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Top AI Coding Tools in 2025: Best Models for Developers & Programmers

Top AI Coding Tools in 2025: Best Models for Developers & Programmers
Hostman Team
Technical writer
Infrastructure

In the early days of computing, programmers wrote code entirely on their own, from scratch and solo. Hardcore mode! The only help they had was paper reference books describing the syntax of specific languages.

Today, things are very different. In addition to countless electronic manuals, guides, articles, videos, and forums, we now have neural networks, arguably one of the most groundbreaking technologies of the early 21st century.

Trained on massive datasets, these AI models have become the primary source of coding assistance.

The advantages are obvious. AI coding tools speed up the development process by taking on much of the routine work involved in writing code. This allows developers to focus on architecture and logic instead of syntax errors and inefficient constructs.

Some tools generate code from scratch, and others analyze and complete already-written code.

However, in recent years, so many AI-powered projects have emerged that it can be difficult for the average person to figure out which AI is actually the best for programming.

There are both specialized and general-purpose models. Some only generate specific types of data (like code), while others handle all kinds (text, code, images). Some are free, others paid.

To determine which AI is the best for programming (and why), we first need to create a list of the top coding AIs, and then analyze the pros and cons of each one.

1. GitHub Copilot

Copilot is arguably the best AI coding assistant, developed by GitHub in collaboration with OpenAI. It’s positioned as an AI co-programmer trained on millions of open-source GitHub repositories.

Features

Developed by the largest cloud-based code hosting platform, Copilot leads the list of neural networks for programming, offering a wide range of capabilities:

  • Code Generation: Produces ready-to-use code snippets in all major languages based on text descriptions: scripts, functions, classes, even entire files. While the AI sometimes generates imperfect results, this can be resolved by making the user’s request more specific.

  • Code Translation: Converts code written in one programming language into logically equivalent code in another. This feature alone puts Copilot ahead of many other coding AIs, as not all models can do this effectively.

  • Code Autocompletion: Suggests autocompletion based on the overall context of the codebase.

  • Refactoring: Enhances code structure, optimizes algorithms, and fixes errors. It can also suggest alternative, more efficient solutions that a developer might not have initially considered.

  • Editor Integration: Integrates via plugins into popular text editors and IDEs like Visual Studio Code, Neovim, JetBrains IDEs, and others.

These features help automate routine coding tasks.

A lesser-known use case of Copilot is learning programming languages. The AI can generate simple code snippets that demonstrate the syntax and mechanics of a specific language.

Interestingly, this teaching method aligns with Stephen Krashen’s Comprehensible Input Hypothesis, which states that language acquisition is driven by understandable input, i.e., the material that the learner can interpret with explanation.

Similarly, Copilot can be used as an interactive reference, potentially replacing resources like Stack Overflow.

Languages Supported

Copilot supports all major programming languages: C, C++, C#, Go, Java, JavaScript, Kotlin, PHP, Python, Ruby, Rust, Scala, Swift, and TypeScript.

It can also generate code using popular frameworks and libraries like React, Angular, Vue.js, Node.js, Django, Flask, and Ruby on Rails.

Pricing Plans

Naturally, GitHub offers only a limited set of Copilot features for free. The free version also has monthly limits on code generations.

The full version is available through subscriptions for individuals, teams, and enterprises. Pricing starts at $4/month, with a 30-day free trial. In return, users get a powerful tool for faster coding.

Despite requiring a subscription, many developers consider Copilot the best AI coding assistant, especially when compared to general-purpose models like ChatGPT, which aren't primarily designed for code generation.

2. Tabnine

Tabnine is an AI that generates code snippets not based on explicit prompts but on the development context formed by the programmer’s current work.

Features

Unlike Copilot, Tabnine primarily focuses on code autocompletion. However, it also offers several distinctive features:

  • Offline Mode: The Enterprise version of Tabnine can run entirely offline, generating code without internet access. This improves data privacy, as code is processed locally and not sent to the cloud; however, it does require more system resources.

  • Personalized Generation: Tabnine learns from a specific developer’s codebase, mimicking their unique style and preferences. This results in personalized suggestions that feel as if the code were written by the developers themselves, in contrast to Copilot, which was trained on public GitHub repositories.

  • IDE Integration: Since Tabnine is not a standalone application but a smart autocompletion engine, it integrates with virtually all major IDEs through plugins, including VS Code, IntelliJ, Visual Studio, Eclipse, Android Studio, AppCode, CLion, GoLand, Neovim, PhpStorm, PyCharm, Rider, RubyMine, WebStorm.

  • Interactive AI Chat: Tabnine also offers a built-in chat interface for personalized communication with the AI. Users can ask questions related to the code in their current editor tab.

All in all, Tabnine is geared more toward typing speed and efficiency rather than generating large chunks of code from scratch. Think of it as classic autocompletion but supercharged with AI.

Languages Supported

Like Copilot, Tabnine supports autocompletion for all major programming languages: Python, JavaScript, TypeScript, Java, C/C++, C#, Go, Ruby, Swift, PHP, Rust, Kotlin, Perl, Dart, Scala.

Pricing Plans

Tabnine is available under two subscription plans:

  • Dev – from $9/month for individual developers.
  • Enterprise – from $39/month for teams and companies.

The Enterprise plan offers advanced customization options, enhanced security, and on-premise deployment for maximum privacy.

3. ChatGPT

ChatGPT is a generative AI developed by OpenAI, based on the GPT (Generative Pre-trained Transformer) architecture.

Unlike Copilot and Tabnine, ChatGPT can generate not just code but also various forms of text content. That makes it a general-purpose neural network—a versatile tool for generating any data based on a user's prompt.

Some might argue that ChatGPT is more text-oriented than code-focused. However, it remains one of the best free AI for coding, as its basic version is available to everyone without a paid subscription.

Features

ChatGPT operates through a chat interface, where prompts are entered as natural language messages.

That’s why its name consists of Chat and GPT:

  • Chat: its ability to hold conversations, answer questions, and maintain real-time dialogue.
  • GPT: short for Generative Pre-trained Transformer:
    • Generative: creates original text rather than copying answers,
    • Pre-trained: trained on vast data corpora,
    • Transformer: refers to the model’s architecture, which uses attention mechanisms to understand and generate contextually accurate responses.

In short, ChatGPT is a conversational AI capable of tackling almost any language-based task — including code generation.

Here’s what it can do:

  • Conversational Interaction: The AI responds as if you were chatting with another person. You can ask it to use a certain tone, generate text, tables, code, and even simple images. This makes ChatGPT a virtual assistant, coworker, or advisor.

  • Free Code Generation: The base model is completely free to use. More advanced versions offer improved performance but require a subscription.

  • Multi-Format Output: It can create more than just code. You can generate any language-based content based on a clear prompt and adapt it to the ongoing context of the conversation.

