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  3. What Is a Cloud Server? | How Do Cloud Servers Work?

What Is a Cloud Server? | How Do Cloud Servers Work?

  • By Gcore
  • June 9, 2023
  • 12 min read
What Is a Cloud Server? | How Do Cloud Servers Work?

In this article, we’ll deep into what cloud servers are, how they work, their types, benefits, and challenges. We’ll also cover factors to consider when choosing a cloud provider.

What Is a Cloud Server?

A cloud server is a pool of powerful virtual or dedicated physical computer resources. These resources are hosted in the public or private cloud infrastructure and can be accessed through the internet or a virtual private network (VPN.)

Despite their off-site locations, the servers in the cloud provide broadly the same functionality as the traditional on-premises physical servers. However, the cloud environment offers scalability, flexibility, and has a lower cost of entry, since it does not require upfront investments in physical infrastructure like an on-premises data center.

A typical cloud server combines high-performance components and systems in one or more locations via high-speed networks. These may be physically located in one or more machines or distributed across several networks. For example, a physical server cluster may provide the CPU and memory (RAM) while the storage is in another location or system, such as network-attached storage (NAS.)

A physical server cluster

While shared cloud server hosting is adequate for many organizations, it may not suit high-traffic applications or those dealing with sensitive data. So, those projects that require more resources or to be isolated from noisy neighbors find private cloud to be a better fit. Healthcare, banks, and other resource-intensive industries, such as gaming websites, video streaming, and research organizations, prefer private cloud servers where resources are physically isolated from all other users.

Types of Cloud Servers

Cloud servers can be categorized into three types, based on where they reside: public, private, and hybrid.

Public Cloud Server

Public cloud servers are computer resources hosted on a public cloud infrastructure, which means they are owned, managed, and maintained by a third-party service provider. The provider makes all resources available to the public via the internet. For example, Gcore offers bare metal servers, virtual machines, and Kubernetes containers.

Private Cloud Server

A private or dedicated cloud server is a cloud architecture where resources are only available to a single organization. The cloud provider installs the dedicated cloud server, such as Gcore’s Bare Metal, in a private region. Private servers provide better performance, control, and security, making them suitable for highly regulated industries, high-traffic websites, or running mission-critical or sensitive applications.

In-House Versus Third-Party Private Cloud Servers

Organizations can host private cloud servers either in-house or in a third-party cloud environment. In the latter case, the provider owns and manages the hardware and software resources, including the supporting infrastructure. The choice depends on objectives regarding control, convenience, cost, and flexibility. Third-party cloud hosting offers less control, but deploying and scaling are faster, simpler, and more cost-effective.

When the private cloud operates from the in-house data center, users access it over the local area network (LAN.) External users or remote offices can access the server over a private network such as a VPN or the organization’s intranet.

Local area network switch and cabling

Hybrid Cloud Server

Hybrid cloud servers are a mix of public cloud and on-premises or private infrastructure. Hybrid cloud offers the best performance, flexibility, availability, and scalability. A hybrid cloud server enables businesses to take advantage of the best of these two environments to achieve optimal outcomes regarding compliance, availability, and security. A typical configuration could involve an off-site public cloud server combined with an in-house or third-party-hosted private cloud server. An example of a hybrid cloud server is Gcore’s Virtual Private Cloud.

Read: What is a bare metal server? | How does it work?

How Does a Cloud Server Work?

Public and private cloud servers work like traditional on-premises physical servers. They provide processing power, run applications, store data, and perform other functions like running databases, mail servers, etc. However, unlike the in-house servers that run locally from the in-house data center, most cloud servers are hosted and managed by third-party cloud computing providers. They are only accessible over the internet or a virtual private network (VPN.)

Most public cloud servers are software-defined using a virtualization process that creates multiple virtual machines on a single physical server. Providers may use a hypervisor or other virtualization technology to abstract the CPU, memory, storage, and other server resources.

For shared public cloud servers, the virtualization technology pools the configured resources to create multiple cloud servers that function like physical machines, but are only accessible via the Internet. This allows customers to share the hosting server’s compute, memory, storage, and other resources. However, each can access a separate instance with the individual operating system. Clients on the same physical server can run different applications and store data independently.

Cloud computing service providers usually invest in, manage, and maintain a high-capacity infrastructure, benefiting from the economies of scale. Consequently, they can pass on these benefits to customers and offer reliable, high-performance servers at lower costs than clients would have spent deploying equivalent systems in their data centers.

