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What is a VPS? 4 Tips on How to Choose the Best VPS

What is a VPS? 4 Tips on How to Choose the Best VPS
Hostman Team
Technical writer
Infrastructure

What is VPS? Why do developers around the world use it? Why should you use it? How to choose the best one?

In this article, we will answer all the questions diving deep into every topic.

What does VPS stand for?

This abbreviation could be deciphered as "Virtual private server". Or "Virtual dedicated server" in some cases.

The term itself brings the description of the technology it relates to, actually. Here we are talking about a server — a platform where webmasters and developers store their project’s data or test different ideas (it includes website’s documents, applications’ media, etc.). But this server is not real. It is a virtualized copy of it that works as a fully-fledged PC but uses hardware of another device as its own. VPS can be used to create many such computers that can be simulated using only one physical machine.

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Why is it "virtual" and "private"?

It is "virtual" because it exists in the hypervisor — a special application that is installed on a PC and can be used as a full-featured emulator of "real" computers. This emulator takes part of tangible hardware and shares it with an artificial PC using complex virtualization technologies. After that procedure is established the server "looks" like a familiar workspace for developers and webmasters renting it.

It is private because in most cases this kind of server is given full control to the administrator renting it. The whole dedicated infrastructure is controlled by one team and they don’t have to share any resources or data with other users that pay for service of the same hosting.

What is the difference between VPS and VDS?

Let’s talk about virtual dedicated servers a bit more. Sometimes, both abbreviations are used together. Like VDS/VPS. Because they mean the same thing as a product. VPS and VDS are virtual servers that are given full control to one administrator or his team.

But the difference exists and it lies in the technological implementation of virtual servers. VPS is associated with OpenVZ virtualization technology and VDS — with KVM.

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But it is important to understand that this designation is very arbitrary. A lot of developers and webmasters use both terms interchangeably.

What is VPS and how does it work?

In general, VPS is a virtual machine that is installed on some PC that can be remotely controlled via a special application or command-line utility.

VPS is a quite cheap way to get your own server without confusing and disturbing functional limitations like in the case of virtual hosting. It costs less because the provider has to buy one physical machine and implement many virtual servers on it instead of buying a PC for every potential webmaster or developer.

And at the same time, VPS is not really limited in its capabilities. It is almost the same in terms of functionality as its counterpart — a dedicated server.

What are VPS's used for?

So, what exactly you can do with VPS and why they’re so necessary for developers and system administrators:

  1. It is used to create informational web platforms, online stores, and various kinds of commercial web applications.

  2. To store any personal data without go-between services like Dropbox or Google Drive.

  3. To develop and test fully functional lightweight applications or MVPs.

  4. To deploy chunky and complex software platforms like Odoo, 1C Bitrix, etc.

  5. To create personal gaming servers (to make money on it) or mail servers (to anonymize correspondence).

  6. To launch and maintain CCTV systems to store a large number of recordings.

There are other use cases for VPS’ but these listed above are the most relevant.

Advantages and disadvantages of VPS

Speaking of benefits, we must pinpoint things like cheapness, independence, less responsibility, good technical equipment. VPS usually costs less than physical servers and at the same time gives capabilities on par with real computers. In most cases, VPS represents an isolated software platform that is accessible by you and your team members. Even the host can’t get inside it and somehow interact with your virtual PC.

Unfortunately, there are a few drawbacks. The performance of VPS will never be as high as the performance of a real computer. Hypervisor and virtualization technologies will be a bottleneck that blocks it from achieving all the potential of used hardware. Further, it is not possible to have any impact on the physical state of the rented PC. Hardware installed in it is installed by the host. You’d never be allowed to change something inside the machine.

Two types of VPS

As we mentioned earlier, there are two virtualization technologies used to create VPS/VDS servers. OpenVZ and KVM. What kind of VPS should you choose? Let’s break them down:

OpenVZ

  • The amount of resources available for your personal service is dynamically changing. If your web project is in heavy usage, the amount of available resources will grow respectively.

  • It is possible to change any characteristics of your PC at any moment without reloading the operating system. Just pay a bit more if you want a more powerful artificial computer.

  • It is possible to lose some amount of performance because other users are accessing the host with you in parallel. So, you’re not independent. Moreover, your data is visible to the host.

  • You can install only Linux OSes to the OpenVZ server because it is based on the Linux kernel.

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KVM

  • The volume of hardware resources is static. It is closer to a real PC than in the case with OpenVZ.

  • You can change CPU and RAM but it is necessary to relaunch a server so changes take their place.

  • You’re fully independent. Nobody can access your data, not even host administrators.

  • You can decide by yourself what operating system to install. Even if you choose Windows or macOS.

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As you can see, VPS is a much more flexible variant but KVM is more reliable and works as a real PC.

VPS hostings in a nutshell

A hosting provider (also called "host") is a business that creates VPSes and sells access to them to developers and webmasters. The host creates data centers around the world and deploys different applications and websites on them.

