04 - Terraform license change sparks move to open source AWS CDK for AWS Infrastructure_

Terraform license change sparks move to open source AWS CDK for AWS Infrastructure.



In a move that has sent ripples across the tech industry, HashiCorp, recently announced a significant shift in its licensing model for Terraform, a popular open-source infrastructure as code (IaC) tool. After approximately nine years under the Mozilla Public License v2 (MPL v2), Terraform will now operate under the non-open source Business Source License (BSL) v1.1. This unexpected transition raises important questions and considerations for companies leveraging Terraform, especially those using AWS.

Terraform has been a staple tool for many developers, enabling them to define and provide data center infrastructure using a declarative configuration language. Its versatility across various cloud providers made it a go-to choice for many. However, with this licensing change, the way organizations use Terraform might undergo a considerable transformation.

Implications for AWS Users and the Shift to Cloud Development Kit (CDK)

For businesses and developers focused on AWS, this change by HashiCorp presents an opportunity to evaluate AWS’s own Cloud Development Kit (CDK). The AWS CDK is an open-source software development framework for defining cloud infrastructure in code and provisioning it through AWS CloudFormation. It provides a high level of control and customization, specifically optimized for AWS services.

As a CIO or CTO selecting an Infrastructure as Code (IaC) tool for your organization, this licensing change may prompt reconsideration. With the importance of mitigating risk in tool selection, the appeal of open-source alternatives without the complexities of licensing issues becomes increasingly clear. This shift could significantly influence the decision towards truly open-source tools like AWS CDK over Terraform for streamlined, hassle-free IaC management especially if you are already using AWS as your cloud provider.

Why CloudKitect Leverages AWS CDK

CloudKitect, a provider of cloud solutions, has strategically chosen to build its products using AWS CDK. This decision is rooted in several key advantages:

  • Optimization for AWS: AWS CDK is inherently designed for AWS cloud services, ensuring seamless integration and optimization. This means that for companies heavily invested in the AWS ecosystem, CDK provides a more streamlined and efficient way to manage cloud resources.
  • Control and Customization: AWS CDK offers a high degree of control, allowing developers to define their cloud resources in familiar programming languages. This aligns well with CloudKitect’s commitment to providing customizable solutions that meet the specific needs of their clients.
  • Enhanced Security and Compliance: Given AWS’s stringent security protocols, using CDK infrastructures can be easily secured and tested to be compliant with various security standards, a critical consideration for enterprises.
  • Future-Proofing: By aligning closely with AWS’s own tools, CloudKitect positions itself to quickly adapt to future AWS innovations and updates, ensuring its products remain at the cutting edge.


HashiCorp’s shift in Terraform’s licensing model is a pivotal moment that prompts a reassessment of the tools used for cloud infrastructure management. For AWS-centric organizations and developers, AWS CDK emerges as a robust alternative, offering specific advantages in terms of optimization, customization, and security. CloudKitect’s adoption of AWS CDK for its product development is a testament to the kit’s capabilities and alignment with future cloud infrastructure trends. This strategic move may well signal a broader industry shift towards more specialized, provider-centric infrastructure as code tools.  If you would like us to evaluate your existing infrastructure, schedule time with one of our AWS cloud experts today.

06 - APIs Unchained_ Embracing the Serverless Cloud Revolution

APIs Unchained: Embracing the Serverless Cloud Revolution



In the ever-evolving landscape of API development, the demand for efficient, scalable, and cost-effective APIs has never been higher. One remarkable innovation that has been making waves is the use of serverless technology to unchain APIs. In this blog post, we will explore how serverless technology is transforming API development, providing businesses with newfound agility and eliminating the scalability constraints associated with server-based API resources

The API Integration Challenge

APIs (Application Programming Interfaces) are the lifeblood of modern software systems. They enable applications to communicate with each other, share data, and offer functionalities over HTTP protocol. However, running APIs to satisfy the ever increasing demands of API clients can be a complex task. Traditionally, organizations had to manage servers and infrastructure to facilitate APIs. This required substantial time, effort, and cost, often leading to scalability and maintenance challenges.