Most cloud computing providers have self-service portals that potential customers use to create and deploy cloud servers in less than ten minutes. Automated systems enable the customers to choose predefined servers or configure custom CPU, storage, memory, and other resources that align with their specific requirements and budget.

Who Should Use Cloud Servers, and Why?

The cloud computing delivery model is suitable for all types and sizes of workloads. Since it allows deployment of powerful virtual machines and bare metal servers without the high upfront costs associated with an on-premises system. The flexibility and affordability of cloud’s pay-as-you-go pricing plans enable everyone—including those with limited budgets—to deploy cloud servers that meet their specific needs.

By contrast, setting up an equivalent in-house system would mean installing physical servers and adequate storage in the organization’s data center. This capital-intensive exercise would require significant time and skill to install, configure, and manage, making it impractical and unaffordable for those with inadequate resources.

To understand the problem users of on-premises servers face is to imagine an organization with seasonal traffic spikes—a good example is an e-commerce platform with traffic spikes during the sales season, such as Black Friday.

The company may invest in large local infrastructures with enough capacity to handle traffic spikes, but those expensive resources remain idle most of the time. A cloud server can reduce deployment costs, time, and overprovisioning of resources while improving efficiencies and revenues.

In this article, we’ll discover what a cloud server is, the different types of cloud servers, how a cloud server works, its many benefits and limited challenges, and why cloud is essential for most individuals and organizations.

What Are the Benefits of Cloud Servers?

The specific benefits of a cloud server vary depending on the organization’s objectives, workload, and type of server and plan. However, numerous common advantages to individuals and businesses are worth a closer look.

Low Deployment and Maintenance Costs

Customers can rent virtual cloud servers and storage at a much lower deployment and maintenance cost. This results in savings in CapEx, space, and running costs, such as highly skilled in-house staff, electricity, cooling, and other requirements for maintaining an on-premises system.

Highly Scalable and Flexible Computing Resources

Servers in the cloud are highly scalable, with an almost unlimited capacity to process resource-intensive workloads and store vast quantities of data. Most providers offer flexible plans, enabling users to choose the appropriate configuration and operating system based on workloads. Customers can quickly and easily create or remove the servers or resources depending on current needs, demand, and budget.

Cloud computing providers often invest in vast capacities so that customers can add resources to meet temporary high-demand workloads and then remove them when demand is low. When a configured cloud server capacity cannot handle a particular workload, the customer organization can scale by quickly adding resources. Cloud servers scale up or down to match the current workload quickly and easily.

Easy to Deploy and Use

Provisioning a cloud server is almost instant. It usually takes less than ten minutes to configure and start using a virtual instance. Installation, physical upgrades, and maintenance associated with on-premises infrastructure are all taken care of by the cloud computing provider.

Global Access and Universal Workload Capability

Cloud servers provide the same functions as on-premises servers. However, they are accessible by more users regardless of their location. An on-site server is only accessible by the users on the company’s network. However, a server in the cloud is accessible through the internet, hence the ability to serve a broader user base.

Internet connectivity enables cloud servers to handle global workloads. However, unexpected disruptions, instabilities, and network latency can degrade performance for users, especially in locations with less reliable internet connectivity. To overcome this, cloud service providers can create duplicate instances to process workloads on servers in different locations, thus ensuring faster connectivity, continuity, and better user experience.

Affordable Pricing Plans

Cloud service providers offer a wide variety of pricing plans based on the specifications. Customers can opt for annual, monthly, or pay-as-you-go payment plans. There are usually different commitment plans available, commonly for three or six months or one or two years. In general, cloud services are charged at an hourly rate. Customers pay for the resources used at the end of the month.

Besides the annual and monthly rates, Gcore offers per-hour billing on production-grade instances designed for heavy workloads, complex calculations, and other high CPU-intensive applications. Per-hour billing is suitable for customers who do not want to sign up for monthly or annual contracts. Typical rates range from $0.03496/hr for a 2vCPU, with 2GB RAM, to $2.9/hr for a 32vCPU, 128GB RAM, and 4GPU.

The overall cost of an instance depends on the allocated computing resources, operating system, and usage time, which are calculated from the moment use begins until it is completely stopped. Storage and network usage are charged separately.

Flexible Cloud Server Specifications

Cloud servers allow client customization to suit unique business needs and use cases. While the providers offer some cloud servers with standard specifications, they also allow the customization of various resources.

