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Best ChatGPT Prompts for Better Answers

Best ChatGPT Prompts for Better Answers
Hostman Team
Technical writer
Infrastructure

ChatGPT is a powerful tool for generating text, code, creating content strategies, and even working with images. It allows users to get accurate answers to complex questions across many fields of human activity. Developed by OpenAI based on the GPT architecture, this AI assistant can understand context, maintain long conversations, and adapt its communication style to the user’s needs.

To get the most useful answers, it’s important to learn how to properly formulate requests, or “prompts,” for ChatGPT. We’ll explain this in more detail below.

We’ll also share the best ChatGPT prompts to help you work more efficiently, show how to build well-structured requests, give examples with detailed ChatGPT responses, and explain how to write prompts for visual content through DALL·E.

All examples in this article use the free version of ChatGPT.

What Prompts Are and Why They Matter

A prompt is a text request that a user sends to ChatGPT or another AI model to get a desired answer. Simply put, it’s an instruction for the neural network explaining exactly what you want to receive.

Why is it important to create a good prompt for ChatGPT? Here are the main reasons:

  • Improved answer accuracy: The clearer and more detailed your prompt, the more relevant and useful the response will be.

  • Time savings: A well-structured request saves you from repeatedly rephrasing or clarifying your question.

  • Fewer mistakes: Clear instructions reduce the risk of incorrect or outdated information.

  • Optimized workflow: Good prompts let you automate complex tasks, from content creation to data analysis.

  • Structured results: Properly designed prompts help get answers in the needed format: lists, tables, step-by-step guides, etc.

  • Personalized responses: Adding context to your request makes ChatGPT’s answers more relevant to your needs (context includes role, tone, audience, format, etc.).

  • Better AI learning: Well-crafted prompts help the AI understand your preferences over time. That’s why it’s best to keep an ongoing conversation with ChatGPT within one chat thread when working on the same topic.

ChatGPT analyzes your request and provides the most relevant answer based on previously learned data. The clearer your prompt, the more accurate the AI’s response will be.

Examples of Weak and Strong Prompts

🔴 Weak Prompt

🟢 Strong Prompt

“Collect information about clouds.”

“Write a 1,000-word piece about the benefits of cloud technologies for small businesses. Include a comparison of Hostman with competitors.”

“Tell me about hosting.”

“Compare Hostman and AWS pricing for high-traffic websites. Highlight the pros and cons of each.”

“Write something about marketing.”

“Write 5 marketing strategies for promoting a SaaS product in 2025 via Facebook. Format: short description + 3 concrete actions for each.”

How to Create Your Own Prompts

Below are the main rules for writing effective prompts and common mistakes to avoid when working with ChatGPT.

Rules for Writing Prompts

Main principles for crafting perfect prompts:

  • The more specific your request, the more relevant the answer.
  • Always specify the desired output format (list, table, step-by-step guide).
  • For professional tasks, add context (AI’s role, difficulty level, target audience).
  • Use examples and analogies to match your expectations precisely.
  • Clearly state any constraints or special requirements.
  • Indicate timeframes for data relevance.
  • Ask for sources when you need verified information.
  • Balance detail with conciseness.

Another useful tip: save successful prompts somewhere convenient: a text editor, personal notes, or a dedicated ChatGPT chat named “Templates.” This helps in the future since many prompts can be reused simply by changing key parameters.

You can also use existing prompt libraries and adapt them to your needs, for example, prompthackers.co.

Common Mistakes

Here are typical mistakes when writing prompts, along with examples of how to improve them:

  • Too general requests
    • 🔴 “Tell me about AI.”
    • 🟢 “Explain how ChatGPT is used in the banking sector in 2025.”
  • Lack of structure
    • 🔴 “Give tips on time management.”
    • 🟢 “Create a checklist: ‘5 time management methods for remote workers.’ Format: Name → Essence → Example.”
  • Ignoring context
    • 🔴 “Write a text.”
    • 🟢 “Write a commercial proposal for Hostman (audience: CTOs of mid-sized companies). Tone: expert, but conversational.”
  • Vague clarifications
    • 🔴 “Make it shorter.”
    • 🟢 “Reduce to 300 words, keeping key data from the table.”
  • Overloading with details
    • 🔴 “Write an article about cloud technologies but exclude AWS, Microsoft Azure, IBM Cloud, Oracle Cloud, DigitalOcean, Linode, Vultr.”
    • 🟢 “Write an article ‘AWS Alternatives for Small Businesses’ with the main focus on Hostman”

Top 10 Universal Prompts for ChatGPT

This section includes ready-made prompt templates that will become reliable tools when working with ChatGPT. These prompts cover a wide range of tasks, from creative brainstorming to complex technical analysis.

We’ll look at 10 universal and practical prompts for the following categories:

  • Analysis and comparison
  • Idea generation
  • Psychology and self-development
  • Content strategy
  • Writing and editing
  • Programming
  • Image generation (DALL·E)
  • Learning and education
  • Business
  • Creativity

Each template is:

  • Well thought out: structured for high-quality answers
  • Universal: suitable for both beginners and professionals
  • Flexible: easily adaptable to specific needs

To use a template, choose the category and replace the placeholders in square brackets with your own values. For complex tasks, you can even combine several templates into one (an example will be shown at the end of the section).

All prompts are optimized for GPT-4 and newer versions to ensure highly relevant results even for advanced professional use.

1. For Analysis and Comparison

Purpose: Professional comparison of products, services, or technologies based on specific criteria with expert conclusions.

Ideal for: Selecting IT solutions, preparing reviews, making business decisions.

Template:

Compare [Object A] and [Object B] by the following criteria: [1–5 parameters].

Format: table with columns “Feature,” “Object A,” “Object B,” and “Recommendation.”

Specify the best option for [scenario].

Example:

Compare Hostman VPS and Linode VPS by: price per 1 vCPU, SLA, support speed, and control panel usability. Highlight the optimal choice for a startup with 50K visitors/month.