Enter Serverless Technology

Serverless technology, often associated with Functions as a Service (FaaS) platforms like AWS Lambda, Google Cloud Functions, and Azure Functions, has revolutionized the way applications are built and integrated. At its core, serverless computing eliminates the need for developers to worry about server management, infrastructure provisioning, and scaling. Instead, developers focus solely on writing code in the form of functions that run in response to events. Doing so offers many benefits over traditional platforms used to power the APIs, some of them are,

1. Cost Efficiency

Serverless technology follows a “pay-as-you-go” model, meaning you are billed only for the computational resources used during code execution. This eliminates the costs associated with maintaining idle servers.

2. Scalability

Serverless platforms automatically scale functions in response to increased workloads. Your APIs can handle thousands of requests without any manual intervention, hence APIs powered by Serverless technology are unchained..

3. Rapid Development

Developers can focus on writing code rather than managing infrastructure, resulting in faster development cycles and quicker time-to-market for applications.

4. Reduced Complexity

Serverless abstracts server management complexities, enabling developers to concentrate on writing efficient, single-purpose functions.

Challenges to Consider

While crafting Lambda functions for domain-specific logic may be straightforward, it’s important to recognize that building a comprehensive serverless infrastructure demands a broader range of components and considerations. Therefore, the infrastructure that surrounds the business logic for constructing enterprise-grade APIs must deliver,

1. Security:

Serverless applications are not immune to security threats. Protecting your serverless functions, data, and user interactions is paramount. Implement robust security practices, including access controls, authentication mechanisms, and thorough testing to fortify your application against vulnerabilities.

2. Monitoring for Success:

Effective monitoring is the heartbeat of any production-grade system. In the serverless realm, monitoring becomes more complex as functions are ephemeral and auto-scaling. Invest in comprehensive monitoring solutions to gain insights into your application’s performance, troubleshoot issues, and ensure optimal user experiences.

3. Encryption Everywhere:

In a world increasingly concerned with data privacy, end-to-end encryption is non-negotiable. Ensure that data is encrypted at rest and in transit, safeguarding sensitive information from evesdropping and complying with privacy regulations.

4. Performance Considerations:

While serverless technology excels in auto-scaling to meet demand, optimizing performance remains a key challenge. Architect your functions with performance in mind, optimizing code, minimizing cold starts, and leveraging caching when appropriate.

5. Best Practices Rule:

Serverless success lies in adhering to best practice recommendations. Stay informed about the latest industry standards and guidelines, embracing proven techniques for scalability, resilience, and maintainability.

However, expecting developers to not only write code but also be an expert on numerous cloud services and configure them accurately can be overwhelming. To address this challenge, CloudKitect offers a range of components and architectural patterns, enabling developers to construct enterprise-grade infrastructure seamlessly, all while keeping their primary focus on the APIs business logic.


Serverless technology has ushered in a new era of powering APIs, unchaining APIs from the constraints of traditional server resources. By harnessing the power of serverless platforms, organizations can streamline development, reduce costs, and enhance scalability. As you embark on your serverless journey, remember to weigh the benefits against the challenges and select the right tools and platforms for your specific use cases. The era of unchained APIs is here, and it’s time to leverage this transformative technology to drive innovation and efficiency in your organization.

07 - Mastering AWS Adoption Strategies_ From Basics to Advanced

Mastering AWS Adoption Strategies: From Basics to Advanced



Welcome to this comprehensive tutorial on AWS adoption strategies! In this guide, we will explore a spectrum of AWS infrastructure configuration approaches, ranging from the fundamental basics to more advanced and sophisticated setups. AWS, or Amazon Web Services, offers a robust cloud computing platform, and understanding how to structure your infrastructure is crucial for optimizing security, efficiency, and scalability.

