What is Mobile App Hosting? 10 Main Principles of Mobile Hosting and 5 Benefits

Machine learning and artificial intelligence in 2025 continue to transform business processes, from logistics automation to personalization of customer services. However, regular processors (CPUs) are no longer sufficient for effective work with neural networks. Graphics cards for AI (GPUs) have become a key tool for accelerating model training, whether it's computer vision, natural language processing, or generative AI. Why GPUs Are Essential for ML and AI Graphics cards for AI are not just computing devices, but a strategic asset for business. They allow reducing the development time of AI solutions, minimizing costs, and bringing products to market faster. In 2025, neural networks are applied everywhere: from demand forecasting in retail to medical diagnostics. GPUs provide parallel computing necessary for processing huge volumes of data. This is especially important for companies where time and accuracy of forecasts directly affect profit. Why CPU Cannot Handle ML Tasks Processors (CPUs) are optimized for sequential computing. Their architecture with 4-32 cores is suitable for tasks like text processing or database management. However, machine learning requires performing millions of parallel operations, such as matrix multiplication or gradient descent. CPUs cannot keep up with such loads, making them ineffective for modern neural networks. Example: training a computer vision model for defect recognition in production. With CPU, the process can take weeks, and errors due to insufficient power lead to downtime. For business, this means production delays and financial losses. Additionally, CPUs do not support optimizations such as low-precision computing (FP16), which accelerate ML without loss of quality. The Role of GPU in Accelerating Model Training GPUs with thousands of cores (from 2,000 to 16,000+) are designed for parallel computing. They process tensor operations that form the basis of neural networks, tens of times faster than CPUs. In 2025, this is especially noticeable when working with large language models (LLMs), generative networks, and computer vision systems. Key GPU Specifications for ML Let’s talk about factors to consider when selecting GPUs for AI.  Choosing a graphics card for machine learning requires analysis of technical parameters that affect performance and profitability. In 2025, the market offers many models, from budget to professional. For business, it's important to choose a GPU that will accelerate development and reduce operational costs. 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.

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.

Modern IT systems are becoming increasingly complex: cloud technologies, microservices, and distributed architectures require not only speed of development but also uninterrupted operation. Against this backdrop, demand for automation and infrastructure reliability is growing. This is where two key methodologies come to the forefront: DevOps and SRE (Site Reliability Engineering). Despite common goals—accelerating product delivery and improving system stability—there are fundamental differences between them. Many still ask themselves: What does an SRE engineer actually do in practice? How are DevOps and SRE related? Are they competitors or allies? Why are these roles so often confused? These questions arise for good reason. Both disciplines use similar tools (Kubernetes, Terraform), implement CI/CD, and fight routine through automation. However, there is a difference in focus: DevOps strives to break down barriers between developers and operations, while SRE engineers concentrate on "reliability engineering": predictability, fault tolerance, and metrics like SLO (Service Level Objectives). The goal of this article is not just to compare SRE and DevOps, but also to show how they complement each other. From this material you will learn: What tasks each methodology solves and where they intersect Why Netflix or Google cannot do without SRE, while startups more often choose DevOps How to choose an approach that will suit your company specifically We will examine real cases, metrics, and even conflicting viewpoints so you can find a balance between speed and stability, as well as understand when to give preference to one methodology or another. What are SRE and DevOps? In the world of IT infrastructure and development, two terms are heard most often: DevOps and SRE (Site Reliability Engineering). They are often confused, roles are mixed, or they are considered synonyms, but in practice these are different approaches with unique goals and methods. Let's understand what stands behind each of them