For example, you could write this fun prompt:

“Give me an example of Python code with a helicopter and a car class. Each should have a fuel variable initialized to 100. Then create objects of each class and have the helicopter hook the car with a cable.”

ChatGPT would generate something like this:

class Helicopter:
    def __init__(self):
        self.fuel = 100
        self.hooked_car = None

    def hook_car(self, car):
        self.hooked_car = car
        print("The helicopter has hooked the car with a cable.")

class Car:
    def __init__(self):
        self.fuel = 100

helicopter = Helicopter()
car = Car()
helicopter.hook_car(car)

You can check this code in any online Python interpreter and get the expected output:

The helicopter has hooked the car with a cable.

So, if you're working late at night and wondering which neural network is best for hands-off code generation, ChatGPT is worth considering. After all, OpenAI is a global leader in machine learning.

At the very least, ChatGPT is the best conversational AI for code creation, capable of generating not only code but also full documents, tables, and even basic images.

Languages Supported

Since it was trained on a vast linguistic dataset, ChatGPT can generate code in nearly any language and not just general-purpose ones.

It supports all major programming languages, including Python, JavaScript, TypeScript, Java, C, C++, C#, Go, PHP, Swift, Kotlin, Ruby, Rust, Haskell, Lisp, Elixir, Erlang, and F#.

It also understands domain-specific languages: HTML, CSS, SASS/SCSS, SQL, GraphQL, Shell, PowerShell, Lua, Perl, YAML, and JSON.

Listing them all would be pointless, as ChatGPT can understand and generate code or text in virtually any format. That's its defining strength.

Pricing Plans

OpenAI offers four subscription tiers for ChatGPT, each expanding the capabilities of the last:

  • Free – All basic features. No cost.
  • Plus – Enhanced performance and access to newer models with better contextual understanding and faster responses. Starts at $20/month.
  • Pro – Full access with no usage limits. Starts at $200/month.
  • Team – Adds collaborative tools, custom roles, and enhanced security for data sharing and storage. Team data is excluded from AI training — ensuring full confidentiality. Starts at $25/month.

Paid plans provide higher accuracy, better performance, and more stability. Still, the free version offers nearly identical functionality — the difference lies in the fine details.

4. Claude

Claude is another natural language processing AI developed by Anthropic. According to its creators, Claude is a safer, more ethical, and more predictable alternative to ChatGPT.

Features

Overall, Claude's capabilities are similar to ChatGPT’s, with a few notable distinctions:

  • Image and Document Analysis: Claude can interpret the contents of images and documents in detail, recognizing real-world objects, diagrams, graphs, numbers, and text. ChatGPT is also capable of this, but only in its paid version. Claude offers it natively.

  • Massive Context Window: Claude supports up to 200,000 tokens, which allows it to analyze large volumes of data. By comparison, ChatGPT maxes out at around 128,000 tokens. One token is roughly 5 characters of English text.

  • High Ethical Standards: Thanks to built-in ethical constraints, Claude is less likely to generate inappropriate content, making its responses more conservative. While this may not matter to some users, from a broader perspective, output filtering is a key trait that separates the best AI coding tools from the rest, especially as AI tools become mainstream.

In short, Claude offers high factual accuracy, which is crucial for generating reliable code based on user instructions.

Languages Supported

According to Anthropic, Claude performs best when generating Python code. However, it also supports other popular languages: JavaScript, Java, C++, Go, PHP, Ruby, C#, Swift, TypeScript, Kotlin, and Rust.

Of course, the full list of supported languages isn’t publicly available, as the model was trained on diverse datasets. Practical testing is the best way to determine support.

Pricing Plans

Claude offers several pricing tiers:

  • Free – Standard chat access via browser or mobile app (iOS/Android). No cost.
  • Pro – Enables structured chats, document analysis, and access to additional Claude models and features. Starts at $18/month.
  • Team – Adds collaboration features for group work. Starts at $25/month.
  • Enterprise – Provides deeper control over generation processes, user role management, and enhanced data privacy. Custom pricing.

Despite Claude being one of the top free AI for coding, it can’t be considered a full competitor to ChatGPT.

Here’s why:

  • Smaller Knowledge Base: ChatGPT was trained on more data, producing more accurate and diverse responses.
  • Limited Availability: Claude is not as widely accessible as ChatGPT and is available in fewer countries.
  • Few Integrations: ChatGPT is integrated into many products (e.g., Office, Azure), while Claude is not.
  • Slower Development: ChatGPT evolves rapidly, releasing updates and features faster than Claude.

Still, Claude is worth trying for anyone who regularly uses AI in programming or text generation tasks.

5. Snyk Code

Snyk Code is an AI-powered static analysis tool for detecting vulnerabilities and errors, part of the broader Snyk ecosystem.

Features

Trained on a database of known vulnerabilities (updated regularly), Snyk Code focuses on secure development:

  • Vulnerability Detection: Performs real-time code analysis during development and commits to catch threats before they reach production.

  • Development Tool Integration: Works with GitHub, GitLab, Bitbucket, and Azure Repos, and is compatible with popular IDEs: VS Code, IntelliJ IDEA, PyCharm, WebStorm, Eclipse.

  • Contextual Fix Recommendations: For every issue found, it provides an explanation and sample fixes, helping developers patch their code quickly and securely.

In essence, Snyk Code is best used after you have written the code as an added security layer before deployment.

Languages Supported

Snyk Code supports major programming languages only: Apex, C, C++, Go, Groovy, Java, Kotlin, JavaScript, .NET, PHP, Python, Ruby, Scala, Swift, Objective-C, TypeScript, VB.NET.

Pricing Plans

Snyk Code is free for individual use, but teams and companies can choose from the following:

  • Free – Basic analysis with a limit of up to 200 scans per month.
  • Team – Adds support for private repos, CI/CD integration, and advanced security features. Starts at $25/month.
  • Enterprise – Includes local deployment, advanced analytics, and enterprise-level controls. Custom pricing.

While Snyk Code doesn’t generate code, its powerful analysis tools and free tier perfectly justify its inclusion in any list of the best free AI tools for coding.

6. Documatic

Documatic is an AI that automatically generates documentation and enables codebase exploration. It analyzes the project, extracts key information, and structures it for easy reference.

Features

Documatic is designed for codebase analysis; all other functionality stems from this core:

  • Automatic Documentation Generation: Produces detailed code explanations, reducing the need for manual comments.

  • Code Search and Navigation: Responds to developer queries with relevant code snippets and context.

  • Project Structure Visualization: Displays project components (dependencies, microservices, repos) as interactive graph nodes, useful for understanding complex architectures.

  • Code Explanation: Clarifies algorithms and logic, making unfamiliar projects easier to understand.

Documatic is passive: it doesn’t generate code, only analyzes and documents it.

