Networking is the practice of connecting two or more devices together so that they can communicate with each other and exchange data. While the internet is the largest and most well-known network example, it consists of various smaller groups and divisions of networks called subnets.
As a rule, subnets belong mostly to large organizations. Subnets can also exist internally within organizations to separate users according to different departments’ network needs. They also exist in homes, hotels, and other settings, though these internal subnets may not be directly exposed to the internet. Connections can be established via various transmission mediums, such as network cables or other wireless technologies.
In this article, you’ll learn and understand at a basic level how subnetting works and see how beneficial it is to know about subnetting and implementing subnets. You’ll also learn about the subnet mask and CIDR notations, and you’ll be introduced to common subnet sizes, network classes, and how to easily calculate subnets for a specific number of hosts on your network.
What Is a Subnet and How It Connects with IP and IP addresses
A subnet is a logical subdivision of an IP network. It allows a larger network to be divided into smaller, more manageable subnetworks, each with its own unique IP address range. Internet Protocol (IP) is a set of rules and procedures that govern how devices identify themselves and exchange data with other devices on a network. One of the most important components of the internet Protocol is the IP address.
An IP address is a unique identifier assigned to each device on a network and used to route data between them. An IP address is made up of four sections separated by dots, each with a decimal number ranging from 0 to 255. These four sections are also called octets, and when you combine all of them, you get a total of 32 bits. So, for example, an IP address could look like this: ‘192.168.100.10’.
There are two versions of IP currently in use: IPv4 and IPv6. The previous example uses IPv4, which is the older and more widely used version, while IPv6 is newer and designed to address the limitations of IPv4. There’s more about this later in the article, but for now, this article will focus on IPv4.
How Does Subnetting Work?
In many medium- to large-sized network environments, it’s common to split the network into smaller subnetworks for various reasons. This process is known as subnetting, and it’s used to improve network performance, manage resources more easily, enforce security policies, and monitor network traffic, among other benefits.
While the subject of subnetting can be technical and mathematical, the following analogy should help you understand the basic concept.
Imagine you have a large field with many people playing different sports, such as football, basketball, and baseball. Without any form of organization, it can be chaotic and difficult for players to focus on their game. By creating separate areas on the field for each sport, such as a football field, a basketball court, and a baseball diamond, players can focus on their game without interference from other sports.
Similarly, subnetting can be considered as dividing the field into smaller sections, each with its own set of boundaries and rules. Subnetting allows a large network to be divided into smaller segments, each with its own IP address range, allowing a specific number of devices to communicate with each other more efficiently in that segment.
Just as the lines and boundaries marking a field take up physical space, subnetting requires the use of a portion of the address space to delineate and organize the network. In each subnet, the first and last IP addresses are reserved for the network address and the broadcast address, while everything in between represents the range of IP addresses that can be assigned to devices within that subnet (*ie*, the playing area).
Benefits of Subnetting
Using subnetting in your network can offer several important benefits. Here are a few of the most significant:
- Improved network performance: Subnetting can help reduce network congestion and improve network performance by limiting the number of devices that communicate with each other in a single broadcast domain. This can prevent network traffic from overwhelming a network segment and ensure that devices communicate more efficiently.
- Enhanced network security: Limiting the number of devices that can communicate with each other in a single broadcast domain also improves network security. Access control lists and security policies implemented using firewalls and other security control systems can be tailored to control the flow of traffic from devices within specific subnets. This also provides better visibility, which makes it easier to monitor network activity and detect and respond to security threats.
- Optimized IP address utilization: Subnetting helps optimize IP address utilization by breaking a large network into smaller segments, each with its own range of IP addresses. This helps conserve your organization’s network’s public IP addresses and reduces the need to purchase additional IP addresses.
- Facilitated network management: Subnetting can make network management easier by breaking down your large network into smaller, more manageable segments. This allows you to easily configure and manage your network devices, assign your network resources, monitor your network activity, and troubleshoot any network issues that may arise.