and how they relate. SRE: Site Reliability Engineering SRE is a discipline that transforms IT system support into engineering science. It was created at Google in 2003 to manage global services like search and YouTube. The main task of an SRE engineer is to guarantee that the system works stably, even under extreme loads. Key SRE Principles: Reliability Above All: Using SLO (Service Level Objectives) metrics to measure availability (for example, 99.99% uptime). If the system is stable, part of the resources is allocated to implementing new features. Automation of Routine: Eliminating manual operations: deployment, monitoring, incident handling. For example, self-healing clusters in Kubernetes. Error Budgets: If the system meets SLO, the team can take risks by testing updates. If the budget is exhausted, focus shifts to fixing errors. Postmortems: Detailed analysis of each failure to prevent its recurrence. DevOps: Culture of Continuous Delivery DevOps is a philosophy that breaks down the barrier between developers (Dev) and operations (Ops). Its goal is to accelerate product release without losing quality. Unlike SRE, DevOps is not tied to specific metrics; it's more of a set of practices and tools for improving processes. Main DevOps Principles: Continuous Integration and Delivery (CI/CD): Automation of testing, building, and deployment. Tools: Jenkins, GitLab CI, GitHub Actions. Infrastructure as Code (IaC): Managing servers through configuration files (Terraform, Ansible) instead of manual settings. Collaboration Culture: Developers and operations work in a unified team, sharing responsibility for releases. Fast Recovery: Minimizing time to fix failures (MTTR metric, Mean Time To Repair). Practical example: Etsy company implemented DevOps practices and increased deployment frequency to 50 times per day. This allowed them to quickly test hypotheses and reduce the number of critical bugs. SRE vs DevOps: Brief Comparison Criterion SRE DevOps Main Goal Maximum system reliability Speed and stability of releases Metrics SLO, Error Budgets, SLI Deployment frequency, MTTR, Lead Time Tools Prometheus, Grafana, PagerDuty Jenkins, Docker, Kubernetes Approach to Risks Clear frameworks through Error Budgets Flexibility and experiments Why are SRE and DevOps So Often Confused? Both methodologies: Use automation to eliminate manual labor Work with the same tools (for example, Kubernetes) Strive for a balance between speed and stability The main difference is in priorities: SRE engineer asks: "How to make the system fault-tolerant?" DevOps asks: "How to deliver code to users faster?" SRE often becomes a logical development of DevOps in large companies where reliability becomes critical. Key Differences Between SRE and DevOps While DevOps and SRE strive to improve IT processes, their approaches and priorities differ significantly. These differences influence how companies implement methodologies, measure success, and distribute roles in teams. Let's examine the key aspects that separate the two disciplines. Focus on Reliability vs Focus on Process SRE: Reliability Engineering as Foundation SRE engineer concentrates on ensuring the system works without failures, even under extreme load conditions. For example, Netflix uses SRE practices to ensure streaming stability with millions of simultaneous connections. The main tool is SLO (Service Level Objectives): clear availability metrics. If the system is stable, the team spends "error budget" on experiments with new features. If the budget is exhausted, all resources go to fixing errors. DevOps: Speed and Process Efficiency DevOps focuses on optimizing code delivery processes from development to production. For example, Amazon deploys code every 11.7 seconds on average thanks to DevOps practices. Priorities: release speed, CI/CD automation, reducing communication time between teams. Reliability is important but secondary: first, deliver functionality to users, then, improve stability. Conflict example: a company implements a new feature through DevOps approach, but SRE engineer blocks the release because tests showed risk of SLO violation. Here a balance between innovation and stability is needed. Metrics and Approaches to Efficiency Assessment SRE: Measuring Reliability SRE metrics quantitatively assess how well the system meets user expectations: SLA (Service Level Agreement): contractual availability level (for example, 99.95%). SLI (Service Level Indicator): actual indicators (latency, error rate). Error Budget: acceptable downtime per month (for example, 43 minutes at 99.95% SLA). If SLI falls below SLO, the team is obligated to pause releases and focus on stability. DevOps: Assessing Speed and Process Quality DevOps