Languages Supported

It supports modern interpreted and compiled languages: Python, Java, JavaScript, TypeScript, Go, C#, PHP.

Pricing Plans

Documatic keeps things simple with just two tiers:

  • Free – Full basic functionality for solo developers, including auto-documentation. No cost.
  • Team / Enterprise – A combined tier for corporate use, offering unlimited analysis, integrations, reporting, and collaboration tools. Custom pricing.

While it’s easy to chase the best AI coding tools, it’s crucial to remember: the developer matters more than the AI. Skills, logic, creativity, and experience outweigh any neural network’s output.

You should only upgrade to premium tools when free features no longer meet your needs.

7. Mintlify

Mintlify is a comprehensive online platform for automating code documentation with AI.

Unlike Documatic, Mintlify offers cloud hosting with visually styled, user-accessible documentation sites.

For instance, a developer or team building a JavaScript library can generate full documentation from a GitHub repo, resulting in a live, multi-page site with API references. These pages are editable using a WYSIWYG editor.

Fun fact: Anthropic uses Mintlify to power the documentation for Claude.

Features

Mintlify connects the project’s codebase to a public-facing documentation site, offering:

  • Automated Documentation Generation: Generates detailed documentation (including API references) directly from your codebase.

  • Version Control Integration: Syncs with GitHub and GitLab, ensuring documentation updates automatically when the code changes, which makes it perfect for CI/CD pipelines.

  • Documentation Site Hosting: Creates a stylish, SEO-optimized site with editable sections.

  • Analytics & Feedback: Provides user analytics and supports direct feedback collection to improve documentation quality.

While powerful, Mintlify has a learning curve as its feature-rich interface takes time to master.

Languages Supported

Supports 12 modern languages: Python, JavaScript, TypeScript, C, C++, PHP, Java, C#, Ruby, Rust, Dart, Go.

Pricing Plans

Mintlify offers four plans:

  • Hobby – Free plan for individuals with full standard functionality.
  • Pro – Advanced configuration and analytics. Starts at $150/month.
  • Growth – Allows full customization, branding removal, and other perks. Starts at $550/month.
  • Enterprise – Full configuration control and dedicated support. Custom pricing.

Where other AI coding tools show their intelligence directly, Mintlify’s AI works silently in the background.

At first glance, it may seem like a manual documentation editor; however, over time, it reveals itself as an automation powerhouse, seamlessly connecting code to documentation.

8. Codeium

Codeium is an AI-powered coding assistant that consists of several products built on artificial intelligence:

  • Windsurf Editor – an integrated development environment (IDE) with built-in AI.
  • Forge – an AI assistant for code analysis and review.

In addition to these, there’s a browser-based chat called Live, as well as numerous IDE extensions – Codeium Extensions.

E7261d0c 08e8 4178 9ec8 Bb509066df1d.png

The Codeium Windsurf Editor integrated development environment, with the code editor on the left and the AI chat on the right. Source: codioailab.com

Features

Codeium offers a wide range of features that assist during coding and code editing:

  • Code Autocompletion: Provides intelligent suggestions as you type.

  • Chat Assistant: A built-in AI chat can explain code snippets in detail, offer refactoring suggestions (passively while you write), and answer programming questions directly within the development environment. It can also advise on build commands and configuration.

  • Intelligent Search: Ensures quick access to classes, methods, functions, and code fragments, streamlining navigation in large codebases.

Essentially, Codeium aims to provide a comprehensive suite of tools for virtually all coding scenarios – all powered by AI.

Languages Supported

Supports all popular programming languages, including: Python, JavaScript, TypeScript, Go, Java, C#, PHP, Ruby, Kotlin, Swift.

Pricing Plans

Codeium offers several pricing plans for both individual developers and entire teams:

  • Free – All standard features. Free of charge.
  • Pro – Expanded context and deeper AI understanding, faster autocompletion, and other advanced features. Starting at $15/month.
  • Pro Ultimate – Even more useful tools and priority support. Starting at $60/month.
  • Teams – Collaboration and analytics tools for teams. Starting at $35/month.
  • Teams Ultimate – Enhanced AI model access. Starting at $90/month.
  • Enterprise SaaS – Custom pricing upon request.

9. Gemini

Gemini is a versatile AI developed by Google. Despite being relatively new, it rounds out our list of the top AI coding assistants in 2025. Unsurprisingly, it’s a direct competitor to both ChatGPT and Claude. 

Features

It’s important to recognize that Google is a major player (arguably a monopolist) in the software market. With vast cloud infrastructure, massive data resources, and many popular services (plus its own OS, Android), Gemini offers a broad array of capabilities for working with both text and visual data:

  • Text Generation, Analysis, and Translation.

  • Image Generation and Analysis: Generates images from text prompts and can also analyze images and describe their contents.

  • Code Generation and Analysis: Generates code snippets in any language and format. Also understands and analyzes code, providing suggestions for improvement. Google also offers the Gemini Code Assist extension for popular IDEs.

  • Integration with Google Services: Integrated with many Google apps and Android tools.

  • Fast Response Generation: Provides answers faster than ChatGPT and generally operates at a higher speed.

  • Large Context Window: Can handle up to 1 million tokens.

Notably, the advanced capabilities of Gemini’s language model are available through a special AI Studio for developers. This environment allows not only text-based interaction but also screen sharing for more detailed feedback.

AI Studio is designed for app developers who want to test Gemini integration with their products.

Languages Supported

Gemini supports the following major programming languages: Python, Java, C++, JavaScript, Go, TypeScript, C#, Ruby, PHP, Swift, Kotlin, Rust, SQL, HTML, CSS, Bash, Perl, Lua, R, Dart, Scala, Julia, Fortran.

Pricing Plans

Google offers a fairly straightforward pricing structure for Gemini:

  • Free – Standard model access.
  • Advanced – Enhanced performance, accuracy, and multimodal capabilities. Starting at $22/month.

Thus, just like ChatGPT, Gemini is another great free AI for programming, particularly when it comes to working with general-purpose data. The ability to generate not only code but also supporting text is an important asset in development.

Conclusion

So, what is the best AI for coding? That’s for each user to decide. Some may be satisfied with intelligent autocompletion, while others may require the generation of large code fragments across multiple languages – complete with detailed explanations.

Model

Type

Features

Pricing

Copilot

Specialized

Code generation, autocompletion

Subscription

Tabnine

Specialized

Autocompletion

Subscription

ChatGPT

General

Generation, analysis

Free, subscription

Claude

General

Generation, analysis

Free, subscription

Snyk Code

Specialized

Analysis

Free, subscription

Documatic

Specialized

Documentation

Free, subscription

Mintlify

Specialized

Documentation, hosting

Free, subscription

Codeium

Specialized

Generation, analysis

Free, subscription

Gemini

General

Generation, analysis

Free, subscription

Ultimately, the most important factor is not the tool itself, but the developer using it. Skills, experience, logic, critical thinking, and creativity all outweigh the capabilities of any neural network.

