Bare metal servers present a powerful solution for individuals or businesses seeking unmatched performance and reliability. These servers—unlike shared hosting services—are exclusively allocated to a single user or tenant, providing dedicated resources for tasks that require top-tier performance, customization, and dependability. In this in-depth exploration of bare metal servers, we’ll explore their function, benefits, and potential applications, helping you decide whether this robust computing solution is the right fit for your business, project, or client needs.
Try Bare Metal with 35% off this month
If you’ve been disappointed by your provider during peak season or you’re looking to scale going into 2025, this is the opportunity for you. For a limited time, new customers can enjoy 35% off on High-frequency Bare Metal Servers for two months.
Take advantage of the offer by January 7 to secure your discount, available for the first 500 customers.
What Are the Key Features of Bare Metal Servers?
A bare metal server is a physical server that is accessed via the cloud. It offers a unique degree of control to a single user over the equipment, unlike shared services. When using a bare metal server, the client has complete management over the hardware resources and server’s load. This results in the following key features:
- High performance. Bare metal provides robust performance capabilities, as the entire server’s resources are dedicated to a single user. This results in faster data processing, reduced latency, and improved overall performance.
- Dedicated resources. Bare metal servers offer dedicated hardware resources such as CPU, RAM, storage, and network interfaces. This means you have exclusive access to these resources, ensuring optimal performance for your applications.
- Enhanced security. Enhanced security is available with bare metal, compared to shared or virtualized environments. Users have complete control over the server’s security measures, ensuring the option of implementing stringent protection protocols. This makes bare metal an ideal solution for sensitive data.
- Scalability. Bare metal offers scalability options to accommodate growing demands. Users can easily upgrade or expand server resources to meet changing requirements without any disruption.
These are just a few of bare metal’s key features. In the next section, let’s take a closer look at how a bare metal server works and explore its benefits in more detail.
How Do Bare Metal Servers Work?
Bare metal servers operate by granting direct access to the underlying physical hardware; they eliminate unnecessary layers and complexities regarding server access, resulting in a more efficient system. Here’s a breakdown of a bare metal server’s operation.
1. Operating System Installation
Users have the flexibility to choose their preferred operating system (OS) and install it directly on the bare metal server. This eliminates the need for virtualization software and provides greater control and customization options. Additionally, keeping the operating system up to date is crucial for optimal server performance as it helps protect data and defend against external threats.
2. Resource Allocation and Management
Resource allocation on a bare metal server gives users full control over computing resources. After the operating system is installed, users can customize the allocation of CPU cores, assign the desired amount of RAM, and determine the storage capacity based on their specific requirements.
This level of control enables optimized utilization of server resources, allowing users to allocate the right amount of computing power, memory, and storage to different applications and workloads. Users can ensure efficient performance and responsiveness for their specific tasks by tailoring the resource allocation to their needs, maximizing the server’s capabilities.
3. Hardware Provisioning and Configuration
The process of hardware provisioning involves assigning and configuring specific physical components of a bare metal server, such as the CPU, RAM, storage, and network interfaces, for exclusive use by the server. This ensures that the user has complete access to the allocated resources and can make the most of them.
What Is the Difference Between Bare Metal Servers and Shared Hosting Servers?
If you own a website, you’re probably familiar with shared hosting servers, where users share resources to save costs. However, there are downsides. Shared hosting is limited in terms of performance and reliability because resources are distributed among multiple users, leading to potential slowdowns during peak usage or when other users’ needs increase. As such, businesses or individuals with resource-intensive applications, growing traffic, or high-performance requirements might encounter performance problems when using shared hosting.
Enter bare metal servers. A bare metal server is a physical server that is exclusively allocated to a single user or tenant. You have your own private space, where you can enjoy all the resources and benefits of having an entire server just for yourself. Unlike shared hosting servers, bare metal servers are not shared with other users, providing dedicated resources for enhanced performance and reliability—this means that bare metal is a serious upgrade for serious users. Bare metal is perfect for tasks that need top-notch performance, reliability, and customization.
What is the Difference Between Bare Metal Servers and Dedicated Server?
While there are similarities between bare metal and dedicated servers—such as being dedicated to a single user and not sharing resources with other users—bare metal servers use more advanced technology in terms of hardware. The main difference lies in how they operate. A bare metal server offers direct access to the hardware and allows for greater customization, while a dedicated server operates within a virtualized environment, sacrificing performance for the benefits of scalability and resource sharing. Dedicated servers can be challenging to operate. On the other hand, bare metal servers operate seamlessly with the assistance of a software called a hypervisor.
This means that a bare metal server is a significantly more powerful solution compared to a dedicated server. A dedicated server can get the job done for certain use cases, but it is worth noting that a bare metal server gives you complete control. It offers superior hardware and greater flexibility for customization to suit your particular requirements to your business.
Who Should Use Bare Metal Servers?
Bare metal servers provide flexible usage options to meet diverse needs. However, the question arises: Who should consider using a bare metal server? Is it suitable for everyone? To help you find the answer, we have compiled a list of who can benefit from using bare metal servers with real examples from across industries.
1. Businesses That Need High Bandwidth to Meet Growing Demands
If you’re a business owner and your online service requires significant processing power and memory to handle demanding workloads like high-traffic websites, gaming servers, or video streaming platforms, it is worth considering using bare metal servers. These servers are designed to provide the necessary resources and performance capabilities to support such intensive tasks.
Case Study: TV Streaming Services
Based on the Digital 2023: Global Overview Report, it is observed that the latest findings show that popular streaming services like Netflix and Disney+ have gained significant traction, capturing over 45% of the total TV viewing time among internet users of working age. This represents an increase of 10% since Q3 2019, indicating that users are now spending more than 1.5 hours per day on streaming services and online TV.
