be launched faster, which will survive peak loads without “crashes,” and how much will it cost with backups and administration? In this article, we will examine the key differences between the cloud and a dedicated server, ways of calculating the total cost of ownership (TCO), and typical bottlenecks in e-commerce: the database, cache, and static files. Cloud and Dedicated Server: Main Differences Let’s draw a simple analogy. The cloud is like a room in a hotel: you can move in quickly, request another room if necessary, cleaning and maintenance are included.  A dedicated server is like owning a house: it is completely yours, no one shares resources, but you need to take care of it yourself. How the Cloud Works You create a cloud server with the necessary configuration and can quickly change its parameters: increase memory, disk space, or add another server for web applications. Usually, this is accompanied by a flexible payment system—for example, in Hostman it is hourly.  The advantages are quick launch, scalability, convenient backups and snapshots. The disadvantages are that with excessive resources it is easy to overpay, and with round-the-clock high load, the cost may be higher than that of a dedicated server. How a Dedicated Server Works This is renting a physical server in a data center. The resources are entirely yours: CPU, memory, disks—without any “neighbors.”  The advantages are stable performance and a favorable price with constant heavy traffic. The disadvantages are slower scaling (waiting for an upgrade or migration), service downtime during failures may last longer, and administration of the server and organization of backups are entirely the responsibility of the client. What’s More Important for a Small Store You can launch an online store in the cloud today, in mere hours. When renting a dedicated server, allow time for its preparation: engineers need to assemble and test the configuration, especially if you ordered a custom one. Usually this takes a couple of days.  In the cloud, resources can be increased in a few clicks. On a dedicated server, the scaling process takes longer: you need to coordinate changes with engineers, wait for components, and install them in the data center. In some cases, it may require migration to a new server. Cloud offers many ready-made tools and automation. A dedicated server, as a rule, will require more manual configuration and regular involvement of an engineer. Money: if you have 20–300 orders per day and traffic “jumps,” the cloud is usually more profitable and quite suitable for solving such tasks. If orders are consistently high, 24/7, without sharp spikes, a dedicated server will be more reliable. In short: if you are just starting out, choose the cloud. When the load becomes consistently high, you can consider a dedicated server. Key Criteria When Choosing Infrastructure for an Online Store Let’s look at the key criteria to pay attention to when choosing between a cloud and a dedicated server. Speed of launch It is important for a business to launch in hours, not days. A cloud server and database are ready in just minutes. A dedicated server takes longer to prepare: on average, about an hour, and when ordering a custom configuration, it may take several days. Expenses Expenses in a small project can be calculated as the sum of these items:  Infrastructure: server, disks, traffic, IP, domains, CDN.  Reliability: backups and storing copies separately.  Administration: updates, monitoring, on-call duty.  Downtime: how much one hour of downtime costs (lost revenue + reputation). Peak loads Sometimes stores run sales, order promotions from bloggers, or it is simply the business season.  In the cloud, you can scale horizontally, setting up another VM, and vertically, by adding more vCPU and RAM.  To speed up images and static files loading, you can connect a CDN—this is equally available in the cloud and on a dedicated server.  With a dedicated server, you either have to pay for all the reserve capacity year-round, or request installation of additional modules—which, again, can take some time (hours or days, depending on component availability). Reliability and recovery There are two main parameters to consider when planning recovery time.  RTO: how much time the project can take to recover after downtime (goal: up to an hour).  RPO: how much data you are ready to lose during recovery (goal: up to 15 minutes, meaning that after the system is restored, you may lose only the data created in the last 15 minutes before the failure). Check: are backups scheduled, are copies stored outside the production server, will the system be able to recover automatically if production goes down. Security At a minimum, configure the site to work through an SSL certificate, set up multi-factor authentication in the control panel for administrators, and create a private network between the web server and the database. Performance Usually the bottlenecks of e-commerce are the database, cache, and images. To avoid problems when scaling, put images and videos in object storage, keep the database as a separate service, preferably with data replication. Monitor the response times of the cart and checkout pages—this is where sales most often fail if pages respond slowly. Growth and flexibility We recommend starting with a simple and reliable scheme: one cloud server + one separate database (DBaaS) + object storage for media. If you plan a sale, add another cloud server and a load balancer to distribute user traffic. Afterwards, return to the original scheme. Flexibility in this case may be more important than the “perfect” architecture at the start. Team competencies If there is no system administrator or developer in the team who can perform sysadmin functions, choose simple solutions: ready CMS images, managed DBs, automatic backups, built-in monitoring. The less manual work, the fewer risks. Building Reliable Infrastructure For a small store, a simple logic works: start with minimal but healthy architecture, and quickly increase capacity during sales. And just as quickly return to normal mode. Start with a clean cloud server on Ubuntu LTS, connect access via SSH keys, and disable password login. At the firewall level, leave only ports 80/443, the others are better disabled.  