Introduction to Subnet Masks
Below we will take a closer look at the constituent components of subnets. An IP address has two parts: the network portion and the host portion, which respectively identify the network (network address) and the specific device within that network.
The subnet mask is a set of 32-bit decimal numbers that determines how many bits of the IP address are used for the network and how many bits are used for the host. In practice, a subnet mask is usually expressed in dotted decimal notation, like an IP address.
To understand how subnet masks work, it helps to think about IP addresses and subnet masks in binary notation. An IP address is a 32-bit number represented as a sequence of 0s and 1s. A subnet mask is also a 32-bit number, with a sequence of 1s followed by a sequence of 0s.
The sequence of 1s in the subnet mask indicates which bits of the IP address belong to the network portion, while the sequence of 0s indicates which bits belong to the host portion.
As seen in the example above, a subnet mask of ‘255.255.255.0’ indicates that the first three octets with eight 1s (8 bits) each and a total of 24 bits represent the portion of the IP address that is reserved for the network, and the last octet (8 bits) is reserved for the host portion.
Subnet Notations
There are two main ways you can write or define a subnet to determine the network and host segments of an IP address: subnet mask notation and classless inter-domain routing (CIDR) notation.
Subnet Mask Notation
In this notation, the subnet mask is written using decimal numbers in four parts separated by periods, and as seen earlier, each part is called an octet. A subnet mask of ‘255.255.255.0’ indicates that the first 24 bits of the IP address are reserved for the network portion, and the remaining 8 bits are reserved for the host portion.
To express a subnet using subnet mask notation, you simply add the subnet mask to the end of the IP address, separated by a space. For example, ‘192.168.100.10 255.255.255.0’ represents a subnet with an IP address of ‘192.168.100.0’ and a subnet mask of ‘255.255.255.0’. Then, the network portion of the IP address is ‘192.168.100’, and the host portion is ‘10’, exactly as seen in the previous illustration.
CIDR Notation
CIDR notation is a compact way of indicating a subnet by using a single number to represent the subnet mask. In CIDR notation, a subnet is indicated by adding a forward slash (‘/’) and a number to the end of the IP address.
The number after the slash indicates the number of bits that are reserved for the network portion of the address. For example, ‘192.168.100.0/24’ means the first 24 bits of the IP address are for the network portion, automatically leaving the last 8 bits for the host. This is equivalent to ‘192.168.100.0’ with a subnet mask of ‘255.255.255.0’.
CIDR notation allows for a more concise representation of subnets, especially when dealing with large networks. It also provides a more explicit indication of how many bits of the IP address are used for the network portion of the subnet, which can save you the trouble of having to calculate it yourself, as you might need to do with subnet mask notation. These benefits have led to CIDR notation becoming increasingly popular.
Common Subnet Classes and Sizes
In the early days of the internet, IP addresses and subnets were divided into classes based on their initial bits, with each class having a fixed number of bits reserved for the network portion and the host portion of the address.
This made it easier to memorize subnet masks and identify them based on the leading numbers of the IP address. The three most commonly used classes are Class A, Class B, and Class C, which correspond to the most common subnet sizes of ‘/8’, ‘/16’, and ‘/24’, respectively.
Here’s a brief explanation of how they differ:
- Class A: Class A networks use the first 8 bits of the IP address for the network portion and the remaining 24 bits for the host portion, as in ‘255.0.0.0’. This allows for a small number of networks, each with a large number of hosts. The Class A address range spans from ‘0.0.0.0’ to ‘127.255.255.255’.
- Class B: Class B networks use the first 16 bits of the IP address for the network portion and the remaining 16 bits for the host portion, as in ‘255.255.0.0’. This allows for a moderate number of networks, each with a moderate number of hosts. The Class B address range spans from ‘128.0.0.0’ to ‘191.255.255.255’.