So, switching to paid versions of AI products – whether they’re code generators or analyzers – only makes sense when the free version clearly falls short for your needs.

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Characteristic Description Significance for ML VRAM Volume Memory for storing models and data Large models require 24-80 GB CUDA Cores / Tensor Cores Blocks for parallel computing Accelerate training, especially FP16 Framework Support Compatibility with PyTorch, TensorFlow, JAX Simplifies development Power Consumption Consumed power (W) Affects expenses and cooling Price/Performance Balance of cost and speed Optimizes budget Video Memory Volume (VRAM) VRAM determines how much data and model parameters can be stored on the GPU. For simple tasks such as image classification, 8-12 GB is sufficient. However, for large models, including LLMs or generative networks, 24-141 GB is required (like the Tesla H200). Lack of VRAM leads to out-of-memory errors, which can stop training. Case: A fintech startup uses Tesla A6000 with 48 GB VRAM for transaction analysis, accelerating processing by 40%. Recommendation: Beginners need 12-16 GB, but for corporate tasks choose 40+ GB. Number of CUDA Cores and FP16/FP32 Performance CUDA cores (for NVIDIA) or Stream Processors (for AMD) provide parallel computing. More cores mean higher speed. For example, Tesla H200 with approximately 14,592 cores outperforms RTX 3060 with approximately 3,584 cores. Tensor Cores accelerate low-precision operations (FP16/FP32), which is critical for modern models. Case: An automotive company trains autonomous driving models on Tesla H100, reducing test time by 50%. For business, this means development savings. Library and Framework Support (TensorFlow, PyTorch) A graphics card for AI must support popular frameworks: TensorFlow, PyTorch, JAX. NVIDIA leads thanks to CUDA, but AMD with ROCm is gradually catching up. Without compatibility, developers spend time on optimization, which slows down projects. Case: A marketing team uses PyTorch on Tesla A100 for A/B testing advertising campaigns, quickly adapting models to customer data. Power Consumption and Cooling Modern GPUs consume 200-700W, requiring powerful power supplies and cooling systems. In 2025, this is relevant for servers and data centers. Overheating can lead to failures, which is unacceptable for business. Case: A logistics company uses water cooling for a GPU cluster, ensuring stable operation of forecasting models. Price and Price-Performance Ratio The balance of price and performance is critical for return on investment (ROI) and long-term efficiency of business projects. For example, Tesla A6000, offering 48 GB VRAM and high performance for approximately $5,000, pays for itself within a year in projects with large models, such as financial data processing or training complex neural networks. However, choosing the optimal graphics card for neural networks depends not only on the initial cost, but also on operating expenses, including power consumption and the need for additional equipment, such as powerful power supplies and cooling systems. For small businesses or beginning developers, a graphics card for machine learning, such as RTX 3060 for $350-500, can be a reasonable start. It provides basic performance for educational tasks, but its limited 12 GB VRAM and approximately 3,584 CUDA cores won't handle large projects without significant time costs. On the other hand, for companies working with generative models or big data analysis, investing in Tesla H100 for $20,000 and more (depending on configuration) is justified by high training speed and scalability, which reduces overall costs in the long term. It's important to consider not only the price of the graphics card itself, but also additional factors, such as driver availability, compatibility with existing infrastructure, and maintenance costs. For example, for corporate solutions where high reliability is required, Tesla A6000 may be more profitable compared to cheaper alternatives, such as A5000 ($2,500-3,000), if we consider reduced risks of failures and the need for frequent equipment replacement. Thus, the price-performance ratio requires careful analysis in the context of specific business goals, including product time-to-market and potential benefits from accelerating ML processes. Best Graphics Cards for AI in 2025 The GPU market in 2025 offers the best solutions for different budgets and tasks. Optimal Solutions for Beginners (under $1,000) For students and small businesses, the best NVIDIA graphic card for AI would be RTX 4060 Ti (16 GB, approximately $500). This graphics card will handle educational tasks excellently, such as data classification or small neural networks. RTX 4060 Ti provides high performance with 16 GB VRAM and Tensor Cores support. Alternative: AMD RX 6800 (16 GB, approximately $500) with ROCm for more complex projects. Case: A student trains a text analysis model on RTX 4060 Ti. Mid-Range: Balance of Power and Price NVIDIA A5000 (24 GB, approximately $3,000) is a universal choice for medium models and research. It's suitable for tasks like data analysis or content generation. Alternative: AMD Radeon Pro W6800 (32 GB, approximately $2,500) is a powerful competitor with increased VRAM and improved ROCm support, ideal for medium projects. Case: A media company uses A5000 for generative networks, accelerating video production by 35%. Professional Graphics Cards for Advanced Tasks Tesla A6000 (48 GB, approximately $5,000), Tesla H100 (80 GB, approximately $30,000), and Tesla H200 (141 GB, approximately $35,000) are great for large models and corporate tasks. Alternative: AMD MI300X (64 GB, approximately $20,000) is suitable for supercomputers, but inferior in ecosystem. Case: An AI startup trains a multimodal model on Tesla H200, reducing development time by 60%. NVIDIA vs AMD for AI NVIDIA remains the leader in ML, but AMD is actively catching up. The choice depends on budget, tasks, and ecosystem. Here's a comparison: Parameter NVIDIA AMD Ecosystem CUDA, wide support ROCm, limited VRAM 12-141 GB 16-64 GB Price More expensive Cheaper Tensor Cores Yes No Community Large Developing Why NVIDIA is the Choice of Most Developers NVIDIA dominates thanks to a wide range of advantages that make it preferred for developers and businesses worldwide: CUDA: This platform has become the de facto standard for ML, providing perfect compatibility with frameworks such as PyTorch, TensorFlow, and JAX. Libraries optimized for CUDA allow accelerating development and reducing costs for code adaptation. Tensor Cores: Specialized blocks that accelerate low-precision operations (FP16/FP32) provide a significant advantage when training modern neural networks, especially in tasks requiring high performance, such as generative AI. Energy Efficiency: The new Hopper architecture demonstrates outstanding performance-to-power consumption ratio, which reduces operating costs for data centers and companies striving for sustainable development. Community Support: A huge ecosystem of developers, documentation, and ready-made solutions simplifies the implementation of NVIDIA GPUs in projects, reducing time for training and debugging. Case: A retail company uses Tesla A100 for demand forecasting, reducing costs by 25% and improving forecast accuracy thanks to broad tool support and platform stability. AMD GPU Capabilities in 2025 AMD offers an alternative that attracts attention thanks to competitive characteristics and affordable cost: ROCm: The platform is actively developing, providing improved support for PyTorch and TensorFlow. In 2025, ROCm becomes more stable, although it still lags behind CUDA in speed and universality. Price: AMD GPUs, such as MI300X (approximately $20,000), are the best budget GPUs for AI, as they are significantly cheaper than NVIDIA counterparts. It makes them attractive for universities, research centers, and companies with limited budgets. Energy Efficiency: New AMD architectures demonstrate improvements in energy consumption, making them competitive in the long term. HPC Support: AMD cards are successfully used in high-performance computing, such as climate modeling, which expands their application beyond traditional ML. Case: A university uses MI300X for research, saving 30% of budget and supporting complex simulations thanks to high memory density. However, the limited ROCm ecosystem and smaller developer community may slow adoption and require additional optimization efforts. Local GPU vs Cloud Solutions Parameter Local GPU Cloud Control Full Limited Initial Costs High Low Scalability Limited High When to Use Local Hardware Local GPUs are suitable for permanent tasks where autonomy and full control over equipment are important. For example, the R&D department of a large company can use Tesla A6000 for long-term research, paying for itself within a year thanks to stable performance. Local graphics cards are especially useful if the business plans intensive daily GPU use, as this eliminates additional rental costs and allows optimizing infrastructure for specific needs. Case: A game development company trains models on local A6000s, avoiding cloud dependency. Additionally, local solutions allow configuring cooling and power consumption for specific conditions, which is important for data centers and server rooms with limited resources. However, this requires significant initial investments and regular maintenance, which may not be justified for small projects or periodic tasks. Pros and Cons of Cloud Solutions Cloud solutions for GPU usage are becoming a popular choice thanks to their flexibility and accessibility, especially for businesses seeking to optimize machine learning costs. Let's examine the key advantages and limitations to consider when choosing this approach. Pros: Scalability: You can add GPUs as tasks grow, which is ideal for companies with variable workloads. This allows quick adaptation to new projects without needing to purchase new equipment. Flexibility: Paying only for actual usage reduces financial risks, especially for startups or companies testing new AI solutions. For example, you can rent Tesla A100 for experiments without spending $20,000 on purchase. Access to Top GPUs: Cloud providers give access to cutting-edge models that aren't available for purchase in small volumes or require complex installation. Updates and Support: Cloud providers regularly update equipment and drivers, relieving businesses of the need to independently monitor technical condition. Cons: Internet Dependency: Stable connection is critical, and any interruptions can stop model training, which is unacceptable for projects with tight deadlines. Long-term Costs: With intensive use, rental can cost more than purchasing local GPU. Case: A startup tests models on a cloud server with Tesla H100, saving $30,000 on GPU purchase and quickly adapting to project changes. However, for long-term tasks, they plan to transition to local A6000s to reduce costs. Conclusion Choosing a graphics card for neural networks and ML in 2025 depends on your tasks. Beginners should choose NVIDIA RTX 4060 Ti, which will handle educational projects and basic models. For the mid-segment, A5000 is a good solution, especially if you work with generative models and more complex tasks. For business and large research, Tesla A6000 remains the optimal choice, providing high video memory volume and performance. NVIDIA provides the best graphic cards for AI and maintains leadership thanks to the CUDA ecosystem and specialized Tensor Cores. However, AMD is gradually strengthening its position, offering ROCm support and more affordable solutions, making the GPU market for ML and AI increasingly competitive.
30 September 2025 · 12 min to read
Infrastructure