ChatGPT response:

60d4d17d 0bdc 4005 98fd Ea7e2d4b826d

Tips:

  • Set timeframes:
    • 🔴 “Compare hosting prices.”
    • 🟢 “Compare 2025 hosting prices including seasonal discounts.”
  • Ask for data sources:
    • 🔴 “Which platform is better?”
    • 🟢 “Compare using data from official websites and independent tests.”
  • Provide context:
    • 🔴 “Which is cheaper?”
    • 🟢 “Which is more cost-effective for a site with 50K visits/month: shared hosting or VPS?”
  • Ask for alternatives:
    • “If the budget is limited to $35/month, what are Hostman’s alternatives?”
  • Specify output format:
    • “Present the data in a table, then give a short verdict for beginners.”

2. For Idea Generation

Purpose: Structured brainstorming with clear logic.

Application: Finding concepts for startups, content marketing, product design, or creative projects.

Template:

As a [role], suggest [N] ideas for [task].

Structure: 1) Title → 2) Target Audience → 3) Benefit → 4) Example → 5) Risks.
Focus on: [requirements].

Example:

As an art director, suggest 5 ad campaign ideas for Hostman in the metaverse. Focus on interactivity and B2B audience.

ChatGPT response:

3be30829 Bab7 47db A006 C2dec753dbef

Tips:

  • Rank ideas by priority:
    • 🔴 “Give 5 post ideas.”
    • 🟢 “Suggest 5 social media post ideas about Hostman, sorted by feasibility/effectiveness. Consider: budget up to $100, B2B engagement.”
  • Define evaluation criteria:
    • “Exclude ideas requiring more than 3 days to execute.”
    • “Prioritize ideas with viral potential.”
  • Ask for examples:
    • “Show similar cases from the industry for the top 3 ideas.”
  • Limit scope:
    • “Only ideas that don’t require contractors.”
    • “Focus on formats: guides, case studies, interactives, polls.”
  • Request next steps:
    • “For the best idea, outline a 3-day action plan.”

3. For Psychology and Self-Development

Purpose: Scientifically grounded methods for solving personal and professional issues.

Especially useful for: coaching, stress self-help, and developing emotional intelligence.

Template:

As a [specialist], create a [duration]-long plan for solving [problem]. 

Include: 1) Theoretical foundation → 2) Step-by-step techniques → 3) Self-diagnosis tools → 4) Recommended resources. 

Adapted for: [audience].

Example:

As an HR expert with experience in IT, design an 8-week onboarding program for a new employee at a cloud company. Include:

  • Role introduction plan (days 1–30, broken down by week)
  • Methods for evaluating professional skills (checklists, test tasks)
  • Mentorship system (roles, meeting frequency, KPIs)
  • Recommendations for integrating into corporate culture (events, company traditions)

ChatGPT response:

1f0c35fe 17c7 4878 9191 Ee5682ac2a40

Tips:

  • Require scientific backing:
    • 🔴 “How to deal with anxiety?”
    • 🟢 “Using CBT (Beck) and the ABCDE model (Ellis), propose a 4-week anxiety management plan for IT specialists. Include research on the effectiveness of these approaches.”
  • Specify theories:
    • “Explain burnout stages using the Maslach model (emotional exhaustion → cynicism → reduced productivity).”
    • “For procrastination, use Piers Steel’s temporal motivation theory.”
  • Request context adaptation:
    • “Apply Gestalt therapy techniques to conflict situations in remote teams.”
    • “How can the GROW model be applied to IT career coaching?”
  • Ask for self-assessment tools:
    • “Add a checklist for tracking progress on a 1–10 scale.”
    • “What 3 questions can help identify the stage of stress according to Selye?”
  • Limit complexity:
    • “Explain terms in simple words, suitable for beginners.”
    • “Exclude medical recommendations.”

4. For Content Strategy

Purpose: Comprehensive publication planning with measurable KPIs.

Ideal for: blogging, SMM, email marketing, and sales funnels.

Template:

As a [position], create a [time period] strategy. 

Include: 1) Target personas → 2) Thematic clusters → 3) Calendar (format/KPIs) → 4) Tools.

Example: 

As a head of content marketing, develop a quarterly blog strategy for Hostman with KPIs focused on trial conversions. Emphasize guides about migrating from competitors.

ChatGPT response:

3e0755c9 A3ff 4668 82e1 083c3e675aa7

Tips:

  • Tie it to business goals:
    • 🔴 “Need a content plan.”
    • 🟢 “Develop a quarterly strategy for the Hostman blog with KPI: +15% increase in trial conversions. 70% educational content, 30% case studies.”
  • Specify success metrics:
    • “For Facebook posts, define target metrics: CTR >3%, engagement >5%.”
    • “Estimate potential reach for each topic.”
  • Request cross-channel integration:
    • “How can a guide be turned into a Facebook post series and email campaign?”
    • “Propose a cross-promotion scheme between YouTube and the blog.”
  • Ask for competitor analysis:
    • “Add analysis of 2 successful strategies from competitors in the cloud segment.”
    • “Which topics bring the highest engagement for competitors?”
  • Limit resources:
    • “For a 2-person team: 1 long-read per week + 3 social channels.”
    • “Without hiring copywriters.”

5. For Working with Texts

Purpose: Creating and optimizing commercial or informational materials. Fields: copywriting, SEO, technical documentation, scripts.

Template:

As a [role], write a [type of text] for [target audience].

Parameters: length → style → required elements → restrictions → SEO.

Structure: [sections].

Example:

As a technical writer, create a guide titled “Setting Up WordPress on Hostman” (1,500 words). Avoid jargon, include GIF instructions.

ChatGPT response:

B77ee57b 07d4 47ef Bd34 8ba5d6b00491

Tips:

  • Clearly define the text’s purpose:
    • 🔴 “Write a text about clouds.”
    • 🟢 “Write a commercial proposal for Hostman aimed at small businesses. Goal: conversion into demo requests.”
  • Set style and tone:
    • “Tone: friendly yet professional, as if explaining to a colleague.”
    • “Avoid bureaucratic phrases; write naturally.”
  • Add SEO parameters if needed:
    • “Include keywords: ‘cloud hosting,’ ‘VPS for business,’ ‘reliable hosting.’ Keep keyword density natural.”
    • “Add LSI words: ‘scalability,’ ‘data security,’ ‘uptime.’”
  • Request examples and comparisons:
    • “Provide 3 strong headline examples for this kind of article.”
    • “Compare with competitor texts: what can be improved?”
  • Limit length and complexity:
    • “Max 1,000 words, divided into H2-H3 subheadings.”
    • “Explain terms like ‘CDN’ in parentheses in simple words.”

