How to Choose an OS for Your Virtual Server

A cloud hosting provider is a company that offers users virtual resources for remote infrastructure management and application deployment. Unlike traditional web hosting, cloud-based service providers allow for flexible configuration of rented resources, helping clients save on hardware, software, and system administration costs. In this article, we’ll review the key factors to consider when choosing a cloud hosting provider, starting with the core services these companies offer. Provided Services There are three main service models that cloud hosting companies typically offer. Ideally, a reliable provider should support all three: IaaS (Infrastructure as a Service): Basic infrastructure resources such as virtual servers, networks, and storage. PaaS (Platform as a Service): Software platforms for various tasks: database management, big data analytics, containerized app development, machine learning systems, and more. SaaS (Software as a Service): Fully managed software solutions that run on the provider’s infrastructure, reducing the load on the client’s computer or mobile device. Key features offered by best cloud providers include: A firewall to protect against DDoS attacks and malware. Automated backups with redundant data storage across multiple locations for disaster recovery. Data encryption to ensure confidentiality; even provider staff cannot access your information. Pricing When evaluating pricing, focus not just on the base rate but on what’s included in the package. Some providers attract customers with low prices, but cheaper plans often come with limited resources or features. For instance: Low-cost plans may not suit clients who handle large data volumes due to disk space limits or slow storage performance. Some providers may offer a “cheap” cloud server but fail to mention that your virtual resources are shared with other clients, reducing performance. Keep in mind: a high-performance server cannot be truly cheap. Company Experience As a rule, the longer a provider has been in the cloud hosting  business, the more reliable it tends to be. However, reputation also matters: look for verified online reviews rather than marketing claims. If a provider has been operating for over 5 years and maintains a solid reputation, it’s usually a trustworthy choice. A broad range of services is also a good indicator of expertise. Certification and Standards A strong advantage is certification under ISO 27001, the international standard for information security management. While not legally required, it shows that the company has a well-structured approach to security: defined access levels, regular internal and external audits, and continuous process improvement. Free Trial Period A trial period can significantly influence a provider’s credibility. If a provider offers 5–10 days (not just a day or two) for testing, it’s a positive sign that they’re confident in the quality of their services. Hardware Pay attention to the performance of CPUs and disk subsystems. Ideally, a provider should offer configurations for different needs, from entry-level setups to high-performance solutions using modern server-grade processors and NVMe drives, which significantly outperform traditional SSDs in speed and reliability. Reliability and SLA A reliable provider must guarantee service uptime in its Service Level Agreement (SLA), typically expressed as a minimum annual availability percentage. The SLA should also guarantee that you receive the computing power and software specified in your plan and that you can modify configurations, add or remove resources, and perform other key management tasks. Data Center Location Providers often advertise the geographic location of their servers as an advantage, but the data center’s certification level is far more important. Look for certification under Tier III, which represents the optimal reliability level (Tier I being the lowest and Tier IV the highest and most expensive). Tier III data centers can perform maintenance without downtime thanks to redundant infrastructure components. Technical Support The quality of technical support is a key differentiator. Pay attention to: Response time. It should be clearly stated in your contract. Willingness to help with tasks like auditing or migrating infrastructure from other services. Professionalism and courtesy—hallmarks of a customer-oriented provider. Contract Termination Even with the best cloud hosting provider, circumstances may change. Before signing up, check: How and when you can retrieve your data. How the provider destroys virtual machines and ensures complete data deletion upon termination. Checklist: Choosing a Cloud Hosting Provider Before making your decision, verify that your provider offers: Support for IaaS, PaaS, and SaaS models with additional features. Flexible, well-priced service packages. 5+ years of experience in the market. (Optional) ISO 27001 certification. A 5–10 day trial period for testing. Multiple hardware configurations with scalable performance. SLA-backed uptime guarantees and resource reliability. A Tier III–certified data center. Qualified, responsive technical support. A secure and transparent contract termination process.