SOLID Principles and Their Role in Software Development

SOLID is an acronym for five object-oriented programming principles for creating understandable, scalable, and maintainable code.  S: Single Responsibility Principle.  O:Open/Closed Principle.  L: Liskov Substitution Principle.  I: Interface Segregation Principle. D: Dependency Inversion Principle. In this article, we will understand what SOLID is and what each of its five principles states. All shown code examples were executed by Python interpreter version 3.10.12 on a Hostman cloud server running Ubuntu 22.04 operating system. Single Responsibility Principle (SRP) SRP (Single Responsibility Principle) is the single responsibility principle, which states that each individual class should specialize in solving only one narrow task. In other words, a class is responsible for only one application component, implementing its logic. Essentially, this is a form of "division of labor" at the program code level. In house construction, a foreman manages the team, a lumberjack cuts trees, a loader carries logs, a painter paints walls, a plumber lays pipes, a designer creates the interior, etc. Everyone is busy with their own work and works only within their competencies. In SRP, everything is exactly the same. For example, RequestHandler processes HTTP requests, FileStorage manages local files, Logger records information, and AuthManager checks access rights. As they say, "flies separately, cutlets separately." If a class has several responsibilities, they need to be separated. Naturally, SRP directly affects code cohesion and coupling. Both properties are similar in sound but differ in meaning: Cohesion: A positive characteristic meaning logical integrity of classes relative to each other. The higher the cohesion, the narrower the class functionality. Coupling: A negative characteristic meaning logical dependency of classes on each other. The higher the coupling, the more strongly the functionality of one class is intertwined with the functionality of another class. SRP strives to increase cohesion but decrease coupling of classes. Each class solves its narrow task, remaining as independent as possible from the external environment (other classes). However, all classes can (and should) still interact with each other through interfaces. Example of SRP Violation An object of a class capable of performing many diverse functions is sometimes called a god object, i.e., an instance of a class that takes on too many responsibilities, performing many logically unrelated functions, for example, business logic management, data storage, database work, sending notifications, etc. Example code in Python where SRP is violated: # implementation of god object class class DataProcessorGod: # data loading method def load(self, file_path): with open(file_path, 'r') as file: return file.readlines() # data processing method def transform(self, data): return [line.strip().upper() for line in data] # data saving method def save(self, file_path, data): with open(file_path, 'w') as file: file.writelines("\n".join(data)) # creating a god object justGod = DataProcessorGod() # data processing data = justGod.load("input.txt") processed_data = justGod.transform(data) justGod.save("output.txt", processed_data) The functionality of the program from this example can be divided into two types: File operations Data transformation Accordingly, to create a more optimal level of abstractions that allows easy scaling of the program in the future, it is necessary to allocate each functionality its own separate class. Example of SRP Application The shown program is best represented as two specialized classes that don't know about each other: DataManager: For file operations.  DataTransformer: For data transformation. Example code in Python where SRP is used: class DataManager: def load(self, file_path): with open(file_path, 'r') as file: return file.readlines() def save(self, file_path, data): with open(file_path, 'w') as file: file.writelines("\n".join(data)) class DataTransformer: def transform(self, data): return [line.strip().upper() for line in data.text] # creating specialized objects manager = DataManager() transformer = DataTransformer() # data processing data = manager.load("input.txt") processed_data = transformer.transform(data) manager.save("output.txt", processed_data) In this case, DataManager and DataTransformer interact with each other using strings that are passed as arguments to their methods. In a more complex implementation, there could exist an additional Data class used for transferring data between different program components: class Data: def __init__(self): self.text = "" class DataManager: def load(self, file_path, data): with open(file_path, 'r') as file: data.text = file.readlines() def save(self, file_path, data): with open(file_path, 'w') as file: file.writelines("\n".join(data.text)) class DataTransformer: def transform(self, data): data.text = [line.strip().upper() for line in data.text] # creating specialized objects manager = DataManager() transformer = DataTransformer() # data processing data = Data() manager.load("input.txt", data) transformer.transform(data) manager.save("output.txt", data) In this case, low-level data operations are wrapped in user classes. Such an implementation is easy to scale. For example, you can add many methods for working with files (DataManager) and data (DataTransformer), as well as complicate the internal representation of stored information (Data). SRP Advantages Undoubtedly, SRP simplifies application maintenance, makes code readable, and reduces dependency between program parts: Increased scalability: Adding new functions to the program doesn't confuse its logic. A class solving only one task is easier to change without risk of breaking other parts of the system. Reusability: Logically coherent components implementing program logic can be reused to create new behavior. Testing simplification: Classes with one responsibility are easier to cover with unit tests, as they don't contain unnecessary logic inside. Improved readability: Logically related functions wrapped in one class look more understandable. They are easier to understand, make changes to, and find errors in. Collaborative development: Logically separated code can be written by several programmers at once. In this case, each works on a separate component. In other words, a class should be responsible for only one task. If several responsibilities are concentrated in a class, it's more difficult to maintain without side effects for the entire program. Open/Closed Principle (OCP) OCP (Open/Closed Principle) is the open/closed principle, which states that code should be open for extension but closed for modification. In other words, program behavior modification is carried out only by adding new components. New functionality is layered on top of the old. In practice, OCP is implemented through inheritance, interfaces, abstractions, and polymorphism. Instead of changing existing code, new classes and functions are added. For example, instead of implementing a single class that processes all HTTP requests (RequestHandler), you can create one connection manager class (HTTPManager) and several classes for processing different HTTP request methods: RequestGet, RequestPost, RequestDelete. At the same time, request processing classes