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Cloud Cost Optimization

By eliminating the traditional barriers to computing resources, the cloud has created a level playing field to compete regardless of organizational size or clout. But organizations that move to the cloud often find themselves grappling with cost inefficiencies created by unrestrained spending. Optimizing cloud costs is, without a doubt, a top priority as it directly impacts the bottom line. As your cloud partner, we help you identify and address inefficiencies and maximize the benefit of the cloud.

Cloud Cost Management Challenges

Studies point out that 35% of cloud expenses are of wasteful nature. Eliminating this waste is an uphill task for many organizations.

Diverse Products

More than a hundred instance types are available today with varying system and network configurations and prices. It can be difficult for organizations to make the most economical choice from this perplexing array.

Idle Resources

Users provision resources on demand but don’t remember to decommission them when they are no longer required. Similarly, they allow instances to run 24x7 rather than when they are needed, leading to revenue leakage.


Many users have trouble determining the capacity of the instance they need and this leads to overprovisioning. Some may provision the wrong type of cloud server. All this results in wasteful expenditure.

Inefficient Storage

Finding the right storage is problematic when a plethora of vendors crowd the market with diverse features and pricing models. The most cost-efficient one is not easily apparent.

Dynamic Vs Static Allocation

Dynamic Vs Static Allocation

For workloads that demand resources for select periods of time (such as batch processing of data) spot instances created at the time of demand are a more economical option. Opportunistically turning instances on and off based on actual usage is a great way to save up to 75% on costs. Automated scheduling makes this more efficient. Cloud platforms can be configured to auto-scale resources based on a set of rules.

Re-architecting the application to leverage spot instances, which can be terminated anytime, is also a cost-cutting measure. However, spot instances are not suitable for stateful applications, databases, etc.

For predictable or unchanging workloads, Reserved Instances (RI), with their guaranteed capacity, are a better choice. RI comes with significant discounts in return for a long-term commitment. It is just as important though to ensure that RI discounts are always availed of and not wasted.

Right Computational Resource

Use of the Right Computational Resource

The selection of GPU over CPU is another decision that has cost implications. GPUs are preferred over CPUs if the applications involve large computations (such as machine learning and 3D rendering). Appropriate CPU or GPU instances can be created depending on the requirement.

Diverse offerings such as Amazon EC2 CPU instance, dedicated GPU instances such as G3 and the less expensive Elastic Graphics can add GPU to existing CPU instances. For machine learning, there are higher-value EC2 - P2 instances with support for parallelism and the less expensive Elastic Inference that can be attached to a regular EC2 instance.

Selection of Right Storage

Selection of Right Storage

Moving data in and out of storage is expensive, so the choice of storage must be decided at the outset and must be guided by your requirements. Amazon S3 Standard is an object store for data that needs to be retrieved often. Amazon S3 Standard - IA and related variants are better for backing up data that is not likely to be accessed for a very long time. For compliance backups and archival storage, Amazon Glacier is a cheaper choice. You can also reduce the costs of data access by selectively retrieving objects from S3 using SQL expressions, thereby reducing transfer and compute costs.

Performance requirements can be better met by storage options such as block storage (for example, Amazon Elastic Block Store) or scalable Linux file system (for example, Amazon Elastic File System). There are other specialized systems for requirements such as high-performance computing and Windows native applications with trade-offs among throughput, retrieval time, and cost.

If access becomes infrequent over time, you can also make use of lifecycle policies to move data from one storage type to another. By implementing lifecycle policies and automatic rules to delete or move data once its lifetime has been served, you can get rid of unwanted data and storage costs. Dated snapshot is a common source of storage wastage, which can be eliminated using the appropriate policy and lifecycle management tool.

Use of Lightweight Alternatives

Use of Lightweight Alternatives


Lambda, with its server-less nature, is a cost-saving option that can be adopted for workloads that are time-limited, low-memory, and stateless. Since failed Lambda executions and retries are expensive, attention needs to be paid to how they are implemented.