Highly Available and Reliable

Cloud servers are highly available, and most providers guarantee 99% uptime. As such, it is rare to experience frequent and lengthy downtimes. Unlike a traditional on-premises server that may experience downtime and take time to fix, cloud servers have several redundant components and faster recovery features, which make them highly available and reliable.

Top-Notch Security

Cloud computing providers use highly effective security solutions to protect the infrastructure and data, and have skilled IT security teams to ensure the servers’ constant protection. They also observe security best practices, such as updating the software and tools.

Read: What is Latency? | How to Reduce Latency

What Are the Challenges of Using Cloud Servers?

Before migrating to the cloud, it is essential to evaluate the challenges you are likely to experience. Issues to consider include performance, security reliability, availability, and more. These may differ from one deployment and cloud server type to the other. However, some common issues include cloud server performance issues, meeting regulations, cloud outages, a lack of direct server control, and incompatibility. Let’s review each one in depth.

Cloud Server Performance Issues

Shared or “logical” cloud servers reside in multi-tenant environments. You don’t have direct control over the VM, its location, or other cloud servers on the same physical machine. As such, excessive usage of resources such as network and storage by other tenants may degrade the performance of your server.

One way of overcoming performance degradation is to migrate your workloads to regions with fewer activities. But the ultimate solution is to move to a server with more capacity. The cost of such a server depends on the type and configuration, with dedicated servers being costlier than virtual servers.

For example, Gcore virtual servers that support Linux and Windows operating systems start at $4.99. The entry-level servers have 1vCPU, 512MB RAM, and 20GB disk, and you can adjust the specifications to match your workload. A dedicated server with an E-2236 Intel Xeon CPU, 32 GB RAM, and 2x 480 SSD disks starts at $206 per month and guarantees outstanding performance without exception, even for highly demanding workloads. A user who needs the full capacity of Gcore’s top-end cloud server could experience performance issues if they tried to put that same workload through an entry-level server, which is why it’s important to pick the option that matches your use case. Gcore’s experts can help with that.

Regulations, Compliance, and Governance

Data privacy rules, regulations, and other industry standards mean that cloud users can come up against challenging differences between geographical locations, which limit options for cloud servers. For example, if regulations or industry standards demand that the organization must store its data within its political and geographic boundaries, this means a limited choice of cloud servers.

Unexpected Cloud Server Outages

Cloud servers may experience unexpected outages due to a problem within the service provider’s infrastructure or as a result of internet issues. Since the servers are hosted at a physical distance, customers have no way to remedy outages and are reliant on the cloud provider for both information and a fix.

Such problems may include network issues or slow performance due to spikes in demand. Additionally, unexpected network issues could be beyond even the provider’s control. To safeguard against such a challenge, organizations may opt to keep the critical workloads in on-premises servers. Choosing a reliable cloud provider, such as Gcore, can help to limit such outages.

Limited Direct Control of the Server

The cloud provider manages the cloud infrastructure, including the type of hardware components and location. This leaves you unable to optimize the systems for the best performance, since you have no direct control of the server.

Incompatibility

If the cloud servers only support some of your operating systems, workloads, and applications, you may experience compatibility issues. Selecting a cloud provider and planning carefully upfront can help mitigate this problem but does not always account for future additional or changed needs.

Factors to Consider When Looking for a Cloud Server

The choice of a cloud server depends on a combination of factors, such as your workload, budget, server technology, and providers’ reputations. Below are the key issues to consider.

Type of Cloud Server to Deploy 

Virtual machines are budget friendly and easier to manage. However, performance issues are always a threat, especially when other tenants have a high demand for resources on the same physical machine. As such, they are suitable for less resource-intensive and non-critical workloads. If you want a third-party vendor-controlled virtualized layer, this is the right choice.

If you need more resources and your workload requires an entire system built from the ground up, then you need a bare metal server. Bare metal offers superior resources compared to VM, and other clients’ demands do not affect bare metal. However, bare metal is costlier than VM. You can host the bare metal server on-premises or on a third-party provider’s infrastructure.

Type of Server Virtualization Technology

Providers may use hardware or software virtualization technologies to subdivide a physical server into several virtual machines. In a logical cloud server, virtualization abstracts resources such as the CPU, storage, memory, and network from the physical hardware, so that services and applications can function without depending so much on the hardware.

Besides hypervisor-based virtualization, providers can use other technologies such as OS-level virtualization, hardware virtualization, hardware-assisted virtualization, etc. It’s important to check whether the virtualization technology is suitable for your operating system, applications, and workloads.