inherit from the base handler class, Request. Accordingly, implementing new request processing methods will require not modifying already existing classes, but adding new ones. For example, RequestHead, RequestPut, RequestConnect, RequestOptions, RequestTrace, RequestPatch. Example of OCP Violation Without OCP, any change in program operation logic (its behavior) will require modification of its components. Example code in Python where OCP is violated: # single request processing class class RequestHandler: def handle_request(self, method): if method == "GET": return "Processing GET request" elif method == "POST": return "Processing POST request" elif method == "DELETE": return "Processing DELETE request" elif method == "PUT": return "Processing PUT request" else: return "Method not supported" # request processing handler = RequestHandler() print(handler.handle_request("GET")) # Processing GET request print(handler.handle_request("POST")) # Processing POST request print(handler.handle_request("PATCH")) # Method not supported Such implementation violates OCP. When adding new methods, you'll have to modify the RequestHandler class, adding new elif processing conditions. The more complex a program with such architecture becomes, the harder it will be to maintain and scale. Example of OCP Application The request handler from the example above can be divided into several classes in such a way that subsequent program behavior changes don't require modification of already created classes. Abstract example code in Python where OCP is used: from abc import ABC, abstractmethod # base request handler class class Request(ABC): @abstractmethod def handle(self): pass # classes for processing different HTTP methods class RequestGet(Request): def handle(self): return "Processing GET request" class RequestPost(Request): def handle(self): return "Processing POST request" class RequestDelete(Request): def handle(self): return "Processing DELETE request" class RequestHead(Request): def handle(self): return "Processing HEAD request" class RequestPut(Request): def handle(self): return "Processing PUT request" class RequestConnect(Request): def handle(self): return "Processing CONNECT request" class RequestOptions(Request): def handle(self): return "Processing OPTIONS request" class RequestTrace(Request): def handle(self): return "Processing TRACE request" class RequestPatch(Request): def handle(self): return "Processing PATCH request" # connection manager class class HTTPManager: def __init__(self): self.handlers = {} def register_handler(self, method: str, handler: Request): self.handlers[method.upper()] = handler def handle_request(self, method: str): handler = self.handlers.get(method.upper()) if handler: return handler.handle() return "Method not supported" # registering handlers in the manager http_manager = HTTPManager() http_manager.register_handler("GET", RequestGet()) http_manager.register_handler("POST", RequestPost()) http_manager.register_handler("DELETE", RequestDelete()) http_manager.register_handler("PUT", RequestPut()) # request processing print(http_manager.handle_request("GET")) print(http_manager.handle_request("POST")) print(http_manager.handle_request("PUT")) print(http_manager.handle_request("TRACE")) In this case, the base Request class is implemented using ABC and @abstractmethod: ABC (Abstract Base Class): This is a base class in Python from which you cannot create an instance directly. It is needed exclusively for defining subclasses. @abstractmethod: A decorator designating a method as abstract. That is, each subclass must implement this method, otherwise creating its instance will be impossible. Despite the fact that the program code became longer and more complex, its maintenance was significantly simplified. The handler implementation now looks more structured and understandable. OCP Advantages Following OCP endows the application development process with some advantages: Clear extensibility: Program logic can be easily supplemented with new functionality. At the same time, already implemented components remain unchanged. Error reduction: Adding new components is safer than changing already existing ones. The risk of breaking an already working program is small, and errors after additions probably come from new components. Actually, OCP can be compared with SRP in terms of ability to isolate the implementation of individual classes from each other. The difference is only that SRP works horizontally, and OCP vertically. For example, in the case of SRP, the Request class is logically separated from the Handler class horizontally. This is SRP. At the same time, the RequestGet and RequestPost classes, which specify the request method, are logically separated from the Request class vertically, although they are its inheritors. This is OCP. All three classes (Request, RequestGet, RequestPost) are fully subjective and autonomous; they can be used separately. Just like Handler. Although, of course, this is a matter of theoretical interpretations. Thus, thanks to OCP, you can create new program components based on old ones, leaving both completely independent entities. Liskov Substitution Principle (LSP) LSP (Liskov Substitution Principle) is the Liskov substitution principle, which states that objects in a program should be replaceable by their inheritors without changing program correctness. In other words, inheritor classes should completely preserve the behavior of their parents. Barbara Liskov is an American computer scientist specializing in data abstractions. For example, there is a Vehicle class. Car and Helicopter classes inherit from it. Tesla inherits from Car, and Apache from Helicopter. Thus, each subsequent class (inheritor) adds new properties to the previous one (parent). Vehicles can start and turn off engines. Cars are capable of driving. Helicopters, flying. At the same time, the Tesla car model is capable of using autopilot, and Apache, radio broadcasting. This creates a kind of hierarchy of abilities: Vehicles start and turn off engines. Cars start and turn off engines, and, as a consequence, drive. Tesla starts and turns off the engine, drives, and uses autopilot. Helicopters start and turn off engines, and, as a consequence, fly. Apache starts and turns off engine, flies, and radio broadcasts. The more specific the vehicle class, the more abilities it possesses. But basic abilities are also preserved. Example of LSP Violation Example code in Python where LSP is violated: class Vehicle: def __init__(self): self.x = 0 self.y = 0 self.z = 0 self.engine = False def on(self): if not self.engine: self.engine = True return "Engine started" else: return "Engine already started" def off(self): if self.engine: self.engine = False return "Engine turned off" else: return "Engine already turned off" def move(self): if self.engine: self.x += 10 self.y += 10 self.z += 10 return "Vehicle moved" else: return "Engine not started" # various vehicle classes class Car(Vehicle): def move(self): if self.engine: self.x += 1 self.y += 1 return "Car drove" else: return "Engine not started" class Helicopter(Vehicle): def move(self): if self.engine: self.x += 1 self.y += 1 self.z += 1 return "Helicopter flew" else: return "Engine not started" def radio(self): return "Buzz...buzz...buzz..." In this case, the parent Vehicle class has a move() method denoting