In order to meet the rising expectations of users and stay competitive in the media industry, content providers need to ensure fast and high-quality content delivery. This is particularly important when facing competition from established platforms like Netflix. To achieve this, having an infrastructure with sufficient bandwidth is crucial; this is where bare metal servers come into play. These servers offer the capability to deliver content at high speeds, ensuring a seamless and enjoyable user experience. Gcore offers an exclusive infrastructure tailored for streaming video, which can be combined with Gcore Bare Metal for a top-of-the-line streaming solution.
2. Services Handling Highly Sensitive Data
Many websites and applications handle highly sensitive information and require enhanced protection options.
Case Study: Fintech, Healthcare, and E-Commerce
In the high-stakes world of fintech, bare metal servers provide the robust security measures required for the safe handling and storage of sensitive financial documents. Similarly, healthcare institutions such as hospitals can use the advanced security features of bare metal servers to protect vital medical records. E-commerce sites, social media platforms, and streaming services continuously handle large volumes of confidential data, such as credit card numbers or social security details.
To ensure the protection of sensitive data, bare metal is a wise choice. Some bare metal server providers, like Gcore, offer built-in defenses against common security threats like DDoS attacks. It’s important to examine these security features available in bare metal servers. We’ll get into this in more detail in the next section.
3. Gaming Companies
By utilizing bare metal servers, gaming companies and their developers can meet the performance demands of modern games, ensuring fast processing speeds and low latency. This is especially crucial for AAA games that require high-performance capabilities.
Additionally, in multiplayer gaming, low latency is essential to provide a seamless and immersive experience for players. Bare metal servers contribute to low latency by reducing network latency, minimizing delays, and enhancing overall gameplay quality. Bare metal servers also help to ensure the security of user data, which is critical in the gaming industry where personal information and payment details are handled.
4. AI and Machine Learning Developers
With 2023’s focus on AI, numerous companies—from tech giants to individual entrepreneurs—are jumping on board to create an impact and take advantage of this growing industry. However, these types of projects require substantial processing capabilities to handle complex algorithms and large datasets. By utilizing bare metal servers, developers can access dedicated hardware resources, such as powerful CPUs and ample RAM, which are crucial for running resource-intensive AI and machine learning workloads.
In addition, the dedicated nature of bare metal servers ensures enhanced data security and privacy. AI and machine learning applications often deal with sensitive data, such as customer information or proprietary datasets. Developers can isolate their workloads on bare metal and maintain complete control over security measures, reducing the risk of unauthorized access or data breaches.
Here’s a helpful table that summarizes the various use cases of bare metal servers by industry or sector:
| Sector/industry | Use cases | Real-life example |
| Technology | Hosting high performance applications Virtualization platforms Big data workloads AI/Machine learning | Hosting a high-traffic website Creating an AI tool Running a data analytics platform |
| Finance | Secure and high-performance applications Banking systems, including financial transaction processing Data management | Hosting a banking system Processing financial transactions securely |
| Healthcare | Electronic health records Healthcare applications Data security and privacy Medical research | Hosting an electronic health record system Running medical research simulations |
| Gaming and media entertainment | Online gaming servers Media streaming platforms Content distribution | Multiplayer game servers like Minecraft and Counter-Strike Media streaming platforms such as Netflix and Apple TV |
| E-commerce | Hosting online platforms Managing high-traffic websites Processing secure transactions | Online marketplaces such as Amazon and Alibaba Processing customer orders securely |
| Research and data science | Complex simulations Large dataset analysis Scientific research Machine learning training | Running scientific simulations Training machine learning models |
It’s important to keep in mind that the way bare metal servers are used can differ depending on specific business needs and requirements. Additionally, there may be other industries that use bare metal servers that are not included in our list.
After learning about the different use cases that can benefit from using a bare metal server, you might be interested in acquiring one for either business or personal use. Let’s discuss the essential factors you should consider before purchasing.
Factors to consider when purchasing a bare metal server
Suppose you’re considering using a bare metal server for your business or project. In that case, there are several necessary factors to consider in order to make an informed decision when purchasing. Let’s take a closer look at them:
1. Cost
Cost is a significant factor to consider when purchasing a bare metal server, and here’s why. First, you need to find a server that fits your budget. Setting a budget and exploring different pricing options allows you to choose a server that offers excellent value without breaking the bank. Secondly, considering pricing allows you to compare the costs and benefits of different service providers. You can evaluate features, performance, and support to find the best deal for your budget.
Taking pricing and your budget into account helps you plan for the future. You can budget for the initial purchase and any ongoing expenses, like maintenance and future upgrades. You should make a purchase confident that you can afford the server and any additional services you need—not only now, but for the foreseeable future.
2. Resources and Performance
Understanding the resources available on a server helps you make sure that it can handle your specific capacity and power requirements. Different applications and workloads have different demands for CPU, RAM, storage and networking capabilities.
Performance is vital in delivering a smooth and great experience for your apps and services, but purchasing a bare metal server is a significant investment. That is why it becomes crucial to carefully assess which resources you actually need to achieve your performance goals, and purchase accordingly. By carefully evaluating the resources and performance of a bare metal server, you can ensure that it aligns with your specific needs, delivers the desired performance level, and provides the scalability required for future growth.
3. Support and Maintenance Services
It is essential to take note of the service provider’s support and maintenance services. Reliable support means that any issues or technical difficulties can be promptly addressed and resolved, minimizing downtime and disruptions to your operations. Regular maintenance and updates are also essential to keep your server running smoothly and securely, protecting your data and ensuring optimal performance. So, choosing a provider that offers comprehensive support and maintenance services can provide you with peace of mind and minimize potential risks and challenges associated with server management.