- Class C: Class C networks use the first 24 bits of the IP address for the network portion and the remaining 8 bits for the host portion, as in ‘255.255.255.0’. This allows for a large number of networks, each with a small number of hosts. The Class C address range spans from ‘192.0.0.0’ to ‘223.255.255.255’.
You should keep in mind that the use of classful addressing has largely been replaced by classless addressing, as mentioned. However, the terms Class A, Class B, and Class C are still commonly used to refer to the traditional subnet sizes of ‘/8’, ‘/16’, and ‘/24’, respectively.
Determining the Number of Hosts on a Subnet
Calculating the number of possible hosts on a subnet is essential in order to ensure that the network can support the required number of devices while avoiding potential performance issues. To calculate the number of possible hosts, you need to know the subnet mask for the network and use that to determine the number of host bits in the IP address.
You can obtain this by subtracting the total number of network bits from 32. Once you have that, you can determine the number of hosts using the following formula:
Number of hosts = 2^(number of host bits)-2In this formula, the number of host bits is equal to the total number of bits in the host portion of the subnet mask. The “-2” at the end is because the first address in the subnet is reserved for the network address and the last address is reserved for the broadcast address, as explained earlier in the article.
You can use a subnet mask of ‘255.255.255.0’ as an example. With this subnet mask, the first 24 bits are used for the network portion of the address, and the remaining 8 bits are used for the host portion. This means that you can have 2^8 (256) possible values in the host portion of the address.
However, as you’ve learned, the first and last addresses in the subnet are reserved. In this case, ‘192.168.1.0’ is the network address, and ‘192.168.1.255’ is the broadcast address. Therefore, the actual number of hosts or devices that can be assigned to this subnet is 256 − 2 = 254.
While the example given earlier for calculating subnet masks may seem easy, it is only the most basic one. In reality, there are much more complex subnet masks that you may need to calculate, and doing so manually can be prone to errors. So, it’s important to take care and be precise to obtain accurate results.
Thankfully, for more complex calculations, online tools like Gcore’s IPv4 subnet calculator can help you perform these calculations more precisely and guarantee accurate results every time.
What Is IPv6, and How Does It Compare to IPv4?
IPv6 (Internet Protocol version 6) is the most recent version of the Internet Protocol. It was developed as a replacement for IPv4, which has been the primary protocol over the last few decades.
IPv6 uses 128-bit addresses, which results in a much larger address space compared to IPv4’s 32-bit addresses. This allows for a virtually unlimited number of devices to be connected to the internet.
IPv6 addresses are expressed in hexadecimal notation and are typically written as eight groups of four hexadecimal digits, separated by colons. This allows for a more efficient use of address space compared to the decimal notation used in IPv4, which is being exhausted.
IPv6 also includes built-in security features such as IPsec, improved support for quality of service (QoS), and some new features and enhancements that are not present in IPv4.
Despite the advantages of IPv6, its adoption has been slow. This is because IPv4 is already widely used and deeply embedded in the infrastructure of the internet, so many networks and devices still use it. Additionally, IPv6 is not backward compatible with IPv4, and there are no mechanisms to enable devices to communicate between the two protocol versions. This is a major issue that needs to be resolved before IPv6 can become the dominant protocol used on the internet.
Conclusion
Subnets and how subnetting works are topics that are quite complicated, but with the help of this article, you should now have a solid grounding in the basics. You learned what subnets are, how to identify networks and hosts on a network using IP addresses and subnet masks, and how to express subnets and calculate the number of hosts on a subnet. Additionally, you were introduced to IPv6 and its advantages over IPv4 in terms of available IPs for devices and security features.
If you plan to create a new network and set up subnets within an existing one, Gcore Cloud will serve you well. You can connect different Cloud products such as virtual instances, bare metal servers, AI clusters, and others, in one single cloud infrastructure by creating networks and subnetworks. You can also make your private subnetworks routable, providing access to the internet. Additionally, you can enable DHCP to simplify the process of assigning IP addresses to devices and add custom DNS servers for better name resolution.
Written by Rexford A. Nyarko
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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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