Their main task is to make deploying as easy as possible for every user.

VPS in USA: hostings, prices

There are many hosting providers in the USA that are great at doing their job.

  • Bluehost — probably the cheapest VPS and quite a popular platform that gives its users unmetered bandwidth. It also gives users an opportunity to easily migrate from old host to new. Renting a server at Bluehost you are getting a free domain and professional technical support 24/7. It costs about $3/month for the most basic plan.

  • Hostman — modernity is at the core of this service. It is not only amazingly reliable servers based on platforms like AWS, Azure, and Google Cloud. It is also the simplest interface to deploy any application, website, or database in a few clicks. And it is just from $5/month for a powerful platform for your projects.

  • Hostgator — the great multipurpose server that only costs around $4 per month. It gives unmetered disk space and bandwidth, a 45-days guarantee, and a large search credit.

  • DigitalOcean — a basic server at DO will cost you around $5 a month. What’s great about DO — its reliability. It is one of the most fast-growing hostings out there. Functional and modern.

  • AWS — one of the biggest platforms to deploy apps and websites. It is the platform created by Amazon and used by giants like Apple. One of the most functional and reliable. The price depends on the number of projects and their resource capacities.

Is there free VPS out there?

There are but they’re problematic. If your host offers you a free server it comes with many caveats for sure. Like:

  • Obligation to place an ad on your website.

  • Limit of resources.

  • No privacy. Nobody will bother about confidential data.

  • No security. Nobody will defend you from hackers and viruses.

  • Limited functionality.

We don’t recommend using free hosting because there’s no such thing as a free lunch. If you don’t pay for the product — you are the product. Your personal information, your files, your users.

How to choose VPS that fits your needs?

The decision strongly depends on what exactly you need to do with your VPS and what is your working environment. So you must answer some questions before renting the virtual server.

Choose an operating system

It is necessary to select an operating system, whether it will be Windows or some Linux distributive.

Linux is more flexible and lightweight. It is a great choice for small projects and backend systems like databases that are manipulated via command line without any needing for the graphical user interface. Furthermore, Linux is more resistant to hackers’ attacks and resource-intensive tasks.

Windows is an option for users that need to work with Microsoft’s services and products. For example, if your team relies on Teams (tautology intended), Office 365, and Outlook, you’d better consider VPS with Windows onboard. Moreover, it is a nice choice for those of you who want to deploy a remote operating system with a full-fledged graphical interface.

Rent appropriate "hardware"

It is a must to rent a server that is fully capable to deal with the job you’re going to delegate to it. Also, it is really important to pay for a bit more so your project won’t stop working because of exponential user base growth.

The one thing you should definitely consider before renting a server — finding one with SSD storage. It will guarantee delivering the data to users in a most efficient and quick way.

Choose the most effective datacenter

The performance of your websites and applications depends not only on used hardware but also on bandwidth. It is really important to choose a host that can ensure a fast and stable internet connection. Besides that, it would be great to see many data centers around the world so you can deploy your projects as close to your potential users as possible.

Moreover, the host must provide you with security measures like a screening system that blocks malware, security staff which is responsible for protecting servers from any physical impact or thefts. And also it should protect your applications and websites from DDoS attacks and any potential data loss.

Choose VPS that is suitable for the job you’re going to with it

Sometimes you have to choose a host based on more specific criteria. For example, you might need a server that is purposefully created to work with gaming servers. It has specific attributes like accents on more broad bandwidth and capability of fast deploying of gaming worlds. There is a good example of such a server called HostHavoc. It has a highly specialized interface and control panel that allows everyone to create their gaming world in a few clicks.

Some hosts provide amazing server capabilities for trading. Like VPS for Forex that gives you access to an instant executional platform to work with. Additionally, they usually can boast of a professional technical support team that has expertise in trading. So if you’re trying to find the best VPS host for Forex, you should find one with such a technical support team.

Also, we would recommend trying out multipurpose platforms like Hostman. It just asks you what you want to deploy and takes care of the rest. Using Hostman deploying applications, websites, databases, and other stuff is a breeze.

A few tips for those who are going to rent their first VPS

  • Don’t pick a plan with the biggest amount of storage at first. There’s a huge chance for you to overpay. You’d better calculate what SSD you need to launch your project and maintain it.

  • Better to overpay for security measures. If you don’t know how to defend yourself from DDoS attacks, pay someone who’d do this.

  • Don’t rent first found VPS over a long period. The best idea would be to use a testing period. Many hosts give one. For example, Hostman lets new users try out every function of the service for 7 days for free.

Summary

That’s it. VPS is an outstandingly useful tool. The only thing you need to do to make it even more effective is to choose the right one. Consider your priorities and needs while you are going through different hosts and VPSes. Don’t pay forward too much and prioritize not only your needs but users’ of yours. Try VPS by Hostman for 7 days for free to understand if it fits you.

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

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