With all of these considerations in mind, let’s explore how Gcore’s bare metal server can benefit businesses and various sectors with its reliable bare metal service.
How Does a Gcore Bare Metal Server Fit Your Needs?
At Gcore, we recognize the importance of balancing affordability, effectiveness, security, and high performance when it comes to servers. That’s why our clients can benefit from our range of cloud services, including our bare metal servers. We prioritize value for money, performance, and top-of-the-line support to ensure a seamless experience for your business.
Let’s take a look at the advantages of Gcore Bare Metal:
- 99.95% SLA with financial guarantees. We provide a production-ready platform that guarantees uptime and provides comprehensive support service level agreements (SLAs.) Our platform is designed to ensure uninterrupted operation and maximum availability for your critical applications and services. With our reliable infrastructure and dedicated support team, you rest assured that your business will maintain a high level of uptime to meet your operational requirements.
- Vast computing resources. At Gcore, we provide unrestricted access to a vast global network of computing power. With our presence in 23 locations worldwide, we offer a low-latency infrastructure that ensures your applications and services can reach users globally with minimal delays. Take advantage of the benefits of our extensive global reach and leverage our infrastructure to deliver optimal performance and user experience.
- Security and reliability. We offer DDoS protection against DDoS attacks at the network and transport layers. Our comprehensive security measures ensure that your infrastructure remains safeguarded from malicious traffic and potential disruptions caused by DDoS attacks. In addition, we offer integration with CDN. You can connect our CDN to the cloud to speed up content delivery to end users in any point of the world. You can also connect our other products: Streaming Platform and Storage.
- Dedicated 24/7 support. We provide round-the-clock, expert technical support. Our dedicated support team is always ready to assist you with any technical issues or inquiries you may have, ensuring that you receive timely and reliable support whenever you need it. We are committed to delivering excellent customer service, day and night.
Learn more about the features, benefits and pricing about Gcore Bare Metal.
Conclusion
A bare metal server is a powerful and versatile hosting solution for businesses and individuals seeking high performance, security, and control over their infrastructure. Its use cases span various industries, including online services, media streaming, gaming, and AI. When buying bare metal servers, it’s crucial to take into account the cost, necessary resources, and available support.
Here at Gcore, we understand the importance of bridging affordability, security, and high performance. With our bare metal server offering, we prioritize cost-efficiency, performance, and reliable support. Our global platform ensures protection against DDoS attacks and provides a production-ready environment with guaranteed uptime and 24/7 support.
Interested in trying out Gcore bare metal service for your business or big project? Want to learn more about how bare metal works? Talk to our experts today. We’re ready to help and hear from you!
Related articles
How to get the size of a directory in Linux
Understanding how to check directory size in Linux is critical for managing storage space efficiently. Understanding this process is essential whether you’re assessing specific folder space or preventing storage issues.This comprehensive guide covers commands and tools so you can easily calculate and analyze directory sizes in a Linux environment. We will guide you step-by-step through three methods: du, ncdu, and ls -la. They’re all effective and each offers different benefits.What is a Linux directory?A Linux directory is a special type of file that functions as a container for storing files and subdirectories. It plays a key role in organizing the Linux file system by creating a hierarchical structure. This arrangement simplifies file management, making it easier to locate, access, and organize related files. Directories are fundamental components that help ensure smooth system operations by maintaining order and facilitating seamless file access in Linux environments.#1 Get Linux directory size using the du commandUsing the du command, you can easily determine a directory’s size by displaying the disk space used by files and directories. The output can be customized to be presented in human-readable formats like kilobytes (KB), megabytes (MB), or gigabytes (GB).Check the size of a specific directory in LinuxTo get the size of a specific directory, open your terminal and type the following command:du -sh /path/to/directoryIn this command, replace /path/to/directory with the actual path of the directory you want to assess. The -s flag stands for “summary” and will only display the total size of the specified directory. The -h flag makes the output human-readable, showing sizes in a more understandable format.Example: Here, we used the path /home/ubuntu/, where ubuntu is the name of our username directory. We used the du command to retrieve an output of 32K for this directory, indicating a size of 32 KB.Check the size of all directories in LinuxTo get the size of all files and directories within the current directory, use the following command:sudo du -h /path/to/directoryExample: In this instance, we again used the path /home/ubuntu/, with ubuntu representing our username directory. Using the command du -h, we obtained an output listing all files and directories within that particular path.