Containers are a lightweight alternative to isolated application execution. By running multiple containers on a dedicated or virtual machine, better utilization of servers can be achieved. The key is, again, correct implementation, such as ensuring the use of lightweight OS images.

Container Host

Containers can be run on virtual machine instances like EC2, where you pay for the capacity of instance and not how much of the instance is used. Specialized platforms like Fargate allow you to deploy the container in a dedicated container host environment.

With Fargate, billing is based on CPU and memory utilization per second. Simple analysis shows that for instance utilization of 50%, Fargate offers 10-20% cost saving. An EC2-based container host may be best for a consistent workload, but for an inconsistent workload like batch processing, Fargate is the better solution.

Management of Licensing Costs

Management of Licensing Costs

The licensing cost of the base operating system can spike the total cost of running an application so it needs to be managed carefully. An instance of a Linux system like Debian or Ubuntu on a virtual machine costs less than Windows or a supported RHEL instance.

To run .NET applications, you need to pay for Windows license. If they are migrated to .net core framework, they can also run on Linux instances that have low or no license fees. Thus migration to .net core can be a cost reduction strategy. If you have already purchased Windows license, you might want to consider taking dedicated hosts to take advantage of the existing licenses.

Avoiding dependence on proprietary databases like Oracle and MS SQL can save money too. Using tools like AWS Schema Conversion, your data can be moved to a less expensive database. Even if you must use a proprietary system, a careful selection of the version, such as MS SQL Web / Standard / Enterprise, can help you realize some cost savings.

Leveraging Single Cloud

Leveraging Single Cloud

Several imperatives push enterprises towards a multi-cloud model—varying department needs, pricing schemes, SLAs, security, and so on. But managing the diverse products from multiple vendors requires specialized tools and additional training, which makes it cost-intensive. A single cloud, on the other hand, eliminates the administrative hassle of switching between platforms and paying for network traffic between clouds.

The chances of getting volume discounts increase when you deploy all your resources on a single cloud. While single cloud is advisable for many reasons, there can be limits to such benefits. The right choice can be made only after careful consideration of your application needs and environment.

Cloud Cost Optimization Through Governance

It is easy to lose sight of the various cloud deployments as they grow in number and complexity. As idle instances accumulate, they lead to what is called virtualization sprawl (in case of virtual machines), with adverse effects on cloud spend, performance, security, etc. By having cloud cost governance policies and mechanisms in place, you can exercise a level of control over your cloud journey and prevent cost overruns.

To rule out conflicting departmental priorities from derailing cost optimization efforts, it is important to define the various roles involved and make cost optimization a shared responsibility. Systems that monitor the use of cloud resources and expenses incurred should also be governed by policy and linked to the respective projects.

Governance policies apply not only to resource allocation but also their life cycle management. A regular audit helps to ensure that the policy is doing its job in curbing unnecessary expenditure.

The FinOps Model of Operations

Computational resources no longer constitute one-time capital building decisions. As computing resource allocation becomes dynamic, so will budget allocation. In such a dynamic environment, interdepartmental collaboration—between development, operations, and finance—is pivotal to keep the pace of innovation going without hurting the financials.

Some of the ways that enterprises can bring the FinOps model into practice include allocating costs to each project or team rather than billing them together. Each team or project should have real-time visibility into cost, so they can make better financial decisions. Resource tagging can help in project-level accounting of resources. The important thing to remember is that cost optimization is an ongoing process and it takes continuous and concerted efforts from the team to achieve this goal.

Our Cloud Cost Optimization Services

We recognize that the cloud is the key driver of your innovation and growth. Our focus as a cloud partner is on getting you the best of the cloud without letting overspend break the financial edifice of your operations.

Whether you are migrating to the cloud or looking to expand your services, we make sure that the process is informed by sound technological strategies and governance policies. Be it in our selection of instances or the adaptation based on your needs, you can count on the rich technological and business experience that puts us head and shoulders above the rest.

End the runaway spending on cloud