6. For Programmers

Purpose: Code generation and analysis with full documentation.

Main uses: writing scripts, debugging, creating APIs, automating DevOps processes.

Template:

As a [language] developer of [X] level, write code for [task].

Input → expected output → constraints → requirements.

Format: algorithm → code → tests.

Example:

As a senior Python developer, create a server monitoring script for Hostman with API integration and Telegram notifications. Requirements: async, logging.

ChatGPT response:

F847719e A8b0 4f88 Bbd7 6ac83b351567

Tips:

  • Specify exact versions:
    • 🔴 “Write a backup script.”
    • 🟢 “Write a Python (3.10+) script for daily MySQL (8.0) backups to Hostman S3. Requirements: async, file logging, Telegram error alerts.”
  • Request explanations:
    • “Add comments every 5 lines to clarify complex code sections.”
    • “Explain why you chose this algorithm (e.g., QuickSort vs. MergeSort).”
  • Require tests:
    • “Add 3 unit tests with edge cases.”
    • “How to test this API in Postman?”
  • Ask for alternatives:
    • “Show alternative solutions in Go and Rust. Compare performance.”
  • Set constraints:
    • “No external libraries.”
    • “Execution time ≤100ms for 10K records.”

7. For Image Creation (DALL·E)

Purpose: Precise technical specifications for neural image generation (DALL·E).

Applications: ad banners, article illustrations, concept art, presentations.

Template:

As an art director, create a prompt:

1) Object → 2) Style → 3) Composition → 4) Color palette → 5) Lighting → 6) Restrictions.

Goal: [usage].

Example:

Create a prompt for a “Hostman Enterprise” banner: a cyberpunk-style server, palette #0A1640/#00C1FF, HUD elements, no people.

ChatGPT response:

C2704583 B97f 40a8 8937 Dc66c4f5315c

Image generated by ChatGPT:

Chat GPT Image

Tips:

  • Be extremely specific:
    • 🔴 “Draw a cloud server.”
    • 🟢 “Generate a 3D render of a Hostman server in blue-white tones. Style: cyberpunk with neon accents. Background: network map with nodes. Aspect ratio 16:9, no people.”
  • Reference known styles:
    • “In the style of the interfaces from the Foundation series.”
    • “Like Wired magazine covers from the 2020s.”
  • Control composition:
    • “Main object centered, occupying 70% of the frame.”
    • “Blurred background with depth-of-field effect.”
  • Request variations:
    • “Show 3 versions: minimalism, retro-futurism, and photorealism.”
    • “Change only the palette to dark/light mode.”
  • Technical constraints:
    • “No text in the image.”
    • “Resolution: 1024×1024, format: PNG.”

8. For Learning and Education

Purpose: Designing educational programs using modern methodologies.

Application: course creation, training materials, workshops, interactive modules.

Template:

As a professor of [subject], design a [number]-hour module.

Include: goals → plan (theory/practice) → adaptations → glossary.

Constraints: [parameters].

Example:

Develop an 8-hour course “Cloud Fundamentals” for university students: lectures in Prezi, labs on Hostman, quizzes in Kahoot.

ChatGPT response:

Ef81c94b Eabd 418d Ba5b E602ff23d5c4

Tips:

  • Base on teaching models:
    • 🔴 “Create a Python course.”
    • 🟢 “Using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation), create a 4-week course ‘Python for Data Analysis.’ Goal: teach students to visualize data using Matplotlib.”
  • Define difficulty level:
    • “For junior DevOps: basics of Kubernetes.”
    • “For senior developers: algorithm optimization in C++.”
  • Add interactive elements:
    • “Include 3 simulated real-world cloud development cases.”
    • “Propose a gamification format for a cybersecurity module.”
  • Require practical tasks:
    • “Design a lab exercise: deploying a web app on Hostman.”
    • “Create a test assignment with automatic checking via GitHub Actions.”
  • Consider technical limitations:
    • “Course must run on low-end PCs (no Docker).”
    • “Use only free tools (VS Code, Colab).”

9. For Business

Purpose: Strategic market and process analysis.

Applications: business planning, SWOT analysis, competitor research, financial modeling.

Template:

As a consultant from [company], conduct an analysis of [object] using the following framework: 

1) Market Size → 2) PESTEL → 3) Benchmarking → 4) SWOT → 5) Forecasts.

Data sources: [list of references].

Example:

Analyze the European cloud gaming market: 2024 market size, PESTEL factors, comparison of NVIDIA GeForce Now / Shadow PC / Boosteroid, and projections through 2026.

ChatGPT response:

9ac60c30 9f26 4686 97a8 A1850e6db603

Tips:

  • Be specific with goals:
    • 🔴 “How to increase profits?”
    • 🟢 “Develop 3 revenue growth strategies to increase a SaaS startup’s revenue by 30% within 6 months. Focus: upselling existing clients and reducing churn rate. Use the AARRR framework.”
  • Ask for supporting data:
    • “Analyze the European cloud services market (size, trends, competitors). Use sources such as Statista, Gartner, and official company reports.”
    • “Calculate CAC for our current ad campaign.”
  • Request alternative approaches:
    • “What are the best options for entering the EU market: partnerships vs. independent launch?”
    • “Compare investment risks for expanding VPS services versus cloud storage solutions.”
  • Link to business processes:
    • “How can the new product be integrated into our existing SaaS ecosystem?”
  • Consider resource limitations:
    • “Budget: up to €50,000, team of 5 people.”
    • “Propose solutions that don’t require hiring additional staff.”

10. For Creativity

Purpose: To generate compelling stories and concepts.

Ideal for:

  • Writers: for books and short stories;
  • Screenwriters: for films and series;
  • Game developers: for characters and worldbuilding;
  • Musicians: for album or concept creation.

Template:

As a [profession], create a [type of work] in the [genre] style.

Parameters: Characters → Setting → Conflict → Style.

Format: Logline → Synopsis → Scene breakdown.

Example:

As a Black Mirror-style screenwriter, develop a concept for an episode about AI in 2045, exploring the theme “Privacy vs Convenience.”