An alternative option is to use control panels (cPanel, FastPanel, etc.), where the stack is deployed “out of the box” and administration is available through a convenient graphical interface. Place the database separately and connect it to the application through a private network. This way it will not be accessible from the internet, and delays will be reduced. Create a separate DB user with minimal rights for the site, enable daily backups and store them outside the production environment. For sessions and cache use Redis: it will reduce load on the database and speed up product cards, search, and order processing. Transfer media files to object storage: CMS can easily be configured so that new uploads go to S3. On top of this, connect a CDN for images, JS, and CSS—this will provide a stable response speed for users from any region and relieve a significant load from web servers. Do not forget about Cache-Control and ETag headers: they will allow users’ browsers to keep static files longer in local cache, which speeds up site loading and reduces server load. Backups are part of the daily routine. For the database, make a daily full backup and several incremental points during the day, store copies for at least 30 days, and place them in another project or storage. Protect files and media with versioning in S3 and weekly server snapshots. Once a quarter perform a recovery “from scratch” on a clean machine to check your RTO and RPO. Monitoring allows you to reduce risks and prevent losses before failures occur. Monitor the response time for the cart and checkout, CPU load, and free disk space. Threshold values should be tied to your traffic: if response time goes down and CPU stays high, get ready to scale. A sales campaign should be prepared as carefully as a separate release. A day or two before launch make a snapshot and bring up a second machine, enable the load balancer, and check that sessions are in Redis so carts are not lost. Prepare the CDN in advance: open the most visited pages, product cards, and search results. Increase database resources in advance and check indexes on fields used for filtering and sorting. After the campaign ends, disable additional servers. Approach security issues without excessive measures, but consistently and systematically. In the store’s admin panel, enable multi-factor authentication and roles, on servers, prohibit SSH passwords, limit by IP, and use fail2ban against password brute force. To avoid overpaying, calculate infrastructure by roles: server, DB, S3 storage, CDN, snapshots and admin hours. Launch additional capacity only during peak days, and under normal load, plan infrastructure based on basic needs. Evaluate the cost of downtime: if it is higher than the cost of an additional server for a week, reserving resources for a promotion will be economically justified. Migration from a dedicated server to cloud hosting is safe if done in two phases. Prepare a copy of the infrastructure, place media files in S3 storage, and run the site on a test domain with regular DB synchronization. On migration day, freeze changes, make the final dump, lower TTL, and switch DNS. After switching, monitor metrics and logs, and keep the previous production environment in “read-only” mode for a day for emergency access. If you need size guidelines, think in terms of load.  Up to one hundred orders per day is usually enough with a server of 2 vCPU and 4–8 GB of memory, a separate DB of 1–2 vCPU and 2–4 GB, SSD of 60–120 GB, and a combination of S3+CDN with Redis.  With a load of 100–500 orders per day it is reasonable to use two cloud servers and a load balancer, a database with 2–4 vCPU and 8–16 GB, and if necessary, add a read replica.  With stable peak loads, the infrastructure is scaled to 2–3 cloud servers with 4–8 vCPU and 16 GB, a database with 4–8 vCPU and 32 GB, replication, and mandatory CDN.  These are starting points; further decisions are dictated by metrics. Conclusion There is no single correct answer in this subject. The choice between cloud and dedicated server depends on traffic, frequency of peaks, team competencies, and how much one hour of downtime costs you. It is important not to guess, but to rely on numbers and understand how quickly you can increase capacity and recover after a failure. If the store is small or growing, it is reasonable to start with the cloud: one server for the application, a separate DB, and object storage for media. Such a scheme can be launched in an evening, handles sales without long downtime, and does not force you to pay for “reserve” all year. The main thing is to immediately enable backups, configure a private network between the server and the DB, and have a scaling plan ready for sales days. When traffic becomes steady and high 24/7, and requirements for performance and integrations tighten, it makes sense to consider a dedicated server or hybrid. Often a combination works where the frontend application and static files remain in the cloud for flexibility, while the heavy DB or specific services move to “hardware.” The decision should be made not by preference, but by TCO, RTO/RPO, and load metrics.
09 September 2025 · 10 min to read

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