Today, in the world of information technology, there are many different practices and methodologies. One of these methodologies is DevSecOps. In this article, we will discuss what DevSecOps is, how its processes are organized, which tools are used when implementing DevSecOps practices, and also why and when a business should adopt and use DevSecOps. What Is DevSecOps DevSecOps (an abbreviation of three words: development, security, and operations) is a methodology based on secure application development by integrating security tools to protect continuous integration, continuous delivery, and continuous deployment of software using the DevOps model. Previously, before the appearance of the DevSecOps methodology, software security testing was usually carried out at the very end of the process, after the product had already been released. DevSecOps fundamentally changes this approach by embedding security practices at every stage of development, not only when the product has been completed. This approach significantly increases the security of the development process and allows for the detection of a greater number of vulnerabilities. The DevSecOps methodology does not replace the existing DevOps model and processes but rather integrates additional tools into each stage. Just like DevOps, the DevSecOps model relies on a high degree of automation. Difference Between DevOps and DevSecOps Although DevOps and DevSecOps are very similar (the latter even uses the same development model as DevOps and largely depends on the same processes), the main difference between them is that the DevOps methodology focuses on building efficient processes between development, testing, and operations teams to achieve continuous and stable application delivery, while DevSecOps is focused exclusively on integrating security tools. While DevOps practices are concentrated on fixing development bugs, releasing updates regularly, and shortening the development life cycle, DevSecOps ensures information security. Stages of DevSecOps Since DevSecOps fully relies on DevOps, it uses the same stages as the DevOps model. The differences lie in the security measures taken and the tools used. Each tool is implemented and used strictly at its corresponding stage. Let’s consider these stages and the security measures applied at each of them. Plan Any development begins with planning the future project, including its architecture and functionality. The DevSecOps methodology is no exception. During the planning stage, security requirements for the future project are developed. This includes threat modeling, analysis and preliminary security assessment, and discussion of security tools to be used. Code At the coding stage, tools such as SAST are integrated. SAST (Static Application Security Testing), also known as “white-box testing”, is the process of testing applications for security by identifying vulnerabilities and security issues within the source code. The application itself is not executed; only the source code is analyzed. SAST also relies on compliance with coding guidelines and standards. Using SAST tools helps to identify and significantly reduce potential vulnerabilities at the earliest stage of development. Build At this stage, the program is built from source code into an executable file, resulting in an artifact ready for further execution. Once the program has been built, it is necessary to verify its internal functionality. This is where tools like DAST come into play. DAST (Dynamic Application Security Testing), also known as “black-box testing”, is the process of testing the functionality of a built and ready application by simulating real-world attacks on it. The main difference from SAST is that DAST does not analyze source code (and does not even require it); instead, it focuses solely on the functions of the running application. Test At the testing stage within DevSecOps, the focus is not only on standard testing such as automated tests, functional tests, and configuration tests, but also on security-oriented testing. This includes: Penetration testing (“pentest”) Regression testing Vulnerability scanning The goal of testing is to identify as many vulnerabilities as possible before deploying the final product to the production environment. Release After product testing has been fully completed, the release and deployment to production servers are prepared. At this stage, the security role involves setting up user accounts for access to servers and necessary components (monitoring, log collection systems, web interfaces of third-party systems), assigning appropriate access rights, and configuring firewalls or other security systems. Deploy During the deployment stage, security checks continue, now focusing on the environments where the product is deployed and installed. Additional configuration and security policy checks are performed. Monitoring Once the release has been successfully deployed, the process of tracking the performance of the released product begins. Infrastructure monitoring is also performed, not only for production environments but also for testing and development environments. In addition to tracking system errors, the DevSecOps process is used to monitor potential security issues using tools such as intrusion detection systems, WAF (Web Application Firewall), and traditional firewalls. SIEM systems are used to collect incident data. DevSecOps Tools DevSecOps processes use a variety of tools that significantly increase the security of developed applications and the supporting infrastructure. The integrated tools automatically test new code fragments added to the system. Alongside commercial products, many open-source solutions are also used, some offering extended functionality. Typically, all tools are divided into the following categories: Static code analysis tools: SonarQube, Semgrep, Checkstyle, Solar appScreener. Dynamic testing tools: Aikido Security, Intruder, Acunetix, Checkmarx DAST. Threat modeling tools: Irius Risk, Pirani, GRC Toolbox, MasterControl Quality Excellence. Build-stage analysis tools: OWASP