With a balance of computing, memory, and storage power. Day-to-day computing at a low cost for web apps, blogs, microservices, virtual desktops, small-medium databases, and back-office operations. Cost-effective for non-resource intensive applications with periodic demand bursts. High connection speed per server amid superior SSD performance. Know More
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Cloud Servers Price Weighted Performance (Higher is Better)
VPSBenchmarks is an independent benchmarking platform. They found that LayerStack provides the highest value per dollar spent among when compared to Google Cloud, Microsoft Azure, AWS and Linode. Read the Report
Plans and Pricing FAQ
How am I billed for my Cloud Servers?
We charge your subscription fee on pre-payment basis. Most LayerStack plans can be billed by monthly, quarterly, or annually. The longer payment terms you commit, the cheaper the monthly average. The details are always kept up-to-date on our Plans & Pricing Page.
How to back up my Cloud Servers?
Pricing for backups is 25% of the cost of the cloud server per month. LayerStack automated backups will be run at regular intervals and back up your data from cloud servers at a single point in time that assigned by the system, allowing you to protect and recover your important business data. You may visit our product documentation for more details. Apart from this, we always put all of the data on a high availability RAID array for redundancy.
Is there any limit on the data transfer?
It depends, most of our service plans implements unlimited data transfer policy. Our automated system may limit your bandwidth, if traffic usage exceeds the limitation amount on a monthly basis. This limitation will be reset every month. The details are always kept up-to-date on our Plans & Pricing Page.
What happens when I reach data transfer limit?
When monthly data usage reaches that data transfer limit mentioned on each service plan, cloud servers service continues without data capping, but the connection speed will be throttled, and the connection experience may be affected. You can check your data usage in LayerPanel 2.
What CPU and Memory are selected for Standard Server, High-Frequency CPU Server and Memory Optimized Server?
All LayerStack’s server use DDR4 ECC RAM to ensure the consistent performance. As for CPU, Standard servers are powered by the 2nd Gen AMD EPYC 64 Core CPU or Intel® Xeon® Scalable Processors with a maximum turbo boost of 3.3GHz. High-Frequency CPU servers and Memory Optimized servers are powered by AMD EPYC 7742, the fastest architecture available in the LayerStack product lineup to meet complex computing needs.
What is the Bandwidth Fair Usage Policies?
LayerStack uses a Fair Use Policy (FUP) for its Cloud Servers to control the amount of data traffic. The FUP aims to prevent overload of the network, abuse and inconvenience to other users.
How do I turn off auto-renewal?
LayerStack subscriptions that paid by credit card are set to auto-renew so that you don't experience any interruption in accessing your products or services. To disable auto-renewal, please contact our customer service team.
Why not have free trials?
Because it helps to keep our customers’ servers and network to the best performance from service abuses.
Which Server is installed with dedicated CPU cores?
Both Dedicated CPU servers and Memory Optimized servers are using dedicated CPU cores to handle clients’ most demanding workloads and applications.User can enjoy the full power of the CPU with the absence of other clients competing for computing power.
What is the difference between Standard Cloud Server, Memory Optimized Cloud Server and High Frequency CPU Cloud Server?
Standard Cloud Server balances the compute, memory, and network resources. Suitable for most general-purpose workloads like virtual desktops, small databases and web management.
Memory Optimized Server is supported with optimized memory specifically for workloads with high memory requirements such as large-scale databases, memory caching and big data processing.
High Frequency CPU Servers featuring top-class CPU to cope with CPU-intensive workloads and applications such as machine learning, data analytics and video coding.