#2 Get Linux directory size using ncduIf you’re looking for a more interactive and feature-rich approach to exploring directory sizes, consider using the ncdu (NCurses Disk Usage) tool. ncdu provides a visual representation of disk usage and allows you to navigate through directories, view size details, and identify large files with ease.For Debian or Ubuntu, use this command:sudo apt-get install ncduOnce installed, run ncdu followed by the path to the directory you want to analyze:ncdu /path/to/directoryThis will launch the ncdu interface, which shows a breakdown of file and subdirectory sizes. Use the arrow keys to navigate and explore various folders, and press q to exit the tool.Example: Here’s a sample output of using the ncdu command to analyze the home directory. Simply enter the ncdu command and press Enter. The displayed output will look something like this:#3 Get Linux directory size using 1s -1aYou can alternatively opt to use the ls command to list the files and directories within a directory. The options -l and -a modify the default behavior of ls as follows:-l (long listing format)Displays the detailed information for each file and directoryShows file permissions, the number of links, owner, group, file size, the timestamp of the last modification, and the file/directory name-a (all files)Instructs ls to include all files, including hidden files and directoriesIncludes hidden files on Linux that typically have names beginning with a . (dot)ls -la lists all files (including hidden ones) in long format, providing detailed information such as permissions, owner, group, size, and last modification time. This command is especially useful when you want to inspect file attributes or see hidden files and directories.Example: When you enter ls -la command and press Enter, you will see an output similar to this:Each line includes:File type and permissions (e.g., drwxr-xr-x):The first character indicates the file type- for a regular filed for a directoryl for a symbolic linkThe next nine characters are permissions in groups of three (rwx):r = readw = writex = executePermissions are shown for three classes of users: owner, group, and others.Number of links (e.g., 2):For regular files, this usually indicates the number of hard linksFor directories, it often reflects subdirectory links (e.g., the . and .. entries)Owner and group (e.g., user group)File size (e.g., 4096 or 1045 bytes)Modification date and time (e.g., Jan 7 09:34)File name (e.g., .bashrc, notes.txt, Documents):Files or directories that begin with a dot (.) are hidden (e.g., .bashrc)ConclusionThat’s it! You can now determine the size of a directory in Linux. Measuring directory sizes is a crucial skill for efficient storage management. Whether you choose the straightforward du command, use the visual advantages of the ncdu tool, or opt for the versatility of ls -la, this expertise enhances your ability to uphold an organized and efficient Linux environment.Looking to deploy Linux in the cloud? With Gcore Edge Cloud, you can choose from a wide range of pre-configured virtual machines suitable for Linux:Affordable shared compute resources starting from €3.2 per monthDeploy across 50+ cloud regions with dedicated servers for low-latency applicationsSecure apps and data with DDoS protection, WAF, and encryption at no additional costGet started today
How to Run Hugging Face Spaces on Gcore Inference at the Edge
Running machine learning models, especially large-scale models like GPT 3 or BERT, requires a lot of computing power and comes with a lot of latency. This makes real-time applications resource-intensive and challenging to deliver. Running ML models at the edge is a lightweight approach offering significant advantages for latency, privacy, and resource optimization. Gcore Inference at the Edge makes it simple to deploy and manage custom models efficiently, giving you the ability to deploy and scale your favorite Hugging Face models globally in just a few clicks. In this guide, we’ll walk you through how easy it is to harness the power of Gcore’s edge AI infrastructure to deploy a Hugging Face Space model. Whether you’re developing NLP solutions or cutting-edge computer vision applications, deploying at the edge has never been simpler—or more powerful. Step 1: Log In to the Gcore Customer PortalGo to gcore.com and log in to the Gcore Customer Portal. If you don’t yet have an account, go ahead and create one—it’s free. Step 2: Go to Inference at the EdgeIn the Gcore Customer Portal, click Inference at the Edge from the left navigation menu. Then click Deploy custom model. Step 3: Choose a Hugging Face ModelOpen huggingface.com and browse the available models. Select the model you want to deploy. Navigate to the corresponding Hugging Face Space for the model. Click on Files in the Space and locate the Docker option. Copy the Docker image link and startup command from Hugging Face Space. Step 4: Deploy the Model on GcoreReturn to the Gcore Customer Portal deployment page and enter the following details: Model image URL: registry.hf.space/ethux-mistral-pixtral-demo:latest Startup command: python app.py Container port: 7860 Configure the pod as follows: GPU-optimized: 1x L40S vCPUs: 16 RAM: 232GiB For optimal performance, choose any available region for routing placement. Name your deployment and click Deploy.Step 5: Interact with Your ModelOnce the model is up and running, you’ll be provided with an endpoint. You can now interact with the model via this endpoint to test and use your deployed model at the edge.Powerful, Simple AI Deployment with GcoreGcore Inference at the Edge is the future of AI deployment, combining the ease of Hugging Face integration with the robust infrastructure needed for real-time, scalable, and global solutions. By leveraging edge computing, you can optimize model performance and simultaneously futureproof your business in a world that increasingly demands fast, secure, and localized AI applications. Deploying models to the edge allows you to capitalize on real-time insights, improve customer experiences, and outpace your competitors. Whether you’re leading a team of developers or spearheading a new AI initiative, Gcore Inference at the Edge offers the tools you need to innovate at the speed of tomorrow. Explore Gcore Inference at the Edge
10 Common Web Performance Mistakes and How to Overcome Them
Web performance mistakes can carry a high price, resulting in websites that yield low conversion rates, high bounce rates, and poor sales. In this article, we dig into the top 10 mistakes you should avoid to boost your website performance.1. Slow or Unreliable Web HostYour site speed begins with your web host, which provides the server infrastructure and resources for your website. This includes the VMs and other infrastructure where your code and media files reside. Three common host-related problems are as follows:Server location: The further away your server is from your users, the slower the site speed and the poorer the experience for your website visitors. (More on this under point 7.)Shared hosting: Shared hosting solutions share server resources among multiple websites, leading to slow load times and spotty connections during peak times due to heavy usage. Shared VMs can also impact your website’s performance due to increased network traffic and resource contention.VPS hosting: Bandwidth limitations can be a significant issue with VPS hosting. A limited bandwidth package can cause your site speed to decrease during high-traffic periods, resulting in a sluggish user experience.Correct for server and VM hosting issues by choosing a provider with servers located closer to your user base and provisioning sufficient computational resources, like Gcore