Dependency-Check, SourceClear, Retire.js, Checkmarx. Docker image vulnerability scanners: Clair, Anchore, Trivy, Armo. Deployment environment security tools: Osquery, Falco, Tripwire. Implementing DevSecOps Before adopting DevSecOps practices in your company, it should be noted that this process does not happen instantly; it requires a well-thought-out, long-term implementation plan. Before implementation, make sure your company meets the following criteria: A large development team is in place. Development follows the DevOps model. Automation is extensively used in development processes. Applications are developed using microservice architecture. Development is aimed at a fast time-to-market. The process of implementing DevSecOps consists of the following main stages: Preparatory Stage At this stage, project participants are informed about the main ideas of using the DevSecOps methodology. It is important to introduce employees to the new security practice, explain the main advantages of the DevSecOps model, and how it helps solve security challenges. This can be done through seminars or specialized courses. Current State Assessment At this stage, it is necessary to ensure that DevOps processes are already established within the team and that automation is widely used. It’s also important to understand the current development processes of your product, identify existing security issues, conduct threat modeling if necessary, and assess potential vulnerabilities. Planning the DevSecOps Implementation At this stage, decisions are made regarding which tools will be used, how the security process will be structured, and how it will be integrated with the existing development process. After successful completion of the familiarization and planning stages, you can begin pilot implementation of DevSecOps practices. Start small, with smaller teams and projects. This allows for faster and more effective evaluation before expanding to larger projects and teams, gradually scaling DevSecOps adoption. It’s also necessary to constantly monitor DevSecOps processes, identify problems and errors that arise during implementation. Each team member should be able to provide feedback and suggestions for improving and evolving DevSecOps practices. Advantages of Using DevSecOps The main advantage of implementing the DevSecOps methodology for business lies in saving time and costs associated with security testing by the information security department. DevSecOps also guarantees a higher level of protection against potential security problems. In addition, the following benefits are noted when using DevSecOps: Early Detection of Security Threats During Development When using the DevSecOps methodology, security tools are integrated at every stage of development rather than after the product is released. This increases the chances of detecting security threats at the earliest stages of development. Reduced Time to Market To accelerate product release and improve time-to-market, DevSecOps processes can be automated. This not only reduces the time required to release a new product but also minimizes human error. Compliance with Security Requirements and Regulations This requirement is especially important for developing banking, financial, and other systems that handle sensitive information, as well as for companies working with large datasets. It’s also crucial to consider national legal frameworks if the product is being developed for a country with specific data protection regulations. For example, the GDPR (General Data Protection Regulation) used in the European Union. Emergence of a Security Culture The DevSecOps methodology exposes development and operations teams more deeply to security tools and methods, thereby expanding their knowledge, skills, and expertise. Why DevSecOps Is Necessary The following arguments support the need to use the DevSecOps methodology in business: Security threats and issues in source code: Vulnerabilities and security problems directly related to the source code of developed applications. Source code is the foundation of any program, and thousands of lines may contain vulnerabilities that must be found and eliminated. Security threats in build pipelines: One of the key conditions of DevOps is the use of pipelines for building, testing, and packaging products. Security risks can appear at any stage of the pipeline. External dependency threats: Problems related to the use of third-party components (dependencies) during development, including libraries, software components, scripts, and container images. Security threats in delivery pipelines: Vulnerabilities in systems and infrastructure used to deliver applications, including both local and cloud components. Conclusion The DevSecOps methodology significantly helps increase the level of security in your DevOps processes. The model itself does not alter the existing DevOps concept; instead, it supplements it with continuous security practices. It is also important to note that DevSecOps does not explicitly dictate which tools must be used, giving full freedom in decision-making. A well-implemented DevSecOps process in your company can greatly reduce security risks and accelerate the release of developed products to market.