Cloud Server Security

Securing your users, workloads, and data on the cloud server should be a priority. This prevents or reduces risks of cyberattacks, DDoS attacks, ransomware, and other security issues that could compromise your systems and lead to performance degradation and even financial and reputation losses for your organization.

Unlike an on-premises data center, where you can protect your digital assets using perimeter security and other technologies, you have less control over the cloud-based server. Instead, you have to rely more on the security measures of the cloud service provider.

It is, therefore, vital to evaluate their technologies and ensure the provider can adequately secure your cloud server. However, cloud security is a shared responsibility between the provider and customers. As such, you still need to deploy various security measures, policies, and practices to protect your users, applications, and data.

Cost-Effective Cloud Services

Good value doesn’t always mean picking the cheapest option available. Select a cloud service that delivers the best features at a fair price. Customizing and optimizing the server comes with added costs, but ensures better performance and security of your workloads and data. It’s also worth paying attention to the available pricing plans, their flexibility, and their compensation policy if you decide to move away before the contract ends.

Support for Private Cloud Server

The public cloud servers offer some level of cost-savings and convenience. However, compliance and data governance requirements may require deploying a private cloud server with resources fenced off from other users. In such a case, you need a provider who supports the private or hybrid cloud server system while offering favorable customization options.

Numerous and Diverse Cloud Features

It is vital to look for a provider who offers a service that meets your current and future needs. Choose a provider offering features such as automatic scaling, load balancing, easy server upgrades, backups, and disaster recovery so that you can feel confident in your efficiency, performance, and continuity.

Reliable and Prompt Support

A good provider must offer reliable support to minimize downtimes. Ideally, look for a provider that provides 24/7 support. Check the reviews to see what other customers have experienced when it comes to support from your potential provider.

Compliance with Regulatory Standards

If you are in an industry that requires compliance with regulations such as GDPR, CCPA, ISO/EIC 27 001, HIPAA, PCI-DSS, and others, look for a compliant provider.

Seamless Migration to the Cloud

Whether switching from an on-premises data center or another cloud provider, your new cloud service provider should assist you in the migration process. Such support ensures a smooth transition and minimal interruption or downtime.

Cloud Server Customization Options

If you anticipate that your business will grow rapidly or experience temporary demand spikes, look for a scalable and customizable service where you can add or remove resources to meet current and future needs. Auto-scaling features enable the automatic addition or removal of server resources, ensuring that you can easily and quickly process any workload while only paying for the computing resources that you actually use.

Should You Switch to a Cloud Server?

Most individuals and organizations benefit from switching to the cloud because it offers cost-effective, scalable, and reliable on-demand computing services without an upfront cost.

However, public cloud is not always the best choice, especially for use cases involving unusually sensitive data (such as healthcare or finance companies) or extreme performance computing requirements. For sensitive workloads, Gcore offers highly secure virtual cloud servers with Intel SGX support. The technology provides extra security such that only authorized users can access the encrypted data.

An assessment of your specific needs and circumstances is important before deciding to switch to cloud, but in almost all cases, it’s a move worth making.

Examples of Cloud Servers

Most organizations and individuals are using cloud servers in one way or another. Some use cases include email, file storage, and web applications. The cloud servers power major platforms and diverse application types, such as Software as a Service (SaaS,) Infrastructure as a Service (IaaS,) and Platform as a Service (PaaS.) Let’s take a look at two examples: Dropbox and Gcore.

Dropbox is a popular web-based file storage, sharing, and synchronization platform. It allows users to store files from their computers and access them from anywhere over the internet and supports real-time collaboration.

Gcore provides customizable cloud computing, CDN, infrastructure, security, and other services to meet computing needs across all industries. These include:

Conclusion

Cloud servers provide customers with a cost-effective solution to meet varying workloads on demand. We have learned about the cloud-based server, how it works, its benefits, and the type to deploy. The next step is to look for a reliable provider with the capacity and ability to meet your needs, which means balancing a number of complex factors.

Choosing the right cloud service provider is critical to achieving your organization’s objectives and success. It’s vital to opt for a service that meets both your current and future needs. At Gcore, we offer high-performance, scalable, affordable cloud servers to support customers and industries with different needs and workloads.

To learn more about Gcore, check out our cloud server products or talk to one of our experts for a free consultation on deploying your cloud servers and improving your operations.

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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

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