ChatGPT response:

4a5d23be F43e 45cc 90e4 07abb229ed14

Tips:

  • Be specific about genre and audience:
    • 🔴 “Write a story about a scientist.”
    • 🟢 “Write the first chapter of a science fiction story about a bioengineer who discovers how to edit DNA using quantum computers. Style: mix of Black Mirror and The Martian. Audience: hard sci-fi fans (ages 25–45), with emphasis on scientific realism.”
  • Request structure:
    • “Outline the plot using Joseph Campbell’s ‘Hero’s Journey’ model.”
    • “Create a dialogue example with subtext (in the style of Aaron Sorkin).”
  • Ask for visualization:
    • “Describe a key cinematic shot for a poster in the style of Blade Runner.”
    • “Which color palette best conveys the atmosphere?”
  • Avoid clichés:
    • “Exclude tropes like ‘the chosen one’ or ‘evil AI.’”
    • “Suggest three unexpected plot twists.”
  • Consider technical constraints:
    • “Script for a 10-minute short film (maximum 5 locations).”
    • “Concept for a mobile game with simple gameplay.”

Combined Prompt Example

The prompt templates presented above cover most professional user tasks. For maximum efficiency, you can combine them, for example: analysis (section 1) + text generation (section 5) + visualization (section 7).

Example prompt

Act as both an IT analyst and a digital marketer. I need a comprehensive comparison of cloud hosting platforms (AWS, Google Cloud, and Hostman) with materials ready for publication. Perform the following tasks sequentially:

1. Conduct a detailed analysis:

  • Compare by: cost per vCPU, SSD size, network bandwidth, SLA uptime.
  • Present results in a table with columns: “Feature,” “AWS,” “Google Cloud,” “Hostman.”
  • Conclude with a recommendation for a startup with a $50/month budget.

2. Write an SEO article based on the analysis:

  • Title: “AWS vs Google Cloud vs Hostman: An Objective Comparison for 2025.”
  • Length: 2,000 words.
  • Structure:
    • Introduction (importance of choosing the right provider);
    • Methodology;
    • In-depth review of each provider;
    • Summary table (from step 1);
    • Recommendations for different use cases.
  • Tone: Expert but accessible;
  • Keywords: “cloud hosting,” “VPS comparison,” “Hostman review.”

3. Create visualization prompts (for DALL·E or Midjourney):

  • Style: Corporate infographic (blue and white color palette).
  • Elements:
    • 3D servers with provider logos;
    • Comparative performance and pricing charts;
    • “Price/Performance” scale;
    • Minimalist background with digital accents.
  • Formats: Article cover, comparative infographic, architecture diagram.

4. Additional tasks:

  • Suggest 3 social media posts based on the article.
  • Format: “Did you know that…” + key takeaway + infographic.
  • Platforms: LinkedIn, Reddit.
  • Ensure all data is consistent across text and visuals. Numbers in the text must match tables and charts. Use professional terminology, but explain complex terms for beginners.

ChatGPT response:

1ca41e8b 6bb4 4710 8ac3 266bf08623cd

This prompt structure provides:

  • A unified request instead of multiple separate ones;
  • Logical flow: analysis → writing → visuals → promotion;
  • Consistent data across all materials;
  • Publication-ready results.

For even higher precision, you can add: “Before starting, ask 3 clarifying questions to better define the task.” This approach helps the AI better understand the project and deliver higher-quality results.

Key Takeaways

In this article, we explored what prompts are and how to craft them effectively, showcasing 10 universal examples across different categories.

A prompt is a text instruction you send to ChatGPT to get a desired response. The clearer and more detailed the prompt, the more accurate and useful the result.

Core principles of effective prompting:

  • Clarity and detail (including timeframes, parameters, and constraints);
  • Specify the response format (table, list, step-by-step guide);
  • Add context (AI role, complexity level, target audience);
  • Include examples and analogies for clarity;
  • Note technical requirements (length, tone, restricted elements).

Common mistakes:

  • Overly vague prompts (“Write something”);
  • No structure or logic;
  • Ignoring context (missing role or audience);
  • Overcomplicating with conflicting details;
  • Poor clarification (missing data or specific names).

Improvement tips:

  • Start broad, then refine details step by step;
  • Save successful prompts as templates;
  • Request data sources for analytical tasks;
  • Use iterations: “Add to the previous answer…”

Additional recommendations:

  • For creative work, include stylistic references;
  • For technical tasks, specify software versions or languages;
  • For business analysis, ask for alternative scenarios;
  • Always verify critical data. ChatGPT is a tool, not a substitute for expertise.

Save the templates from this guide as a quick-reference list and adapt them over time to fit your workflow. By mastering the art of crafting effective prompts, you’ll unlock ChatGPT’s full potential, transforming it into a personal assistant for work, creativity, and learning. Experiment with phrasing, analyze results, and refine your prompts: that’s how you’ll make AI a truly powerful tool in your toolkit.

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IT Cost Optimization: Reducing Infrastructure Expenses Without Compromising Performance