CDN. Use virtual dedicated servers (VDS/VPS) rather than shared hosting to avoid network traffic from other websites affecting your site’s performance. If you already use a VPS, consider upgrading your hosting plan to increase server resources and improve UX. For enterprises, dedicated servers may be more suitable.2. Inefficient Code, Libraries, and FrameworksPoor-quality code and inefficient frameworks can increase the size of web pages, consume too many resources, and slow down page load times. Code quality is often affected by syntax, semantics, and logic errors. Correct these issues by writing clean and simple code.Errors or inefficiencies introduced by developers can impact site performance, such as excessive API calls or memory overuse. Prevent these issues by using TypeScript, console.log, or built-in browser debuggers during development. For bugs in already shipped code, utilize logging and debugging tools like the GNU debugger or WinDbg to identify and resolve problems.Improving code quality also involves minimizing the use of large libraries and frameworks. While frontend frameworks like React, Vue, and Angular.js are popular for accelerating development, they often include extensive JavaScript and prebuilt components that can bloat your website’s codebase. To optimize for speed, carefully analyze your use case to determine if a framework is necessary. If a static page suffices, avoid using a framework altogether. If a framework is needed, select libraries that allow you to link only the required components.3. Unoptimized Code Files and FontsEven high-quality code needs optimization before shipping. Unoptimized JavaScript, HTML, and CSS files can increase page weight and necessitate multiple HTTP requests, especially if JavaScript files are executed individually.To optimize code, two effective techniques are minification and bundling.Minification removes redundant libraries, code, comments, unnecessary characters (e.g., commas and dots), and formatting to reduce your source code’s size. It also shortens variable and function names, further decreasing file size. Tools for minification include UglifyJS for JavaScript, CSSNano for CSS, and HTMLminifier for HTML.Bundling groups multiple files into one, reducing the number of HTTP requests and speeding up site load times. Popular bundling tools include Rollup, Webpack, and Parcel.File compression using GZIP or Brotli can also reduce the weight of HTTP requests and responses before they reach users’ browsers. Enable your chosen compression technique on your server only after checking that your server provider supports it.4. Unoptimized Images and VideosSome websites are slowed down by large media files. Upload only essential media files to your site. For images, compress or resize them using tools like TinyPNG and Compressor.io. Convert images from JPEG, PNG, and GIF to WebP and AVIF formats to maintain quality while reducing file size. This is especially beneficial in industries like e-commerce and travel, where multiple images boost conversion rates. Use dynamic image optimization services like Gcore Image Stack for efficient processing and delivery. For pages with multiple images, use CSS sprites to group them, reducing the number of HTTP requests and speeding up load times.When adding video files, use lite embeds for external links. Standard embed code, like YouTube’s, is heavy and can slow down your pages. Lite embeds load only thumbnail images initially, and the full video loads when users click the thumbnail, improving page speed.5. No Lazy LoadingLazy loading delays the rendering of heavy content like images and JavaScript files until the user needs it, contrasting with “eager” loading, which loads everything at once and slows down site load times. Even with optimized images and code, lazy loading can further enhance site speed through a process called “timing.”Image timing uses the HTML loading attribute in an image tag or frameworks like Angular or React to load images in response to user actions. The browser only requests images when the user interacts with specific features, triggering the download.JavaScript timing controls when certain code loads. If JavaScript doesn’t need to run until the entire page has rendered, use the defer attribute to delay its execution. If JavaScript can load at any time without affecting functionality, load it asynchronously with the async attribute.6. Heavy or Redundant External Widgets and PluginsWidgets and plugins are placed in designated frontend and backend locations to extend website functionality. Examples include Google review widgets that publish product reviews on your website and Facebook plugins that connect your website to your Facebook Page. As your website evolves, more plugins are typically installed, and sometimes website admins forget to remove those that are no longer required.Over time, heavy and unused plugins can consume substantial resources, slowing down your website unnecessarily. Widgets may also contain heavy HTML, CSS, or JavaScript files that hinder web performance.Remove unnecessary plugins and widgets, particularly those that make cURL calls, HTTP requests, or generate excessive database queries. Avoid plugins that load heavy scripts and styles or come from unreliable sources, as they may contain malicious code and degrade website performance.7. Network IssuesYour server’s physical location significantly impacts site speed for end users. For example, if your server is in the UK and your users are in China, they’ll experience high latency due to the distance and DNS resolution time. The greater the distance between the server and the user, the more network hops are required, increasing latency and slowing down site load times.DNS resolution plays a crucial role in this process. Your authoritative DNS provider resolves your domain name to your IP address. If the provider’s server is too far from the user, DNS resolution will be slow, giving visitors a poor first impression.To optimize content delivery and reduce latency, consider integrating a content delivery network (CDN) with your server-side code. A CDN stores copies of your static assets (e.g., container images, JavaScript, CSS, and HTML files) on geographically distributed servers. This distribution ensures that users can access your content from a server closer to their location, significantly improving site speed and performance.8. No CachingWithout caching, your website has to fetch data from the origin server every time a user requests. This increases the load time because the origin server is another physical hop that data has to travel.Caching helps solve this problem by serving pre-saved copies of your website. Copies of your web files are stored on distributed CDN