Nowadays, artificial intelligence (AI) has literally burst into everyday life. It has long since moved beyond simple things like solving math problems—now AI handles much more serious challenges, such as processing huge volumes of data or preparing analytical reports.  In this article, we'll examine two AI models that have recently captured the artificial intelligence market: DeepSeek, created by the Chinese company DeepSeek AI, and ChatGPT, developed by the American company OpenAI. What Are DeepSeek and ChatGPT? DeepSeek is a free chatbot and artificial assistant created by the Chinese company DeepSeek AI in 2025. The development cost of DeepSeek also generated significant buzz in the media and social networks—it amounted to just $5.6 million. Moreover, DeepSeek's development used only 2048 NVIDIA chips. By February 2025, DeepSeek released several versions of its product—DeepSeek V3 and R1. Among their features were open-source code and free access, which significantly increased DeepSeek's popularity from the start. The DeepSeek model is oriented toward a wide range of tasks, including text generation, programming, and data analysis. ChatGPT is an AI-powered chatbot created by OpenAI, founded in 2015 by Elon Musk and Sam Altman. It was first shown to the world in November 2022 and immediately caused a sensation in the AI field. ChatGPT is based on the GPT (Generative Pre-trained Transformer) architecture. By 2025, newer, more advanced versions were released, such as GPT-4o and o1. However, there are downsides—to access all its capabilities, you need a paid subscription, unlike the free DeepSeek. Key Differences Between DeepSeek and ChatGPT DeepSeek and ChatGPT have a number of fundamental differences. The first difference is the distribution model. DeepSeek is positioned as an open platform: its source code is available on GitHub, and basic functions are provided free of charge through a web interface, API, and mobile applications. This makes it an ideal choice for developers wishing to integrate AI into their projects, or for users on a limited budget. ChatGPT uses a freemium model: the free version is limited in the number of requests and functionality, while full access to advanced models (such as GPT-4o) requires a subscription costing from $20 to $200 per month, depending on the plan. The second difference is the architectural approach. DeepSeek uses Mixture of Experts (MoE) technology, where the model consists of many specialized subnetworks. This reduces computational costs and speeds up query processing. ChatGPT relies on the classic GPT architecture, which requires more resources but provides deep contextual understanding and high versatility. Differences in Language Models The technical foundation of DeepSeek and ChatGPT significantly affects their performance. ChatGPT is built on the GPT architecture, which is a transformer with a huge number of parameters. For example, GPT-4 has over a trillion parameters, and the latest versions, such as o1, reach 1.8 trillion. Training such models requires colossal resources. DeepSeek uses a different architecture called MoE. In this system, the model consists of multiple "experts," each specializing in a specific type of task: one might handle programming, another text analysis, and a third mathematical calculations. According to DeepSeek AI, training version V3 cost only $5.58 million, which is tens of times cheaper than ChatGPT. Another difference lies in the training methods used. ChatGPT uses hundreds of terabytes of data and the RLHF (Reinforcement Learning from Human Feedback) technique, which helps the model better understand user requirements and avoid errors. DeepSeek trains on a smaller volume of data (for example, 14.8 trillion tokens for V3), supplementing them with synthetic datasets and optimization for specific tasks. This approach makes DeepSeek faster, but sometimes less accurate when executing complex user requests. Text Generation Quality The quality of generated text is one of the most important criteria when evaluating language models. ChatGPT is traditionally considered the leader in creating natural, coherent, and stylistically rich texts. It can write essays in the style of literary classics, movie scripts, scientific articles, or even humorous dialogues.In 2025, new versions of the language model, such as GPT-4o and o1, significantly reduced the likelihood of producing erroneous statements, substantially improved the logical structure of texts, and increased accuracy in answering complex questions. DeepSeek also demonstrates high-quality text creation. However, in complex creative tasks, DeepSeek falls short: its texts may be less elegant, and in long dialogues, it sometimes loses the thread of conversation or simplifies the style. Users note that DeepSeek handles short and medium requests better, while ChatGPT wins in multi-stage scenarios. Generation speed is another important factor to consider. Thanks to MoE, DeepSeek processes requests faster, which is noticeable in mass text generation or under limited resource conditions. ChatGPT, on the other hand, requires more time for analysis and processing, but the result justifies expectations in tasks where depth and quality are important. Coding and Programming Programming and use in the IT industry is one of the most in-demand and popular functions of language models, but here DeepSeek and ChatGPT offer different approaches. ChatGPT has established itself as a universal assistant for developers. It supports dozens of programming languages, can write code, explain algorithms, and find errors. In 2025, a deep reasoning mode was added, which allows the model to solve complex problems step by step. However, the free version of ChatGPT is limited in code volume and processing speed, forcing users to switch to paid plans. Despite the fact that DeepSeek was originally designed with the needs of programmers and IT specialists in mind, it often exceeds expectations in this area. Its open-source code and free access have made it a hit among open-source communities. DeepSeek R1, for example, showed outstanding results in code writing: it generates working solutions faster than ChatGPT and often adds useful details, such as line comments, game score tracking, or performance optimization. Tests in SwiftUI, Go, and Python showed that DeepSeek sometimes surpasses ChatGPT in code readability and speed of executing simple tasks, although in complex implementations (such as multithreaded applications) it may fall short. DeepSeek's special feature is DeepThink mode, which shows the step-by-step logic of solving a problem, which is ideal for learning and debugging. ChatGPT also offers similar functions, but only in paid versions, such as Advanced Reasoning. For simple tasks (writing a script or parsing data), DeepSeek wins thanks to speed and accessibility, but for large projects with long-term support, ChatGPT remains a more reliable choice. Language