Infrastructure costs grow imperceptibly. Typically, teams start by renting a couple of virtual machines, a database, and storage. In this setup, the system works smoothly, and the team focuses on the product. But as the project grows, the infrastructure "sprawls": one provider for servers, another for databases, a third for file storage. Test environments, temporary instances, and "just in case" disks appear. As a result, the budget increases not because of new features, but because of numerous disparate solutions. The more complex the architecture becomes, the harder it is to control costs. The team spends time not on the product but on maintaining infrastructure, trying to maintain a balance between performance and budget. In this article, we'll explore how to approach cloud infrastructure rationally: what to optimize first, what we often overpay for, how to avoid fragmentation, and how to make the team’s life easier by consolidating key services on a single platform. Infrastructure Audit: What to Check First Cloud cost optimization doesn't start with cuts, but with transparency. Companies often try to save money without understanding where exactly the money is going. Therefore, the first step is to conduct an audit of the current infrastructure and identify inefficient or unused resources. To conduct a good audit, companies usually invite cloud architects or DevOps engineers. They typically look for problems according to the following plan. 1. Server Load The most common cause of unnecessary expenses is virtual machines launched "with reserve." If CPU and RAM consistently work at 10-20%, it means the configuration is excessive. This is especially noticeable in projects that scaled in a hurry and where resources were expanded just in case. It's useful to evaluate average and peak CPU load, the amount of RAM used, disk subsystem metrics like IOPS and latency, as well as network traffic dynamics—this provides a holistic understanding of how efficiently servers are working. In this case, even a small configuration adjustment can reduce costs without loss of stability. 2. Idle Resources Over time, infrastructure accumulates test servers, temporary databases, forgotten disks, and old snapshots. This is the invisible but constant expense item. Pay attention to virtual machines without traffic, disconnected disks, outdated backups, and test instances that were once launched temporarily but remained in the infrastructure. These are the elements that should be optimized in the first hours of the audit. 3. Databases Databases are one of the most expensive infrastructure components. Here, it's important to look not only at the number of resources, but also at the actual load. Often large clusters are deployed simply because "it's safer that way." It's useful to check query frequency, number of active connections, disk load, and the actual volume of storage used—these indicators will help quickly determine whether the current cluster size is justified. Also make sure databases aren't duplicated for different environments. 4. Logs and Storage Logs and media files can take up more and more space if they're not moved to object storage. Storing all this on server disks is unjustifiably expensive. Evaluate the volume of logs, their storage and rotation policy, media archive size, as well as backup location and frequency—this makes it easier to understand whether data is accumulating where it shouldn't be. Optimizing Compute Resources After the audit, it becomes clear which servers the project really needs and which work inefficiently. The next step is to select configurations so that they correspond to the actual load and grow with the product, rather than exceeding it several times over. The main principle here is that resources should not be "more than needed," but "exactly as much as needed now." If the load increases, in the cloud it's easier to add resources to an existing server or add a new instance than to constantly maintain a reserve for peak loads. This approach allows you to reduce costs without risk to stability. It's important to correctly choose machine types for different tasks. For example, standard VMs are most often suitable for web applications, GPU-optimized servers for analytical or ML workloads, and separate disk configurations for services with high read and write intensity. Another way to optimize cloud computing costs is not to scale up one large server, but to distribute the load across several smaller VMs using a load balancer. It receives incoming traffic and directs it to available instances so that no single machine becomes a "bottleneck." This approach scales smoothly: if the project grows, you simply add a new VM to the pool, and the balancer immediately takes it into account when distributing requests. In Hostman, the load balancer is built into the ecosystem and easily connects to any set of servers. When the load increases, the team spins up new instances; when it decreases, they shut down excess ones, thus adapting infrastructure to real conditions, not theoretical peaks. Ultimately, compute resource optimization is about flexibility. Resources scale with the product, and the budget is spent on what actually brings value, not on excessive configurations. Optimizing Database Operations After the audit, it becomes clear which database instances are actually used. The next step is to build a data storage architecture that is not only reliable but also economically justified. In working with databases, this largely depends on the correct choice of technology and operating model. Choosing a Database Engine Different types of loads require different approaches. Transactional systems—online stores, CRM, payment services—work best with classic OLTP (Online Transaction Processing) solutions like PostgreSQL or MySQL, where write speed and operation predictability are important. If we're talking about documents, user content, or flexible data schemas, MongoDB is more convenient. And analytical tasks—reports, metrics, aggregates over millions of rows—are better suited to OLAP (Online Analytical Processing) solutions like ClickHouse. The right database choice immediately reduces costs: the project doesn't overpay for resources that don't fit the load type and doesn't waste time on complex workarounds. Why DBaaS Saves Budget Even a perfectly selected database becomes expensive if you deploy and maintain it yourself. Administration, updates, replication, backup, fault tolerance—all this takes a lot of time and requires competencies that are difficult and expensive for startups or small teams to maintain. The DBaaS format removes most of these tasks. The platform provides SLA, monitors cluster fault tolerance, updates versions, manages backups, and provides clear scaling tools.  In addition, there are no hidden costs: the database works within a stable platform, and the provider takes on all infrastructure tasks. Horizontal Scaling Without Overpaying When the load grows, it doesn't always make sense to strengthen the main node. In managed databases, it's easier and more reliable to scale the system by distributing different types of load across separate services: leave the transactional part in the OLTP database and move analytical calculations to a separate OLAP cluster like ClickHouse. This approach reduces pressure on the main node and saves the application from slowdowns due to heavy queries. Within DBaaS, this is usually the most predictable and accessible scaling scenario—without manual sharding and complex replica configuration. This approach reduces pressure on the master node and allows avoiding a sharp budget jump. The system scales gradually: as the load grows, replicas are added rather than expensive "monolithic" server configurations. How to Save on Databases in Hostman Managed databases combine the convenience of DBaaS and configuration flexibility. Clusters are created in minutes, and configuration is selected based on project needs—without excessive reserve. When the load grows, you can increase the configuration. Scaling happens quickly and without complex migrations, and payment is only for actual resource consumption. This approach helps keep the budget under control and not overpay for capacity that is only partially used. File and Log Storage: Transition to Object Storage When a project grows, file volume inevitably increases: media, exports, backups, temporary data, system artifacts. In the early stages, they're often stored directly on the server disk—this seems like the simplest and fastest solution. But as the application grows, this approach begins to noticeably increase costs and complicate infrastructure operations. Why It's Unprofitable to Store Files on Server Disks The main disadvantage is tying data to a specific machine. If a server needs to be replaced, expanded, or moved, files have to be copied manually. Scaling also becomes a problem: the more data stored, the faster disk costs grow, which are always more expensive than cloud storage. Another complexity is fault tolerance. If something happens to the server, files are at risk. To avoid this, you have to configure disk duplication or external backups—and that's additional costs and time. How Object Storage Reduces Costs S3 object storage removes most of these limitations. Data is stored not on a specific server, but in a distributed system where each file becomes a separate object with a unique key. Such storage is cheaper, more reliable, and doesn't depend on specific applications or VMs. The economic effect is immediately noticeable: Volume can be increased without migrations and downtime Files are automatically distributed across nodes, ensuring fault tolerance No need to pay for disk resources of individual servers Easier to plan the budget—storage cost is predictable and doesn't depend on machine configuration Where to Use S3 in Applications S3 is convenient to use where data should be accessible from multiple parts of the system or where scaling is important: Images and user content Web application