The Basic AWS Setup

Many organizations kickstart their AWS journey by deploying all their application resources within a single AWS account. It’s a straightforward and convenient approach, but it’s not necessarily aligned with best practices, especially regarding security. This common practice exposes systems to a high risk of misconfiguration, potentially leading to security breaches and data loss. To establish a more robust and secure AWS environment, it’s essential to explore advanced account structures and resource partitioning strategies that align better with security and operational best practices.

A Simple Two-Account Strategy

A significant step up from the basic setup is adopting a two-account strategy. In this approach, every organization should maintain at least two separate AWS accounts:

  • Development Account: Dedicated to the development and testing of applications.
  • Production Account: Solely for hosting production workloads, with provisions for automation in deployment processes.

This dual-account structure offers several advantages, primarily bolstering security. By segregating development and production environments, access to sensitive production data is limited, reducing the risk of accidental deletions and enhancing data protection. This separation aligns with security best practices and contributes to the overall stability and reliability of your AWS infrastructure.

AWS Account Management with Control Tower

Taking AWS account management to the next level involves the use of a dedicated AWS account for management purposes and integrating the AWS Control Tower service to create a basic landing zone. This approach results in the establishment of two distinct organizational units:

  • Security Organization Unit: Comprising an ‘audit’ account for security checks and a ‘centralized logging’ account for log consolidation and enhanced monitoring.
  • Workloads Organization Unit: Further refining the architecture, this organizational unit divides into a ‘Dev OU’ tailored for development workloads and a ‘Prod OU’ exclusively for hosting production workloads.

This meticulously structured setup serves as a robust foundation, allowing scalability and future maturity without the need for extensive overhauls. It not only enhances security but also optimizes resource management, setting the stage for an efficient and adaptable AWS infrastructure.

The Enhanced Landing Zone - Strategies for SMBs and Enterprises

For those seeking an even more advanced AWS setup, CloudKitect recommends the Enhanced Landing Zone approach. This configuration introduces additional organizational units, including a ‘Sandbox OU’ for developer experimentation and an ‘Infrastructure OU’ dedicated to shared services like Route53. Within the ‘workloads OU,’ further refinement is achieved by establishing distinct organizational units for development, staging, and production environments. Each unit is equipped with specific security control policies, ensuring a fine-tuned approach to security management.

To enhance security further, this strategy deploys config rules in each account for compliance checks against industry standards such as PCI, NIST, and CIS. The results of these checks are directed to an audit account configured with Security Hub, Amazon Macie, and GuardDuty to conduct in-depth analysis and report on compliance and security violations. Additionally, a central logging account is designated to receive logs from every account, facilitating comprehensive log analysis and improving overall security posture.


The success of your AWS journey lies in its foundation. While the complexity of AWS infrastructure may seem daunting, establishing the right organizational structure, implementing security policies, and optimizing resource management are essential. Fortunately, CloudKitect offers proprietary tools that streamline this entire infrastructure setup, reducing it from a potentially daunting task to a process that can be completed in just a few hours.

Remember, a well-structured AWS environment not only enhances security but also sets the stage for efficient operations, scalability, and future growth. So, whether you’re just starting your AWS journey or looking to refine your existing setup, mastering these AWS adoption strategies is a step in the right direction.

10 - Navigating the Cloud with Compliance_ Why It’s More Crucial Than Ever

Navigating the Cloud with Compliance: Why It’s More Crucial Than Ever



In today’s digital age, where businesses are accelerating their move to the cloud, there’s an essential factor that can’t be overlooked: compliance. With standards like PCI, NIST 800, CIS, HIPAA, and many others emerging as industry benchmarks, ensuring compliance is no longer a luxury—it’s a necessity.

The AWS Default Dilemma

AWS, one of the leading cloud service providers, is designed with an expansive and flexible approach. Its defaults are built for versatility to cater to a wide variety of user needs. However, while AWS defaults are fantastic for simpler workloads, they don’t always come pre-configured to meet various compliance requirements of complex applications. Why is this? It’s because AWS aims to be a broad canvas, allowing businesses to paint their unique operational models.