Support Multilingualism plays an important role for users around the world. ChatGPT supports over 50 languages, with a high level of accuracy and contextual understanding. It easily switches between languages within a single dialogue, maintaining natural communication. For example, a request in Spanish "Explain quantum entanglement in simple words" will be processed taking into account scientific terminology and adapted for a Spanish-speaking audience. ChatGPT also handles rare languages and dialects well, making it a universal tool for the global market. DeepSeek is also multilingual and supports over 20 languages, including English, Chinese, Arabic, Spanish, Portuguese, and others. However, its performance in languages other than English and Chinese is sometimes lower due to a smaller volume of training data. For example, in long dialogues in Spanish, DeepSeek may accidentally switch to English or generate a less accurate translation of complex phrases. This is especially noticeable in technical or legal texts where high terminological accuracy is required. Nevertheless, for basic tasks such as translating instructions or writing simple texts, DeepSeek copes quite well. Accessibility and Cost Accessibility and cost are also key factors when choosing between DeepSeek and ChatGPT. DeepSeek is distributed for free; however, API usage requires paid plans. The DeepSeek interface is accessible through a web browser on the official website and through a mobile application on iOS and Android. Access can also be obtained locally through the Ollama framework. Open-source code allows developers to customize the model to their needs, making it ideal for experiments, startups, and educational projects. By 2025, DeepSeek became a popular application in the App Store and Google Play, especially in Asian countries and Eastern Europe. While ChatGPT is distributed under a Freemium model, it only offers a free basic version based on the GPT-4o mini model. This model has limitations on the number of requests sent and also imposes restrictions on text volume. Full access to models like GPT-4o or o1 requires a subscription, the cost of which ranges from $20 per month to hundreds of dollars for plans with API and increased limits. DeepSeek wins in economy and ease of access, especially for users on a limited budget. ChatGPT offers more features for those willing to pay for premium functions, such as integration with external services, image generation, or working with large volumes of data. Comparison Table For clarity, we've compiled the main characteristics of the two AIs into a table for convenient comparison. Criterion DeepSeek ChatGPT Accessibility Free, open-source Distributed under Freemium model Cost $0 for chatbot use. API is paid. For working with models through API, tokens are used. Prices for input tokens start at $0.14 per million tokens (with caching). For output tokens, the price starts at $0.28 per million tokens. Can be used for free with a limited number of requests. API access is paid. Has higher token rates (depends on the model used). For the GPT-3.5 Turbo model, prices start at $0.50 per million (for input tokens) and $1.50 per million (for output tokens). For the GPT-4o model, prices start at $5.00 per million (for input tokens) and $15.00 per million (for output tokens). For the o1 model, prices start at $15.00 per million (for input tokens) and $60.00 per million (for output tokens). Text Quality Good, concise, practical High, natural, creative Coding Work Fast, efficient, readable code Accurate, universal, complex tasks Language Support Support for over 20 different languages, medium accuracy Support for over 50 languages, high accuracy Speed High Medium Best Suited For Simple tasks, including working with text, creating various small materials Complex projects, such as those related to creativity and solving business tasks. Also ideal for working with large data and creating programs in one of the supported programming languages What to Choose: DeepSeek or ChatGPT? The choice between the two chatbots DeepSeek and ChatGPT depends on user needs, budget, and, most importantly, the types of tasks that need to be solved. DeepSeek is ideally suited for users who need a fast, free, and efficient tool for everyday tasks. Such tasks include writing source code for a small project, analyzing text documents, searching for information on the internet, or generating simple texts such as letters or notes. Its advantages are especially noticeable for students, beginning developers, small businesses, and enthusiasts, where resource conservation and the absence of entry barriers are important. Another advantage of DeepSeek is the lack of fees for using the chatbot itself. Payment is only required for users who plan to use the API. ChatGPT, on the other hand, is better suited for complex tasks requiring high-quality text (including writing lengthy articles, scripts, business plans, etc.), deep analysis, or multi-stage reasoning. However, unlike DeepSeek, ChatGPT is distributed under a freemium model in which chatbot use is limited by the number of requests sent to the bot. The API is also paid and costs more than DeepSeek's API. Examples of DeepSeek and ChatGPT Usage: DeepSeek: Writing simple scripts for automating most types of tasks, searching for and generating technical material. ChatGPT: Generating complex texts, for example, for creating stories with full plots, solving complex algebraic problems. Also suitable for processing large data and working with analytical material. Conclusion Both AI models have advantages and disadvantages. Among DeepSeek's advantages are the lack of usage fees and speed of operation, making it a good solution for performing basic tasks. ChatGPT leads in text quality, versatility, and depth of analysis, which justifies its cost for professionals and complex projects. Both models continue to evolve, and their competition contributes to progress in the field of AI. DeepSeek is suitable for those looking for an accessible, fast tool, while ChatGPT is for those ready to tackle large, universal tasks.