static files Archives and exported data Backups CI/CD artifacts Machine logs that then undergo processing This separation reduces the load on application servers and gives infrastructure more flexibility. S3 Features in Hostman In Hostman, object storage integrates with the rest of the platform infrastructure and works on the S3-compatible API model, which simplifies the transition from other solutions. Lifecycle policies are also supported: you can automatically delete old objects, move them to cheaper storage classes, or limit the lifespan of temporary files. This helps optimize costs without manual intervention. Integration with virtual servers and Kubernetes services makes S3 a convenient architecture element: the application can scale freely, and data remains centralized and reliably stored. Containerization: How to Ensure Stability and Reduce Operating Costs Containerization has become a basic tool for projects where it's important to quickly deploy environments, predictably update services, and flexibly work with load. In addition to development convenience, it also provides tangible savings: a properly configured container architecture allows using infrastructure much more efficiently than the classic "one server—one application" model. Why Containers Are Cheaper to Operate Unlike virtual machines, containers start faster, take up fewer resources, and allow placing multiple services on the same node without risks to stability. The team stops maintaining multiple separate servers "for every little thing"—all services are packaged in containers and distributed across nodes so that resources are used as densely as possible. This reduces infrastructure costs and decreases the number of idle machines. Savings Through Kubernetes Kubernetes has a particularly noticeable impact on the budget. It automatically adjusts the number of containers to the load: if traffic has grown, it spins up new instances; if it has fallen, it stops excess ones. The project pays only for actual resource usage, not for reserves maintained for peak values. In addition, Kubernetes simplifies fault tolerance. Applications are distributed among different servers, and the failure of one node doesn't lead to downtime. This reduces costs associated with failures and decreases the need for expensive backup servers. Less Manual Work, Lower Costs In container architecture, updates, rollbacks, test environment deployments, and scaling turn into automated processes. The team spends less time on administration, which means less money on operational tasks. Kubernetes also allows running environments for the duration of tasks. For example, spinning up environments for CI/CD, load testing, or preview—and automatically deleting them after work is completed. Kubernetes in Hostman Kubernetes is provided as a fully managed service (KaaS). The platform handles updating master nodes, network configuration, fault tolerance, and the overall state of the cluster. The team works only with nodes and containers, avoiding routine DevOps tasks. Nodes can be added or removed literally in minutes. This is convenient when the load fluctuates: infrastructure quickly expands or contracts, and the budget remains predictable. Integration with object storage, network services, and managed databases makes Kubernetes part of a unified architecture where each element scales independently and without unnecessary costs. Network and Security Without Unnecessary Costs When designing network architecture, it's easy to make mistakes that not only reduce system resilience but also increase the budget. How Improper Network Organization Increases Budget Even small flaws in network configuration can cause a noticeable financial drain. For example, if an internal service is accessible via a public IP, traffic starts passing through an external channel, which increases latency and data transfer costs. A similar situation arises when the database and backend are on different servers but not connected by a private network. Some cloud providers might meter such traffic, which can become an unexpected expense. In Hostman, data transfers are free, but a private network still offers advantages: higher transfer speeds, reduced security risks, and the ability to avoid unnecessary public IPs. Without private networks, security also becomes more complicated. To restrict access, you have to build additional firewall rules and load balancers, and each such solution costs money, be it in the form of resources or human hours. Savings Start With Network Structure In a rational network organization, each component operates in its proper zone and routes traffic to where it's safe and free. Private networks allow isolating sensitive services (databases, internal APIs, queues) and completely removing them from public space. This reduces the attack surface, decreases the number of required firewall rules, and eliminates costs for unnecessary traffic. Floating IPs help save on fault tolerance: instead of reserving a powerful server, it's enough to prepare for quickly transferring the address to another VM. Switching happens almost instantly, and the service remains available for users. This scheme allows ensuring resilience without the expense of duplicate configurations. Reducing Costs Through Fault Tolerance Improperly configured networks often cause downtime, and downtime means direct losses. Proper load distribution, load balancers, and private routes allow avoiding a situation where one server becomes a bottleneck and takes the application out of service. A separate point is DDoS protection. This is not only about security but also about economics: during an attack, the service can become unavailable, and unavailability almost always means losing customers, orders, and reputation. DDoS protection cuts off malicious traffic before it enters the infrastructure, reducing server load and preventing downtime that easily turns into tangible losses. Automation: How to Reduce Operating Costs Even perfectly selected infrastructure can remain expensive if managed manually. Creating test environments, updating configurations, scaling, backup rotation, server management—all this turns into a long chain of manual actions that take hours of work and lead to errors. Automation reduces maintenance costs through repeatability, predictability, and the elimination of human error. Why Manual Infrastructure Is More Expensive Manual operations always mean: Risk of forgetting to delete a temporary environment Inconsistent settings between servers Unpredictable downtime due to errors Developer time spent on routine instead of the product These are direct and indirect costs that easily hide in the process but noticeably increase the final budget. Which Processes Are Most Profitable to Automate From a savings perspective, three areas provide the most benefit: Environment Deployment. Quick creation of environments for development, testing, preview, and load tests. The environment is spun up automatically, works for the required time, and is deleted when no longer needed. Infrastructure Scaling. Load peaks can be handled automatically: spin up additional resources based on metrics, then shut them down. This way, you pay only for the peak, not for maintaining a constant reserve. Unified Configuration Description. When the environment is described as code, it can be reproduced at any stage, from development to production. This reduces the number of errors and eliminates "manual magic." Infrastructure as Code: An Economic Tool IaC solves the main problem of the manual approach: unpredictability. Configuration is stored in Git, changes are tracked, environments are created identically. The team spends less time on maintenance, plans the budget more easily, and responds to load changes faster. As a result, operating costs are reduced, and infrastructure becomes more transparent and manageable. Hostman Tools for Automation Hostman provides a set of tools that help build automation around the entire infrastructure: Public API. Automatic management of servers, networks, databases, and storage. Terraform provider, for a complete IaC approach: the entire infrastructure is described as code. cloud-init. Allows deploying servers immediately with preconfigured settings, users, and packages. Together, they create infrastructure that can be spun up, modified, and scaled automatically, without unnecessary actions and costs. This is especially important for teams that need to move quickly but without constant overspending. Conclusion Optimizing infrastructure costs is about building a mature approach to working with resources. At each stage, it seems that costs are quite justified, but in total they turn into a tangible burden on the budget—especially if the team scales quickly. To keep spending under control, it's important not to cut resources blindly, but to understand how infrastructure works and which elements the product really needs here and now. An audit helps find inefficient parts of the system. Correct work with computing power and databases reduces costs without loss of performance. Transition to object storage makes the architecture more flexible and reliable. Containerization and Kubernetes remove dependence on manual actions. Automation frees the team from routine and prevents errors that cost money. Proper network organization increases resilience—and simultaneously reduces costs. For many projects, it makes sense to rent a VPS instead of investing in dedicated hardware. VPS hosting for rent gives you predictable performance, root access, and the freedom to scale resources as your workload grows—without overpaying upfront. Rational architecture is not about saving for saving's sake. It's about resilience, speed, and the project's ability to grow without unnecessary technical and financial barriers. And the earlier the team transitions from chaotic resource accumulation to a thoughtful management model, the easier it will be to scale the product and budget together.
09 December 2025 · 16 min to read
Infrastructure