servers, meaning they’re available physically closer to website viewers, resulting in quicker load times.An additional type of caching, DNS caching, temporarily stores DNS records in DNS resolvers. This allows for faster domain name resolution and accelerates the initial connection to a website.9. Excessive RedirectsWebsite redirects send users from one URL to another, often resulting in increased HTTP requests to servers. These additional requests can potentially crash servers or cause resource consumption issues. To prevent this, use tools like Screaming Frog to scan your website for redirects and reduce them to only those that are absolutely necessary. Additionally, limit each redirect to making no more than one request for a .css file and one for a .js file.10. Lack of Mobile OptimizationForgetting to optimize for mobile can harm your website’s performance. Mobile-first websites optimize for speed and UX. Better UX leads to happier customers and increased sales.Optimizing for mobile starts with understanding the CPU, bandwidth, and memory limitations of mobile devices compared to desktops. Sites with excessively heavy files will load slowly on mobiles. Writing mobile-first code, using mobile devices or emulators for building and testing, and enhancing UX for various mobile device types—such as those with larger screens or higher capacity—can go a long way to optimizing for mobile.How Can Gcore Help Prevent These Web Performance Mistakes?If you’re unsure where to start in correcting or preventing web performance mistakes, don’t worry—you don’t have to do it alone. Gcore offers a comprehensive suite of solutions designed to enhance your web performance and deliver the best user experience for your visitors:Powerful VMs: Fast web hosting with a wide range of virtual machines.Managed DNS: Hosting your DNS zones and ensuring quick DNS resolution with our fast Managed DNS.CDN: Accelerate both static and dynamic components of your website for global audiences.With robust infrastructure from Gcore, you can ensure optimal performance and a seamless experience for all your web visitors. Keep your website infrastructure in one place for a simplified website management experience.Need help getting started? Contact us for a personalized consultation and discover how Gcore can supercharge your website performance.Get in touch to boost your website
How to Choose Between Bare Metal GPUs and Virtual GPUs for AI Workloads
Choosing the right GPU type for your AI project can make a huge difference in cost and business outcomes. The first consideration is often whether you need a bare metal or virtual GPU. With a bare metal GPU, you get a physical server with an entire GPU chip (or chips) installed that is completely dedicated to the workloads you run on the server, whereas a virtual GPU means you share GPU resources with other virtual machines.Read on to discover the key differences between bare metal GPUs and virtual GPUs, including performance and scalability, to help you make an informed decision.The Difference Between Bare Metal and Virtual GPUsThe main difference between bare metal GPUs and virtual GPUs is how they use physical GPU resources. With a bare metal GPU, you get a physical server with an entire GPU chip (or chips) installed that is completely dedicated to the workloads you run on the server. There is no hypervisor layer between the operating system (OS) and the hardware, so applications use the GPU resources directly.With a virtual GPU, you get a virtual machine (VM) and uses one of two types of GPU virtualization, depending on your or a cloud provider’s capabilities:An entire, dedicated GPU used by a VM, also known as a passthrough GPUA shared GPU used by multiple VMs, also known as a vGPUAlthough a passthrough GPU VM gets the entire GPU, applications access it through the layers of a guest OS and hypervisor. Also, unlike a bare metal GPU instance, other critical VM resources that applications use, such as RAM, storage, and networking, are also virtualized.The difference between running applications with bare metal and virtual GPUsThese architectural features affect the following key aspects:Performance and latency: Applications running on a VM with a virtual GPU, especially vGPU, will have lower processing power and higher latency for the same GPU characteristics than those running on bare metal with a physical GPU.Cost: As a result of the above, bare metal GPUs are more expensive than virtual GPUs.Scalability: Virtual GPUs are easier to scale than bare metal GPUs because scaling the latter requires a new physical server. In contrast, a new GPU instance can be provisioned in the cloud in minutes or even seconds.Control over GPU hardware: This can be critical for certain configurations and optimizations. For example, when training massive deep learning models with a billion parameters, total control means the ability to optimize performance optimization—and that can have a big impact on training efficiency for massive datasets.Resource utilization: GPU virtualization can lead to underutilization if the tasks being performed don’t need the full power of the GPU, resulting in wasted resources.Below is a table summarizing the benefits and drawbacks of each approach: Bare metal GPUVirtual GPUPassthrough GPUvGPUBenefitsDedicated GPU resourcesHigh performance for demanding AI workloadsLower costSimple scalabilitySuitable for occasional or variable workloadsLowest costSimple scalabilitySuitable for occasional or variable workloadsDrawbacksHigh cost compared to virtual GPUsLess flexible and scalable than virtual GPUsLow performanceNot suitable for demanding AI workloadsLowest performanceNot suitable for demanding AI workloadsShould You Use Bare Metal or Virtual GPUs?Bare metal GPUs and virtual GPUs are typically used for different types of workloads. Your choice will depend on what AI tasks you’re looking to perform.Bare metal GPUs are better suited for compute-intensive AI workloads that require maximum performance and speed, such as training large language models. They are also a good choice for workloads that must run 24/7 without interruption, such as some production AI inference services. Finally, bare metal GPUs are preferred for real-time AI tasks, such as robotic surgery or high-frequency trading analytics.Virtual GPUs are a more suitable choice for the early stages of AI/ML and iteration on AI models, where flexibility and cost-effectiveness are more important than top performance. Workloads with variable or unpredictable resource requirements can also run on this type of GPU, such as training and fine-tuning small models or AI inference tasks that are not sensitive to latency and performance. Virtual GPUs are also great for occasional, short-term, and collaborative