vehicle movement. Inheriting classes override the basic Vehicle behavior, setting their own movement method. Example of LSP Application Following LSP, it's logical to assume that Car and Helicopter should preserve movement ability, adding unique types of movement on their own: driving and flying. Example code in Python where LSP is used: # base vehicle class class Vehicle: def __init__(self): self.x = 0 self.y = 0 self.z = 0 self.engine = False def on(self): if not self.engine: self.engine = True return "Engine started" else: return "Engine already started" def off(self): if self.engine: self.engine = False return "Engine turned off" else: return "Engine already turned off" def move(self): if self.engine: self.x += 10 self.y += 10 self.z += 10 return "Vehicle moved" else: return "Engine not started" # various vehicle classes class Car(Vehicle): def ride(self): if self.engine: self.x += 1 self.y += 1 return "Car drove" else: return "Engine not started" class Helicopter(Vehicle): def fly(self): if self.engine: self.x += 1 self.y += 1 self.z += 1 return "Helicopter flew" else: return "Engine not started" def radio(self): return "Buzz...buzz...buzz..." class Tesla(Car): def __init__(self): super().__init__() self.autopilot = False def switch(self): if self.autopilot: self.autopilot = False return "Autopilot turned off" else: self.autopilot = True return "Autopilot turned on" class Apache(Helicopter): def __init__(self): super().__init__() self.frequency = 103.4 def radio(self): if self.frequency != 0: return "Buzz...buzz...Copy, how do you hear? [" + str(self.frequency) + " GHz]" else: return "Seems like the radio isn't working..." In this case, Car and Helicopter, just like Tesla and Apache derived from them, will preserve the original Vehicle behavior. Each inheritor adds new behavior to the parent class but preserves its own. LSP Advantages Code following LSP works with parent classes the same way as with their inheritors. This way you can implement interfaces capable of interacting with objects of different types but with common properties. Interface Segregation Principle (ISP) ISP (Interface Segregation Principle) is the interface segregation principle, which states that program classes should not depend on methods they don't use. This means that each class should contain only the methods it needs. It should not "drag" unnecessary "baggage" with it. Therefore, instead of one large interface, it's better to create several small specialized interfaces. In many ways, ISP has features of SRP and LSP, but differs from them. Example of ISP Violation Example code in Python that ignores ISP: # base vehicle class Vehicle: def __init__(self): self.hp = 100 self.power = 0 self.wheels = 0 self.frequency = 103.4 def ride(self): if self.power > 0 and self.wheels > 0: return "Driving" else: return "Standing" # vehicles class Car(Vehicle): def __init__(self): super().__init__() self.hp = 80 self.power = 250 self.wheels = 4 class Bike(Vehicle): def __init__(self): super().__init__() self.hp = 60 self.power = 150 self.wheels = 2 class Helicopter(Vehicle): def __init__(self): super().__init__() self.hp = 120 self.power = 800 def fly(self): if self.power > 0 and self.propellers > 0: return "Flying" else: return "Standing" def radio(self): if self.frequency != 0: return "Buzz...buzz...Copy, how do you hear? [" + str(self.frequency) + " GHz]" else: return "Seems like the radio isn't working..." # creating vehicles bmw = Car() ducati = Bike() apache = Helicopter() # operating vehicles print(bmw.ride()) # OUTPUT: Driving print(ducati.ride()) # OUTPUT: Driving print(apache.ride()) # OUTPUT: Standing (redundant method) print(apache.radio()) # OUTPUT: Buzz...buzz...Copy, how do you hear? [103.4 GHz] In this case, the base vehicle class implements properties and methods that are redundant for some of its inheritors. Example of ISP Application Example code in Python that follows ISP: # simple vehicle components class Body: def __init__(self): self.hp = 100 class Engine: def __init__(self): self.power = 0 class Radio: def __init__(self): self.frequency = 103.4 def communicate(self): if self.frequency != 0: return "Buzz...buzz...Copy, how do you hear? [" + str(self.frequency) + " GHz]" else: return "Seems like the radio isn't working..." # complex vehicle components class Suspension(Engine): def __init__(self): super().__init__() self.wheels = 0 def ride(self): if self.power > 0 and self.wheels > 0: return "Driving" else: return "Standing" class Frame(Engine): def __init__(self): super().__init__() self.propellers = 0 def fly(self): if self.power > 0 and self.propellers > 0: return "Flying" else: return "Standing" # vehicles class Car(Body, Suspension): def __init__(self): super().__init__() self.hp = 80 self.power = 250 self.wheels = 4 class Bike(Body, Suspension): def __init__(self): super().__init__() self.hp = 60 self.power = 150 self.wheels = 2 class Helicopter(Body, Frame, Radio): def __init__(self): super().__init__() self.hp = 120 self.power = 800 self.propellers = 2 self.frequency = 107.6 class Plane(Body, Frame): def __init__(self): super().__init__() self.hp = 200 self.power = 1200 self.propellers = 4 # creating vehicles bmw = Car() ducati = Bike() apache = Helicopter() boeing = Plane() # operating vehicles print(bmw.ride()) # OUTPUT: Driving print(ducati.ride()) # OUTPUT: Driving print(apache.fly()) # OUTPUT: Flying print(apache.communicate()) # OUTPUT: Buzz...buzz...Copy, how do you hear? [107.6 GHz] print(boeing.fly()) # OUTPUT: Flying Thus, all vehicles represent a set of components with their own properties and methods. No finished vehicle class carries an unnecessary element or capability "on board." ISP Advantages Thanks to ISP, classes contain only the necessary variables and methods. Moreover, dividing large interfaces into small ones allows specializing logic in the spirit of SRP. This way interfaces are built from small blocks, like a constructor, each of which implements only its zone of responsibility. Dependency Inversion Principle (DIP) DIP (Dependency Inversion Principle) is the dependency inversion principle, which states that upper-level components should not depend on lower-level components. In other words, abstractions should not depend on details. Details should depend on abstractions. Such architecture is achieved through common interfaces that hide the implementation of underlying objects. Example of DIP Violation Example code in Python that doesn't follow DIP: # projector class Light(): def __init__(self, wavelength): self.wavelength = wavelength def use(self): return "Lighting [" + str(self.wavelength) + " nm]" # helicopter class Helicopter: def __init__(self, color="white"): if color == "white": self.light = Light(600) elif color == "blue": self.light = Light(450) elif color == "red": self.light = Light(650) def project(self): return self.light.use() # creating vehicles helicopterWhite = Helicopter("white") helicopterRed = Helicopter("red") # operating vehicles print(helicopterWhite.project()) # OUTPUT: Lighting [600 nm] print(helicopterRed.project()) # OUTPUT: Lighting [650 nm] In this case, the Helicopter implementation depends on the Light implementation. The helicopter must consider the projector configuration principle, passing certain parameters to its object. Moreover, the script similarly configures the Helicopter using a boolean variable. If the