Apache Kafka and Real-Time Data Stream Processing

Apache Kafka is a high-performance server-based message broker capable of processing enormous volumes of events, measured in millions per second. Kafka's distinctive features include exceptional fault tolerance, the ability to store data for extended periods, and ease of infrastructure expansion through the simple addition of new nodes. The project's development began within LinkedIn, and in 2011, it was transferred to the Apache Software Foundation. Today, Kafka is widely used by leading global companies to build scalable, reliable data transmission infrastructure and has become the de facto industry standard for stream processing. Kafka solves a key problem: ensuring stable transmission and processing of streaming data between services in real time. As a distributed broker, it operates on a cluster of servers that simultaneously receive, store, and process messages. This architecture allows Kafka to achieve high throughput, maintain operability during failures, and ensure minimal latency even with many connected data sources. It also supports data replication and load distribution across partitions, making the system extremely resilient and scalable. Kafka is written in Scala and Java but supports clients in numerous languages, including Python, Go, C#, JavaScript, and others, allowing integration into virtually any modern infrastructure and use in projects of varying complexity and focus. How the Technology Works To work effectively with Kafka, you first need to understand its structure and core concepts. The system's main logic relies on the following components: Messages: Information enters Kafka as individual events, each representing a message. Topics: All messages are grouped by topics. A topic is a logical category or queue that unites data by a specific characteristic. Producers: These are programs or services that send messages to a specific topic. Producers are responsible for generating and transmitting data into the Kafka system. Consumers: Components that connect to a specific topic and extract published messages. To improve efficiency, consumers are often organized into consumer groups, thereby distributing the load among different instances and allowing better management of parallel processing of large data volumes. This division significantly improves overall system performance and reliability. Partitions: Any topic can be divided into partitions, enabling horizontal system scaling and increased performance. Brokers: Servers united in a Kafka cluster perform functions of storing, processing, and managing messages. The component interaction process looks as follows: The producer sends a message to a specified topic. The message is added to the end of one of the topic's partitions and receives its sequential number (offset). A consumer belonging to a specific group subscribes to the topic and reads messages from partitions assigned to it, starting from the required offset. Each consumer independently manages its offset, allowing messages to be re-read when necessary. Thus, Kafka acts as a powerful message delivery mechanism, ensuring high throughput, reliability, and fault tolerance. Since Kafka stores data as a distributed log, messages remain available for re-reading, unlike many queue-oriented systems. Key Principles Append-only log: messages are not modified/deleted (by default), they are simply added. This simplifies storage and replay. Partition division for speed: one topic is split into parts, and Kafka can process them in parallel. Thanks to this, it scales easily. Guaranteed order within partition: consumers read messages in the order they were written to the partition. However, there is no complete global ordering across the entire topic if there are multiple partitions. Messages can be re-read: a consumer can "rewind" at any time and re-read needed data if it's still stored in Kafka. Stable cluster operation: Kafka functions as a collection of servers capable of automatically redirecting load to backup nodes in case of broker failure. Why Major Companies Choose Apache Kafka There are several key reasons why large organizations choose Kafka: Scalability Kafka easily handles large data streams without losing performance. Thanks to the distributed architecture and message replication support, the system can be expanded simply by adding new brokers to the cluster. High Performance The system can process millions of messages per second even under high load. This level of performance is achieved through asynchronous data sending by producers and efficient reading mechanisms by consumers. Reliability and Resilience Message replication among multiple brokers ensures data safety even when part of the infrastructure fails. Messages are stored sequentially on disk for extended periods, minimizing the risk of their loss. Log Model and Data Replay Capability Unlike standard message queues where data disappears after reading, Kafka stores messages for the required period and allows their repeated reading. Ecosystem Support and Maturity Kafka has a broad ecosystem: it supports connectors (Kafka Connect), stream processing (Kafka Streams), and integrations with analytical and Big Data systems. Open Source Kafka is distributed under the free Apache license. This provides numerous advantages: a huge amount of official and unofficial documentation, tutorials, and reviews; a large number of third-party extensions and patches improving basic functionality; and the ability to flexibly adapt the system to specific project needs. Why Use Apache Kafka? Kafka is used where real-time data processing is necessary. The platform enables development of resilient and easily scalable architectures that efficiently process large volumes of information and maintain stable operation even under significant loads. Stream Data Processing When an application produces a large volume of messages in real time, Kafka ensures optimal management of such streams. The platform guarantees strict message delivery sequence and the ability to reprocess them, which is a key factor for implementing complex business processes. System Integration For connecting multiple heterogeneous services and applications, Kafka serves as a universal intermediary, allowing data transmission between them. This simplifies building microservice architecture, where each component can independently work with event streams while remaining synchronized with others. Data Collection and Transmission for Monitoring Kafka enables centralized collection of logs, metrics, and events from various sources, which are then analyzed by monitoring and visualization tools. This facilitates problem detection, system state control, and real-time reporting. Real-Time Data Processing Through integration with stream analytics systems (such as Spark, Flink, Kafka Streams), Kafka enables creation of solutions for operational analysis and rapid response to incoming data. This allows for timely informed decision-making, formation of interactive monitoring dashboards, and instant response to emerging events, which is critically important for applications in finance, marketing, and Internet of Things (IoT). Real-Time Data Analysis Through interaction with stream analytics tools (for example, Spark, Flink, Kafka Streams), Kafka becomes the foundation for developing solutions ensuring fast processing and analysis of incoming data. This functionality enables timely important management