AI/ML projects that don’t require dedicated hardware—for example, an academic collaboration that includes multiple institutions.To choose the right type of GPU, consider these three factors:Performance requirements. Is the raw GPU speed critical for your AI workloads? If so, bare metal GPUs are a superior choice.Scalability and flexibility. Do you need GPUs that can easily scale up and down to handle dynamic workloads? If yes, opt for virtual GPUs.Budget. Depending on the cloud provider, bare metal GPU servers can be more expensive than virtual GPU instances. Virtual GPUs typically offer more flexible pricing, which may be appropriate for occasional or variable workloads.Your final choice between bare metal GPUs and virtual GPUs depends on the specific requirements of the AI/ML project, including performance needs, scalability requirements, workload types, and budget constraints. Evaluating these factors can help determine the most appropriate GPU option.Choose Gcore for Best-in-Class AI GPUsGcore offers bare metal servers with NVIDIA H100, A100, and L40S GPUs. Using the 3.2 Tbps InfiniBand interface, you can combine H100 or A100 servers into scalable GPU clusters for training and tuning massive ML models or for high-performance computing (HPC).If you are looking for a scalable and low-latency solution for global AI inference, explore Gcore Inference at the Edge. It especially benefits latency-sensitive, real-time applications, such as generative AI and object recognition.Discover Gcore bare metal GPUs
How to Configure Grafana for Visualizing Kubernetes (K8s) Cluster Monitoring
Kubernetes monitoring allows you to observe your workloads and cluster resources, spot issues and failures, and efficiently manage pods and other resources. Cluster admins should prioritize tracking the performance and stability of clusters in these environments. One popular tool that can help you visualize Kubernetes monitoring is Grafana. This monitoring solution lets you display K8s metrics through interactive dashboards and real-time alerts. It seamlessly integrates with Prometheus and other data sources, providing valuable insights.Gcore Managed Kubernetes simplifies the Grafana setup process by providing a managed service that includes tools like Grafana. In this article, we’ll explain how to set up and configure Grafana to monitor Kubernetes, its key metrics, and dashboards.Setting Up Grafana for Effective Kubernetes MonitoringTo begin monitoring Kubernetes with Grafana, first, check that you have all the requirements in place: a functioning Kubernetes cluster, the Helm package manager installed, and kubectl set up to communicate with your cluster.Install Grafana in a Kubernetes Cluster. Start by adding the Grafana Helm repository.helm repo add grafana https://grafana.github.io/helm-chartshelm repo updateNext, install Grafana using Helm. This command deploys Grafana into your Kubernetes cluster:helm install grafana grafana/grafanaNow it’s time to configure Grafana for the Kubernetes environment. After installation, retrieve the admin password by using the command below:kubectl get secret --namespace default grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echoThen access the Grafana UI by port-forwarding:kubectl port-forward svc/grafana 3000:80Open your web browser and navigate to http://localhost:3000. Log in using the default username admin and the password you retrieved. Once logged in, you can configure Grafana to monitor your Kubernetes environment by adding data sources such as Prometheus and creating custom dashboards.You’ve now successfully set up Grafana for Kubernetes monitoring!Key Metrics for Kubernetes MonitoringUnderstanding metrics for Kubernetes monitoring allows you to visualize your cluster’s reliability. Key metrics are the following:Node resources. Track CPU and memory usage, disk utilization, and network bandwidth to understand resource consumption and identify bottlenecks.Cluster metrics. Monitor the number of nodes to understand resource billing and overall cluster usage, and track running pods to determine node capacity and identify failures.Pod metrics. Measure how pods are managed and deployed, including instances and deployment status, and monitor container metrics like CPU, memory, and network usage.State metrics. Keep an eye on persistent volumes, disk pressure, crash loops, and job success rates to ensure proper resource management and application stability.Container metrics. Track container CPU and memory usage relative to pod limits, and monitor network data to detect bandwidth issues.Application metrics. Measure application availability, performance, and business-specific metrics to maintain optimal user experience and operational health.Setting Up Grafana DashboardsYou can opt to design and tailor Grafana dashboards to monitor your Kubernetes cluster. This will help you better understand your systems’ performance and overall well-being at a glance.Log into Grafana. Open your web browser, go to http://localhost:3000/, and log in with the default credentials (admin for both username and password), then change your password if/when prompted.Grafana—Log In to Start MonitoringAdd data source. Navigate to Configuration and select Data Sources. Click on Add Data Source and choose the appropriate data source, such as Prometheus.Create a dashboard. Go to Create > Dashboard, click Add New Panel, choose the panel type (e.g., Time series chart, Gauge, Table), and configure it with a PromQL query and visualization settings.Adding a New Panel in Grafana DashboardOrganize and save the dashboard. Arrange panels by clicking Add Panel > Add Row and dragging panels into the desired rows. To save the dashboard, click the save icon, name it, and confirm the save.Gcore Managed Kubernetes for Kubernetes MonitoringWhether you’re getting started with monitoring Kubernetes or you’re a seasoned pro, Gcore Managed Kubernetes offers significant advantages for businesses seeking efficient and reliable Kubernetes cluster monitoring and container management:Ease of integrating Grafana: The service seamlessly integrates with Grafana, enabling effortless visualization and monitoring of performance metrics via dashboards.Automated control: Gcore Managed Kubernetes simplifies the setup and monitoring process by using automation. This service conducts health checks on your nodes, automatically updating and restarting them when needed to keep performance at its best.Enhanced security and reliability: Gcore Managed Kubernetes guarantees the management of nodes by integrating features like automatic scaling and self-repairing systems to maintain optimal performance.Discover Gcore Managed Kubernetes, including automated scaling, one-click provisioning, and Grafana integration.