Yet, as more and more workloads seek robust security through compliance adherence, there arises a challenge. Organizations often find themselves needing experienced cloud architects. These architects are not just versed in the nuances of AWS and its plethora of services but are also have extensive knowledge of various compliance standards. They dive deep, configuring services to meet these standards, and subsequently rigorously test them for compliance—a task that is undeniably time-consuming and intricate.

Enter CloudKitect: Your Compliance Compass in the Cloud

This is where the brilliance of CloudKitect shines through. Imagine not having to traverse the difficulties of achieving compliance alone. CloudKitect has already undertaken the task of ensuring each service aligns with diverse standards such as PCI, NIST, CIS, and more. In doing so, it provides organizations with a clear roadmap to adherence without the typical headaches.

What does this mean for your teams? Empowerment. With CloudKitect, even teams that aren’t compliance specialists can build enterprise-grade infrastructures that resonate with the highest standards.

The CloudKitect Advantage

By using CloudKitect components and patterns to build your infrastructures in the cloud, you’re not just adding another tool to your digital arsenal; you’re hiring an architect. Our solutions pave the way, saving invaluable time and resources, ensuring that your journey to cloud compliance isn’t filled with pitfalls but is a streamlined, effective process.


In an era where data breaches are commonplace and the protection of digital assets is paramount, compliance isn’t just about ticking boxes. It’s about instilling trust, maintaining reputation, and ensuring the safety of both businesses and their clientele. With CloudKitect, that journey becomes less about navigating complex terrains and more about strategic progression. Ready to fast track your compliant journey in the cloud? CloudKitect is here to guide the way.

15 - 5 Reasons for to Embrace Serverless

5 Reasons for to Embrace Serverless


Many startups embrace a serverless-first strategy as the world shifts towards cloud computing. This means building applications that rely on cloud-based services for their backend infrastructure rather than deploying and managing servers.

Here are the top 5 reasons why startups should consider a serverless-first strategy.

1. Scalability:

One of the most significant benefits of a serverless architecture is scalability. With traditional servers, startups need to estimate how many resources they will need in advance and deploy servers accordingly. This can lead to underutilization or overutilization of resources, increasing costs. In contrast, serverless architectures allow startups to scale up or down based on demand, which saves money and increases efficiency.

2. Reduced Cost:

With a serverless architecture, startups only pay for the resources they use, meaning there is no need to maintain or pay for servers that may not be used. Furthermore, serverless architectures also save money by reducing the amount of time and resources needed for maintenance and support, which can be a significant expense for startups.

3. Increased Agility:

Serverless architectures enable startups to rapidly develop and deploy new features without worrying about infrastructure management. This increased agility allows startups to focus on innovation and user experience rather than worrying about servers and maintenance.

4. Improved Security:

Serverless architectures can also enhance security. With traditional server setups, startups are responsible for securing the server itself, as well as the applications and data hosted on it. With serverless architectures, the cloud provider takes care of the security of the underlying infrastructure, freeing startups from this burden.

5. Easier to Manage:

Finally, serverless architectures are easier to manage than traditional server setups. With serverless architectures, startups can focus on developing their applications and leave the infrastructure management to the cloud provider. This not only saves time and resources but also ensures that the infrastructure is always up-to-date and optimized.


In conclusion, a serverless first strategy can offer many benefits to startups, including scalability, reduced costs, increased agility, improved security, and easier management. XLER8R is built with a serverless first mindset, offering all the advantages mentioned above out of the box.

16 - Optimizing Cloud_ The Importance of a Multi-Account Strategy

Optimizing Cloud: The Importance of a Multi-Account Strategy


Amazon Web Services (AWS) provides an array of resources and services that have revolutionized how organizations approach their IT infrastructure. However, building enterprise grade cloud infrastructure is complex and a critical facet of simplifying these complex infrastructures is effective account structuring. In this context, CloudKitect always recommends their customer to employ a multi-account strategy that will provide an advanced layer of security, easier management, and efficient cost-tracking.

Enhanced Security

The multi-account strategy is crucial for maintaining the security and integrity of AWS resources. By segregating resources into distinct accounts, a boundary is created, preventing security incidents from impacting resources across the whole organization. In case of a security breach, the issue is confined to the compromised account, which significantly reduces the potential damage.