decisions, visualization of indicators in convenient interactive dashboards, and instant response to changing situations, which is extremely relevant for financial sector companies, marketers, and IoT solution developers. Use Case Examples Here are several possible application scenarios: Web platforms: any user action (view, click, like) is sent to Kafka, and then these events are processed by analytics, recommendation system, or notification service. Fintech: a transaction creates a "payment completed" event, which the anti-fraud service immediately receives. If suspicious, it can initiate a block and pass data further. IoT devices: thousands of sensors send readings (temperature, humidity) to Kafka, where they are processed by streaming algorithms (for example, for anomaly detection), and then notifications are sent to operators. Microservices: services exchange events ("order created," "item packed," etc.) through Kafka without calling each other directly. Log aggregation: multiple services send logs to Kafka, from where analytics systems, SIEM, or centralized processing systems retrieve them. Logistics: tracking delivery statuses or real-time route distribution. Advertising: collection and analysis of user events for personalization and marketing analytics. These examples demonstrate Kafka's flexibility and its application in various areas. When Kafka Is Not Suitable It's important to understand the limitations and situations when Kafka is not the optimal choice. Several points: If the data volume is small (for example, several thousand messages per day) and the system is simple, implementing Kafka may be excessive. For low traffic, simple queues like RabbitMQ are better. If you need to make complex queries with table joins, aggregations, or store data for very long periods with arbitrary access, it's better to use a regular database. If full ACID transactions are important (for example, for banking operations with guaranteed integrity and relationships between tables), Kafka doesn't replace a regular database. If data hardly changes and doesn't need to be quickly transmitted between systems, Kafka will be excessive. Simple storage in a database or file may be sufficient. Kafka's Differences from Traditional Databases Traditional databases (SQL and NoSQL) are oriented toward storing structured information and performing fast retrieval operations. Their architecture is optimized for reliable data storage and efficient extraction of specific records on demand. In turn, Kafka is designed to solve different tasks: Working with streaming data: Kafka focuses on managing continuous data streams, while traditional database management systems are designed primarily for processing static information arrays. Parallelism and scaling: Kafka scales horizontally through partitions and brokers, and is designed for very large stream data volumes. Databases (especially relational) often scale vertically or with horizontal scaling limitations. Ordering and stream: Kafka guarantees order within a partition and allows subscribers to read from different positions, jump back, and replay. Latency and throughput: Kafka is designed to provide minimal delays while simultaneously processing enormous volumes of events. Example Simple Python Application for Working with Kafka If Kafka is not yet installed, the easiest way to "experiment" with it is to install it via Docker. For this, it's sufficient to create a docker-compose.yml file with minimal configuration: version: "3" services: broker: image: apache/kafka:latest container_name: broker ports: - "9092:9092" environment: KAFKA_NODE_ID: 1 KAFKA_PROCESS_ROLES: broker,controller KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093 KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092 KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT KAFKA_CONTROLLER_QUORUM_VOTERS: 1@localhost:9093 KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1 KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1 KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1 KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0 KAFKA_NUM_PARTITIONS: 3 Run: docker compose up -d Running Kafka in the Cloud In addition to local deployment via Docker, Kafka can be run in the cloud. This eliminates unnecessary complexity and saves time. In Hostman, you can create a ready Kafka instance in just a few minutes: simply choose the region and configuration, and the installation and setup happen automatically. The cloud platform provides high performance, stability, and technical support, so you can focus on development and growth of your project without being distracted by infrastructure. Try Hostman and experience the convenience of working with reliable and fast cloud hosting. Python Scripts for Demonstration Below are examples of Producer and Consumer in Python (using the kafka-python library), the first script writes messages to a topic and the other reads. First, install the Python library: pip install kafka-python producer.py This code sends five messages to the test-topic theme. from kafka import KafkaProducer import json import time # Create Kafka producer and specify broker address # value_serializer converts Python objects to JSON bytes producer = KafkaProducer( bootstrap_servers="localhost:9092", value_serializer=lambda v: json.dumps(v).encode("utf-8"), ) # Send 5 messages in succession for i in range(5): data = {"Message": i} # Form data producer.send("test-topic", data) # Asynchronous send to Kafka print(f"Sent: {data}") # Log to console time.sleep(1) # Pause 1 second between sends # Wait for all messages to be sent producer.flush() consumer.py This Consumer reads messages from the theme, starting from the beginning. from kafka import KafkaConsumer import json # Create Kafka Consumer and subscribe to "test-topic" consumer = KafkaConsumer( "test-topic", # Topic we're listening to bootstrap_servers="localhost:9092", # Kafka broker address auto_offset_reset="earliest", # Read messages from the very beginning if no saved offset group_id="test-group", # Consumer group (for balancing) value_deserializer=lambda v: json.loads(v.decode("utf-8")), # Convert bytes back to JSON ) print("Waiting for messages...") # Infinite loop—listen to topic and process messages for message in consumer: print("Received:", message.value) # Output message content These two small scripts demonstrate basic operations with Kafka: publishing and receiving messages. Conclusion Apache Kafka is an effective tool for building architectures where key factors are event processing, streaming data, high performance, fault tolerance, and latency minimization. It is not a universal replacement for databases but excellently complements them in scenarios where classic solutions cannot cope. With proper architecture, Kafka enables building flexible, responsive systems. When choosing Kafka, it's important to evaluate requirements: data volume, speed, architecture, integrations, ability to manage the cluster. If the system is simple and loads are small—perhaps it's easier to choose a simpler tool. But if the load is large, events flow continuously, and a scalable solution is required, Kafka can become the foundation. Despite certain complexity in setup and maintenance, Kafka has proven its effectiveness in numerous large projects where high speed, reliability, and working with event streams are important.
08 December 2025 · 12 min to read

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