metrics show how efficiently the development cycle works: Deployment Frequency: how many times per day/week code reaches production. Lead Time: time from commit to release. MTTR (Mean Time To Recovery): average recovery time after failure. Example: DevOps team is proud of 20 deployments per day, but SRE engineer points out that 5 of them led to SLO violations. Joint metric analysis is required here. Approach to Automation SRE: Automation for Error Prevention SRE engineer automates tasks that can lead to failures: Self-healing systems: automatic restart of failed services. Problem prediction: ML algorithms for log analysis and incident prevention. Orchestration: tools like Kubernetes for cluster management without manual intervention. Example: At Google, SRE automation allows handling 90% of incidents without human involvement. DevOps: Automation for Acceleration DevOps uses automation to eliminate manual bottlenecks: CI/CD pipelines: automatic tests, building, and deployment. Infrastructure as Code (Terraform, Ansible): rapid environment deployment. Monitoring: tools like Prometheus for real-time performance tracking. Example: Spotify company reduced microservice deployment time from hours to minutes using DevOps automation. Comparative Table Criterion SRE DevOps Main Focus Reliability and fault tolerance Code delivery speed and collaboration Key Metrics SLO, SLI, Error Budgets Deployment frequency, Lead Time, MTTR Automation Failure prevention, self-recovery CI/CD acceleration, infrastructure management Why are These Differences Important? For startups, speed is often critical, so the choice falls on DevOps. Large companies (banks, cloud platforms) choose SRE where failures cost millions. In hybrid teams, SRE engineers and DevOps work together: the first monitors reliability metrics, the second optimizes processes. SRE often becomes an "evolution" of DevOps in mature organizations where reliability becomes a KPI. Interconnection and Intersection Points of SRE and DevOps Despite differences in focus, SRE and DevOps do not oppose each other; they complement and strengthen IT processes. Their interaction resembles symbiosis: DevOps sets speed and flexibility, while SRE engineer adds reliability control. Let's examine where their paths intersect and how they create a unified ecosystem. Common Goals: Balance Between Speed and Stability Both methodologies strive for the same thing: making IT systems efficient and predictable. They are united by: Reducing manual labor through automation. Accelerating feedback between developers and operations. Minimizing downtime. Tools: One Set, but Different Priorities Both DevOps and SRE use the same tools but apply them for different tasks: Tool DevOps SRE Kubernetes Microservice orchestration, fast deployment Managing cluster fault tolerance Terraform Infrastructure deployment "as code" Automated resource recovery Prometheus Real-time performance monitoring Metric analysis for SLO compliance Example: Spotify uses Kubernetes both for automatic service scaling (DevOps) and load balancing during failures (SRE). Cultural Principles of DevOps and SRE DevOps emphasizes team interaction. The methodology breaks down barriers between developers and operations, betting on cross-functional collaboration. For example, daily standups with both teams are conducted for quick problem resolution. SRE emphasizes systematicity and measurements. Here engineering rigor comes to the forefront: operations becomes an exact science with availability metrics, errors, and automated recovery scenarios. How this works in practice: A DevOps engineer sets up CI/CD pipelines for frequent releases. An SRE engineer establishes limits through Error Budget so releases don't violate stability. If SLO is under threat, teams jointly decide: accelerate fixes or temporarily freeze innovations. Hybrid Roles: DevOps Engineer vs SRE In small companies, one specialist can combine both roles: Sets up CI/CD (DevOps). Implements SLO for monitoring (SRE). Uses infrastructure as code for speed and reliability balance. Practical example: a fintech startup uses GitLab CI for daily deployments (DevOps) and Grafana for SLO tracking (SRE). This allows them to scale without hiring separate teams. SRE and DevOps Intersection Points Criterion Common Elements Automation CI/CD, orchestration, infrastructure management Metrics MTTR (recovery time), incident frequency Culture Responsibility for stability at all stages Tools Kubernetes, Terraform, Prometheus, Docker Why is SRE Called "Advanced DevOps"? SRE often emerges where DevOps reaches its limits: In large companies with high uptime requirements. In projects where errors cost millions (medicine, finance). When a systematic