How ScienceSoft Optimizes Infrastructure Management Costs
62% of ScienceSoft's revenue comes from engagements lasting over two years. How do we earn our clients' loyalty? We help them spot optimization opportunities and achieve up to 150% IT cost savings compared to what they would get with in-house support.
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Is Your MSP Doing Their Best for Cost Optimization?
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According to CloudBolt Industry Insights (300 respondents, including VPs, CXOs, and directors of 1,000+ employee companies), 80% of businesses want to change their MSP. 60% of those thinking of the change blame their MSPs for the lack of proper cost optimization, and 41% report the lack of visibility in their cloud spending. Here is how we tackle both challenges: |
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Strategic process optimization
IT teams from ScienceSoft go beyond following SOPs and always look for process optimization opportunities. Suppose we notice an inflow of repeating L1 support tickets. Instead of involving more support agents, we first analyze the root cause of the upsurge. A solution could be building a self-service knowledge base or implementing an AI-powered virtual assistant that can handle 80% of standard user requests, ultimately reducing support costs by 30%.
Transparent infrastructure costs
We ensure complete transparency of infrastructure expenses. Our engineers use dedicated tools (e.g., AWS Cost Explorer, Azure Cost Management and Billing, Google Cloud Console) to prepare a detailed cloud cost breakdown, identify what drains your IT budget, and help you determine the best way to reduce cloud consumption. Additionally, we can set up alerts to notify you when cloud expenses exceed a predefined threshold.
How ScienceSoft Reduces Infrastructure Costs
Below, ScienceSoft's infrastructure engineers share some of the best practices we use to optimize cloud resource consumption. We are focusing on the cloud as it is the most popular deployment option nowadays.
Rightsizing cloud resources
Spotting unused and underutilized resources
We rely on performance monitoring tools (e.g., Amazon CloudWatch, Azure Monitor) and cloud spending visualization tools to identify unused and underutilized cloud resources. When we detect instances that did not see more than 40% CPU and memory usage in the past month, we can usually downsize them by half and save up to $600 per instance every month.
Autoscaling
ScienceSoft applies autoscaling functionality to infrastructures with fluctuating workloads. It automatically terminates unused instances during lower demand periods and adds instances during peak demand. This technique helps significantly reduce cloud costs during low-traffic periods while maintaining stable performance and high availability under high load.
Identifying root causes of performance issues
Our engineers inspect the resources working at peak capacity and find the real causes of performance constraints before adding more cloud instances. For example, when a non-profit organization turned to ScienceSoft with performance issues in its Azure-based apps, we did not unquestioningly increase the storage capacity. Instead, we optimized two databases. This not only solved the performance issues but resulted in a 4x decrease in application maintenance costs.
Tagging instances
Often, unused cloud servers are deleted, but the associated storage instances remain active. To avoid it, ScienceSoft establishes maximum visibility in cloud environments and tags all instances, labeling them to specify their environment, owner, and purpose. Binding connected cloud resources by tagging also helps create transparent cost allocation reports. This way, we can provide our client with a clear cloud cost breakdown for each particular app or infrastructure component.
Applying optimal cloud pricing models
With so many cloud instance types on the market, it may be challenging to find the best deal. Our Microsoft- and AWS-certified engineers stay up to date with the latest cloud offerings to choose the right combo of instances and ensure maximum cost efficiency for your IT infrastructure. See how different cloud pricing models influence the cost:
| On-demand instances | Spot instances | Reserved instances | Saving plans |
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On-demand instances
You can buy instances on-demand, paying per hour of actual usage. This way, you do not commit long-term, but it is a more expensive option than the discounted pricing models. Also, there may be no on-demand capacity during peak times (e.g., Black Friday), limiting your ability to add more instances when you need them most. |
Spot instances
Spot (i.e., currently not used by anyone) instances are 90% cheaper than on-demand ones, but the cloud provider can terminate them at any time and without much warning. We use them only for fault-tolerant and stateless apps whose data is stored on several instances. This way, your app won't be affected by a sudden instance termination. |
Reserved instances
Reserved instances are up to 75% cheaper than on-demand instances. We recommend them when your workloads have predictable usage patterns without big fluctuations, as you essentially pre-purchase a specific instance type for 1–3 years. This is how we helped a contact center software provider optimize cloud consumption. |
Saving plans
Our engineers use saving plans for fluctuating workloads, as there is no point in committing to one instance type. Instead, you commit to a specific amount of cloud usage in $/hr (or $/s) for 1–3 years, but it is not tied to a particular instance type. This way, you can achieve up to 73% cost savings compared to on-demand instances. |
Introducing DevOps
Enabling Infrastructure as Code (IaC)
In managing development infrastructures, our DevOps engineers adhere to the IaC approach to introduce changes quickly and securely. Additionally, the approach facilitates automated security, performance, and consumption checks.
Containerization
Our engineers containerize software with all its dependencies to simplify its maintenance and modernization. Compared to virtual machines, containers use less computing power as they share the operating system kernel. By avoiding the need to duplicate the operating system for each container and run several virtual machines, we decrease infrastructure costs and reduce the required storage and memory.