Moreover, each AWS account has distinct Identity and Access Management (IAM) policies, allowing for granular control over access to resources. This limits the scope of privileges that any individual user or service has, further enhancing the security within each account.

Simplified Management and Operational Resilience

A multi-account architecture allows for clear separation of concerns. Each account can be designated to a particular department, project, or environment (development, staging, production). This can drastically simplify resource management, as resources pertinent to a specific department or project are easily identifiable and manageable.

Operational resilience is another key benefit. For instance, if one account’s resources hit a service limit, it will not affect the operation of resources in other accounts. This isolation aids in maintaining business continuity even if an issue arises in a particular department or project.

Efficient Cost Allocation and Tracking

An AWS multi-account strategy can play a vital role in cost management. By breaking down AWS usage per account, organizations can better track and allocate costs. Each account can be assigned to a specific cost center or project, thereby enabling accurate cost attribution. This enhances the transparency of cloud expenditure, making it easier for organizations to understand where their money is being spent and which projects or departments are incurring those costs.

Compliance and Auditing

With a multi-account strategy, compliance and auditing become more manageable tasks. Each account has its own set of CloudTrail logs, simplifying the auditing process. It becomes much easier to track actions and changes in an environment specific to a project or a department. If compliance needs to be ensured across a certain department, having a separate AWS account for it means that auditors only need to focus on that specific account rather than the whole organization.

Greater Control over Service Limits

Each AWS account comes with its own service limits, which provides an additional layer of control and prevents any one project or department from using all of an organization’s resources. By employing a multi-account strategy, you’re able to ensure that one department’s heavy usage won’t impact other departments’ operations.


In conclusion, a multi-account strategy is a powerful tool when operating in the AWS cloud. By offering enhanced security, simplified management, efficient cost tracking, easier compliance and auditing, and better control over service limits, it significantly simplifies and strengthens the cloud management for organizations of all sizes. This allows businesses to take full advantage of the flexibility and power of the AWS cloud, while maintaining control and visibility over their resources. Therefore, a multi-account strategy should be a key part of any organization’s AWS planning and management.

CloudKitect has developed advanced tooling to facilitate the effortless adoption of a multi-account strategy, incorporating all best practice recommendations from AWS. Reach out to us today for a thorough evaluation of your Cloud Strategy and let us assist you in embracing cloud technology in the most effective manner.

17 - 5 Reasons Why a Reliable Application is Crucial

5 Reasons Why a Reliable Application is Crucial


As startups strive to establish themselves in a fiercely competitive market, having a reliable application can make all the difference. A reliable application is essential for any startup looking to attract and retain customers, scale up their business, and maintain a positive reputation in the industry. In this article, we will explore the top five reasons why building a reliable application is critical for startup success. From enhancing customer experience to reducing downtime and increasing profitability, these reasons highlight the importance of investing in a reliable application from the very beginning.

1. Customer satisfaction:

A reliable application ensures customers have a positive experience using the product, leading to increased customer satisfaction and loyalty. Unreliable applications can frustrate customers, leading to negative reviews and declining customer retention.

2. Brand reputation:

The reliability of an application can directly impact a startup’s brand reputation. Word of mouth is a powerful marketing tool, and negative feedback about an unreliable application can quickly spread, damaging a startup’s reputation.

3. Cost savings:

Developing a reliable application from the outset can save startup money in the long run. Fixing bugs and addressing issues with an unreliable application can be time-consuming and expensive. Investing in reliability early on can help prevent these issues from arising in the first place.

4. Competitive advantage:

In today’s market, customers have many choices regarding technology products. A reliable application can be a key differentiator for a startup, setting it apart from its competitors and attracting new customers.

5. Scalability:

As a startup grows and attracts more customers, it needs to ensure its application can handle the increased traffic and usage. A reliable application can scale more efficiently, allowing the startup to grow without encountering technical issues.