approach to reliability management is needed. Example: Google, which created SRE, initially used DevOps practices, but the scale of services required more rigorous discipline. When Should Companies Hire SRE Engineers vs DevOps? The choice between SRE and DevOps depends on company scale, process maturity, and project specifics. Sometimes these roles are combined, but more often they complement each other. Let's examine when SRE engineers are needed and where classic DevOps is more effective. Small Companies vs Large Corporations DevOps is the optimal choice for startups and small teams for the following reasons: Small infrastructure: deep SLO setup is not required. Flexibility: need to quickly release MVP and test hypotheses. Budget: hiring a separate SRE engineer is economically impractical. Example: A mobile startup uses GitHub Actions for CI/CD and Heroku for deployment. DevOps engineer here combines developer and operations roles. For corporations and corporate projects, SRE becomes necessary for the following reasons: High risks: downtime costs millions (for example, banks, trading platforms). Complex architecture: microservices, distributed systems, hybrid clouds. Strict SLA: for example, 99.999% uptime for financial transactions. Example: In a taxi service, SRE engineers monitor service stability during peak loads during rush hour. Which Projects Need SRE? SRE engineer is critically important in projects where: Reliability is the main KPI. For example, in cloud platforms (AWS, Google Cloud) or medical systems where failures threaten patient lives. High traffic, such as social networks (Facebook, TikTok) or streaming services (Twitch, Netflix). Complex infrastructure. For example, distributed databases (Cassandra, Kafka) or multi-regional clusters. Example: at Uber, SRE engineers manage a global booking system where even 5 minutes of downtime leads to $1.8 million loss. Where is DevOps More Effective? DevOps dominates in scenarios where important factors are: Code delivery speed. Such projects include mobile applications with frequent updates to fix bugs or E-commerce: quick implementation of seasonal features (for example, Black Friday). Flexible methodologies, such as Agile/Scrum, where quick feedback and regular short sprints are important. Non-standard projects. For example, MVP for startups: need to test ideas without deep optimization or various research tasks requiring AI/ML experiments. Example: Slack company uses DevOps practices to deploy new features several times a day, maintaining balance between speed and stability. SRE vs DevOps: Choice for Projects Criterion SRE DevOps Company Type Large corporations, corporate projects Startups, small and medium business Projects High-load systems, critical to downtime MVP, products with frequent updates Budget High: SRE salary, expensive tools Moderate: cloud services, open-source Risks Financial/reputational losses during failures Time loss on routine Can SRE and DevOps be Combined? Yes, and this often happens in medium-sized companies: DevOps sets up processes and CI/CD. SRE engineer connects at the growth stage when SLA requirements appear. Hybrid approach example: Airbnb uses DevOps for quick feature implementation and SRE for controlling booking and payment reliability. Conclusion SRE and DevOps are not opposing methodologies but complementary elements of a modern IT ecosystem. Both disciplines solve one task—making development and operations efficient—but approach it from different sides. SRE engineer focuses on reliability, using strict metrics (SLO, Error Budgets) and automation to prevent failures. This is the choice for large companies where downtime costs millions and systems operate under extreme loads. DevOps bets on speed and flexibility, breaking down barriers between teams and implementing CI/CD. This is the ideal option for startups and projects where quickly testing hypotheses is important. Intersection points are common tools (Kubernetes, Terraform), interaction culture, and striving for automation. In mature companies, SRE and DevOps work in tandem: one insures the other. Practical Advice: If you're just starting, begin with DevOps to establish processes. If your system is growing and reliability requirements are tightening, implement SRE. In corporate projects, combine both approaches, as Google and Airbnb do: DevOps for speed, SRE for control. SRE vs DevOps is not an "either-or" question, but a search for balance. It's precisely the combination of flexibility and rigor that allows creating products that are simultaneously innovative and stable. Choose a strategy that meets your goals and remember: in modern IT there's no room for compromises between speed and reliability.