projector or helicopter implementation changes, the configuration parameters may stop working, which will require modification of upper-level object classes. Example of DIP Application The projector implementation should be completely isolated from the helicopter implementation. Vertical interaction between both entities should be performed through a special interface. Example code in Python that considers DIP: from abc import ABC, abstractmethod # base projector class class Light(ABC): @abstractmethod def use(self): pass # white projector class NormalLight(Light): def use(self): return "Lighting with bright white light" # red projector class SpecialLight(Light): def use(self): return "Lighting with dim red light" # helicopter class Helicopter: def __init__(self, light): self.light = light def project(self): return self.light.use() # creating vehicles helicopterWhite = Helicopter(NormalLight()) helicopterRed = Helicopter(SpecialLight()) # operating vehicles print(helicopterWhite.project()) # OUTPUT: Lighting with bright white light print(helicopterRed.project()) # OUTPUT: Lighting with dim red light In such architecture, the implementation of a specific projector, whether NormalLight or SpecialLight, doesn't affect the Helicopter device. On the contrary, the Helicopter class sets requirements for the presence of certain methods in the Light class and its inheritors. DIP Advantages Following DIP reduces program coupling: upper-level code doesn't depend on implementation details, which simplifies component modification or replacement. Thanks to active use of interfaces, new implementations (inherited from base classes) can be added to the program, which can be used with existing components. In this, DIP overlaps with LSP. In addition to this, during testing, instead of real lower-level dependencies, empty stubs can be substituted that simulate the functions of real components. For example, instead of making a request to a remote server, you can simulate delay using a function like time.sleep(). And in general, DIP significantly increases program modularity, vertically encapsulating component logic. Practical Application of SOLID SOLID principles help write flexible, maintainable, and scalable code. They are especially relevant when developing backends for high-load applications, working with microservice architecture, and using object-oriented programming. Essentially, SOLID is aimed at localization (increasing cohesion) and encapsulation (decreasing coupling) of application component logic both horizontally and vertically. Whatever syntactic constructions a language possesses (perhaps it weakly supports OOP), it allows following SOLID principles to one degree or another. How SOLID Helps in Real Projects As a rule, each iteration of a software product either adds new behavior or changes existing behavior, thereby increasing system complexity. However, complexity growth often leads to disorder. Therefore, SOLID principles set certain architectural frameworks within which a project remains understandable and structured. SOLID doesn't allow chaos to grow. In real projects, SOLID performs several important functions: Facilitates making changes Divides complex systems into simple subsystems Reduces component dependency on each other Facilitates testing Reduces errors and makes code predictable Essentially, SOLID is a generalized set of rules based on which software abstractions and interactions between different application components are formed. SOLID and Architectural Patterns SOLID principles and architectural patterns are two different but interconnected levels of software design. SOLID principles exist at a lower implementation level, while architectural patterns exist at a higher level. That is, SOLID can be applied within any architectural pattern, whether MVC, MVVM, Layered Architecture, Hexagonal Architecture. For example, in a web application built on MVC, one controller can be responsible for processing HTTP requests, and another for executing business logic. Thus, the implementation will follow SRP. Moreover, within MVC, all dependencies can be passed through interfaces rather than created inside classes. This, in turn, will be following DIP. SOLID and Code Testability The main advantage of SOLID is increasing code modularity. Modularity is an extremely useful property for unit testing. After all, classes performing only one task are easier to test than classes consisting of logical "hodgepodge." To some extent, testing itself begins to follow SRP, performing multiple small and specialized tests instead of one scattered test. Moreover, thanks to OCP, adding new functionality doesn't break existing tests, but leaves them still relevant, despite the fact that the overall program behavior may have changed. Actually, tests can be considered a kind of program snapshot. Exclusively in the sense that they frame application logic and test its implementation. Therefore, there's nothing surprising in the fact that tests follow the same principles and architectural patterns as the application itself. Criticism and Limitations of SOLID Excessive adherence to SOLID can lead to fragmented code with many small classes and interfaces. In small projects, strict separations may be excessive. When SOLID May Be Excessive SOLID principles are relevant in any project. Following them is good practice. However, complex SOLID abstractions and interfaces may be excessive for simple projects. On the contrary, in complex projects, SOLID can simplify code understanding and help scale implementation. In other words, if a project is small, fragmenting code into many classes and interfaces is unnecessary. For example, dividing logic into many classes in a simple Telegram bot will only complicate maintenance. The same applies to code for one-time use (for example, one-time task automation). Strict adherence to SOLID in this case will be a waste of time. It must be understood that SOLID is not a dogma, but a tool. It should be applied where it's necessary to improve code quality, not complicate it unnecessarily. Sometimes it's easier to write simple and monolithic code than fragmented and overcomplicated code. Alternative Design Approaches Besides SOLID, there are other principles, approaches, and software design patterns that can be used both separately and as a supplement to SOLID: GRASP (General Responsibility Assignment Software Patterns): A set of responsibility distribution patterns describing class interactions with each other. YAGNI (You Ain't Gonna Need It): The principle of refusing excessive functionality that is not immediately needed. KISS (Keep It Simple, Stupid): A programming principle declaring simplicity as the main value of software. DRY (Don't Repeat Yourself): A software development principle minimizing code duplication. CQS (Command-Query Separation): A design pattern dividing operations into two categories: commands that change system state and queries that get data from the system. DDD (Domain-Driven Design): A software development approach structuring code around the enterprise domain. Nevertheless, no matter how many approaches there are, the main thing is to apply them thoughtfully, not blindly follow them. SOLID is a useful tool, but it needs to be applied consciously.
29 September 2025 · 25 min to read

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