TCO Comparison: Self-Managed Kubernetes vs. Managed Kubernetes Provider
Calculating the total cost of ownership (TCO) for Kubernetes requires identifying all major expenses, including infrastructure costs, personnel costs, and potential cloud provider fees. With a clear picture of TCO, you can make a more informed decision when choosing between self-managed (self-hosted) Kubernetes and a managed Kubernetes provider. The TCOs of the two approaches are significantly different, and this article will show you exactly how and why.TCO Comparison SummaryThe table below shows the key aspects of the TCO comparison between self-managed Kubernetes and managed Kubernetes providers. It compares infrastructure expenses, including provider fees, and an engineer’s salary.For this comparison, we’ll assume that a company would need only one DevOps engineer for managed Kubernetes, whereas companies opting for self-hosted Kubernetes would need three. We’ll look at rented cloud VMs for self-hosted, and out-of-the-box K8s clusters for managed Kubernetes—two standard scenarios for a fair comparison. For both scenarios, the infrastructure costs shown in the table are the average when considering AWS, Azure, Google Cloud, and Gcore. InfrastructureEngineers’ salaryTotal annual costSelf-hosted Kubernetes$13,737.64$321,500$335,238Managed Kubernetes$6,157.8$107,167$113,325As you can see, the TCO of self-hosted Kubernetes is almost three times higher than that of managed Kubernetes. Let’s explore the reasons for this major cost discrepancy.Infrastructure Cost ComparisonKubernetes is a free software. But to run it, you have to rent or buy infrastructure, such as VMs or physical servers. The way you do so differs depending on whether you opt for self-hosted or managed Kubernetes. To understand infrastructure costs, we need to take a closer look at each method in turn and explore the components required.Self-hosted KubernetesIf you choose to run K8s independently, you’ll need to rent VMs for the Kubernetes master node (the control plane) and worker nodes. Let’s consider a production-grade cluster consisting of the following:3 VMs for the control plane, required for fault tolerance2 VMs for the worker nodesFor simplicity, we choose VMs with a configuration suitable for an average web project: 8 vCPU, 16 GB RAM, and 75 GB SSD.Here is the pricing* offered by four cloud providers for VMs available in the US:ProviderVM types and resourcesTotal annual cost of five VMsAWSc6g.2xlarge—8 vCPU, 16 GB RAM, 75 GB SSD$12,273.6AzureA8 v2 series—8 vCPU, 16 GB RAM, 64 GB SSD**$17,764.2Google CloudN1 series—8 vCPU, 16 GB RAM, 75 GB SSD$16,721.33Gcoreg1 standard series—8 vCPU, 16 GB RAM, 75 GB SSD$8,191.42Average$13,737.64* Prices are for on-demand VMs; no commitment; no VAT; ingress traffic is not included.** Azure only offers fixed volume sizes for built-in storage.Managed KubernetesWith managed K8s, you don’t have to worry about renting separate VMs and setting up the Kubernetes software. You choose the VM configurations for your worker nodes, and a provider prepares them for you. The result is an out-of-the-box Kubernetes cluster.Sometimes, you also have to consider fees for control plane management (fixed) and egress traffic (consumption-based). Providers like AWS, Google Cloud, and Azure charge for this, while others—like Gcore—don’t.Here are the prices* offered by four cloud providers for similar cluster configurations in the US:ProviderControl plane managementCluster of two worker nodesTotal annual costConfigurationAnnual costAmazon EKS$8768 vCPU, 16 GB RAM, 75 GB SSD$4,909.44$5,785.44AKS (Azure)$8768 vCPU, 16 GB RAM, 64 GB SSD**$7,048.08$7,924.08GKE (Google)$876X vCPUs, X GB RAM$6,832.08$7,708.08Gcore Managed Kubernetes08 vCPU, 16 GB RAM, 75 GB SSD$3,213.6$3,213.6Average$6,157.8* Prices are for on-demand VMs; no commitment; no VAT; ingress traffic is not included.** Azure only offers fixed volume sizes for built-in storage.Engineer Cost ComparisonTo maintain a production-grade cluster for an average web project, you need:For a self-hosted K8s cluster—3 DevOps engineersFor a managed K8s cluster—1 DevOps engineerTo learn more about the technical reasons behind these calculations, read our article on the difference between managed and self-managed Kubernetes.According to Glassdoor, the median salary for a DevOps engineer is as follows:In the US: $140,000In Germany: €69,000 (or $74,333, the highest in Europe) DevOps salary in the USDevOps salary in GermanyAverage annual salarySelf-hosted Kubernetes (3 engineers)$420,000$222,999$321,500Managed Kubernetes (1 engineer)$140,000$74,333$107,167Final ComparisonHere is the final TCO comparison between self-managed Kubernetes and managed Kubernetes providers:ProvidersInfrastructureEngineers’ salaryTotal annual costBy providerAverageSelf-hosted KubernetesAWS$12,273.6$13,737.64$321,500$335,238Azure$17,764.2GCP$16,721.33Gcore$8,191.42Managed KubernetesAmazon EKS$5,785.44$6,157.8$107,167$113,325AKS (Azure)$7,924.08GKE (Google)$7,708.08Gcore Managed Kubernetes$3,213,6Summing UpPlease note that these approximate calculation probably aren’t exactly what you’ll experience. The actual numbers will depend on many factors, including:Size and complexity of your projectLocation where you hire engineers and deploy a K8s clusterChoice of providerHow you consume and scale computing resourcesHowever, the difference between the TCO of the two methods is relevant to what we got above: the total cost of ownership of self-managed Kubernetes is about three times higher than that of managed Kubernetes.The main reason is that Managed Kubernetes means a provider handles many of the most complex operations. This includes managing the underlying infrastructure and control plane, regular and security upgrades, monitoring, scaling the cluster, and, critical to production, high availability guaranteed by an SLA. With self-hosted K8s, you have to do that yourself, which means a larger infrastructure, larger team size, and higher salary costs.ConclusionUnderstanding the TCO difference between self-managed Kubernetes and a managed Kubernetes provider can help you choose a solution that is more suitable for your team and meets your budget. Kubernetes cost analysis can also help you identify areas for optimization, such as right-sizing your infrastructure or optimizing workloads for better resource utilization. However, the TCO isn’t the only aspect of choosing how to run Kubernetes: you should also consider things like the setup and maintenance responsibilities, as well as your project requirements.If you’re looking for reliable, high-performance, and scalable Kubernetes clusters, try Gcore Managed Kubernetes. We offer free cluster management with a 99.9% SLA, bare metal and GPU support for worker nodes, and free egress traffic.Explore Gcore Managed Kubernetes
Subscribe to our newsletter
Get the latest industry trends, exclusive insights, and Gcore updates delivered straight to your inbox.