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

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

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

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

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

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

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

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

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

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

1. GitHub Copilot

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

Features

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

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

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

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

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

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

These features help automate routine coding tasks.

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

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

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

Languages Supported

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

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

Pricing Plans

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

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

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

2. Tabnine

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

Features

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

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

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

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

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

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

Languages Supported

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

Pricing Plans

Tabnine is available under two subscription plans:

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

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

3. ChatGPT

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

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

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

Features

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

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

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

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

Here’s what it can do:

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

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

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

For example, you could write this fun prompt:

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

ChatGPT would generate something like this:

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

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

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

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

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

The helicopter has hooked the car with a cable.

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

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

Languages Supported

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

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

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

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

Pricing Plans

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

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

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

4. Claude

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

Features

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

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

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

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

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

Languages Supported

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

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

Pricing Plans

Claude offers several pricing tiers:

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

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

Here’s why:

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

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

5. Snyk Code

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

Features

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

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

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

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

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

Languages Supported

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

Pricing Plans

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

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

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

6. Documatic

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

Features

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

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

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

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

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

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

Languages Supported

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

Pricing Plans

Documatic keeps things simple with just two tiers:

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

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

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

7. Mintlify

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

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

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

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

Features

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

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

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

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

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

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

Languages Supported

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

Pricing Plans

Mintlify offers four plans:

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

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

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

8. Codeium

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

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

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

E7261d0c 08e8 4178 9ec8 Bb509066df1d.png

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

Features

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

  • Code Autocompletion: Provides intelligent suggestions as you type.

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

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

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

Languages Supported

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

Pricing Plans

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

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

9. Gemini

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

Features

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

  • Text Generation, Analysis, and Translation.

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

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

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

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

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

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

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

Languages Supported

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

Pricing Plans

Google offers a fairly straightforward pricing structure for Gemini:

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

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

Conclusion

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

Model

Type

Features

Pricing

Copilot

Specialized

Code generation, autocompletion

Subscription

Tabnine

Specialized

Autocompletion

Subscription

ChatGPT

General

Generation, analysis

Free, subscription

Claude

General

Generation, analysis

Free, subscription

Snyk Code

Specialized

Analysis

Free, subscription

Documatic

Specialized

Documentation

Free, subscription

Mintlify

Specialized

Documentation, hosting

Free, subscription

Codeium

Specialized

Generation, analysis

Free, subscription

Gemini

General

Generation, analysis

Free, subscription

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

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

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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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