In summary, building a reliable application is essential for startups to succeed in today’s market. CloudKitect is a turnkey solution built with reliability as its foundation while accelerating your time to market. 

19 - Breaking Free_ From Monolithic to Serverless

Real Examples of Machine Learning


What is Machine Learning?

Machine learning is a field of artificial intelligence in which we focus on problems that can not be solved using traditional instruction-based algorithms. Instead, we build algorithms that learn based on past experiences without being explicitly programmed. Hence the name machine learning.

Machine Learning Analogy

One way to think about machine learning is to compare a machine learning model to a student and the process of training a machine learning model to the process of teaching a student.

Like a student, a machine learning model starts with little or no knowledge about the problem space. It learns by being exposed to data, just like a student learns by being exposed to new information.

We train machine learning models by using a large amount of data and tweaking their parameters based on the accuracy of their results. This process is similar to a teacher presenting new information to students and providing feedback on their progress.

Just like students become more knowledgeable and skilled as they continue to learn and practice, machine learning models continue to learn from data and become more accurate at making predictions.

Various categories of Machine Learning algorithms are utilized to address diverse problem types.

Supervised Machine Learning

Supervised learning is a type of machine learning where an algorithm is trained on labeled data to make predictions or classifications on new, unseen data.

Imagine you are a teacher and want to teach your students to recognize different types of animals. You start by showing them pictures of different animals and labeling them with their names. For example, you show them a picture of a dog and label it as “dog”. Then you show them a rabbit picture and label it “rabbit”. You continue this process for many different animals.

After your students have seen many labeled animal pictures, you quiz them on new pictures they haven’t seen before. For example, you show them a rabbit picture and ask them to identify it. Because they have seen many labeled pictures of rabbits before, they can recognize the rabbit.

In this analogy, you are the supervisor providing labeled data to the students (algorithm). The pictures of animals represent the training data, and the new image of the rabbit represents the unseen data that the algorithm is predicting or classifying. 

Unsupervised Machine Learning

While supervised learning algorithms are used to make predictions or decisions based on labeled data, unsupervised learning algorithms find patterns and relationships in data that is not labeled.

Imagine you are a librarian tasked with organizing a large collection of books. You have no idea what the books are about or how they should be grouped together, but you need to find a way to organize them in some logical order.

So, you start by grouping books with similar titles or authors. Then, as you start reading the books, you notice that specific themes or topics emerge. You start grouping books with similar themes or topics together, even with different titles or authors.

Eventually, you end up with groups of books with similar themes or topics, even though you didn’t have any preconceived notions of what those themes or topics should be. This is similar to unsupervised learning, where an algorithm is trained on unlabeled data and identifies patterns or relationships within the data without any prior knowledge of what those patterns or relationships should be.

Reinforcement Learning

Reinforcement learning is a type of machine learning that involves training an agent to take actions in an environment to maximize a reward. The agent is trained through trial and error by interacting with the environment and receiving feedback through rewards or punishments.

Imagine you are a dog owner trying to train your dog to perform tricks. You start by rewarding the dog with treats every time it performs a trick correctly. For example, if you want to teach the dog to sit, you give it a treat every time it sits on command.

As the dog continues to perform the trick correctly, you give it fewer treats and only reward it when it serves the trick perfectly. This is like reinforcement learning, where the agent (the dog) learns to take actions (performing tricks) in an environment (the training session) to maximize a reward signal (the treats).

Over time, the dog learns which actions lead to rewards and which do not, adjusting its behavior accordingly. For example, if the dog realizes that barking does not lead to a reward, it may stop barking and focus on performing other tricks that do lead to a reward.

In this analogy, the dog is the reinforcement learning agent, the tricks are the actions it takes in the environment, and the treats are the reward signal. By using reinforcement learning, the dog learns to perform tricks more effectively and efficiently by maximizing the rewards it receives.


In summary, Machine Learning has many practical applications, including improving customer experiences, optimizing business processes, and even making medical diagnoses. It’s a powerful tool that can help you find insights and make predictions in a way that would be impossible with traditional programming techniques.