The Future of Cloud Migrations: Leveraging Generative AI for Fast and Cost-Effective Results

When it comes to cloud migration outcomes, results can vary greatly. Some projects successfully transform business operations, eliminate technical debt and unlock new growth opportunities. However, many migrations fall short or even stall due to poor planning, inadequate resources or a lack of expertise.

Rackspace Technology has been helping customers successfully migrate to a variety of cloud options for over a decade. Now, with the advancements in generative AI, we are seeing AI-infused technologies revolutionize the migration process across all phases. For example, they are reducing risk and creating faster outcomes, all while reducing costs.

Assess your migration opportunities

The first, and arguably most critical, phase of a migration project is the assessment. Without properly building the business case with a realistic ROI, understanding the journey they are embarking on and obtaining stakeholder buy-in, achieving a successful outcome is a gamble.

Cloud migration statistics reveal staggering numbers. When it comes to outcomes, 83% of all data migration projects fail or exceed their budgets, according to Gartner. It also predicts that by 2026, roughly 45% of all enterprise IT budgets will be spent on the cloud. These insights speak to the fact that the stakes are high in cloud migration. Leveraging generative AI tools can greatly increase the odds of a successful migration by helping to reduce risks.
 

Streamline asset discovery and validate assumptions

The discovery phase begins once all stakeholders agree that the migration makes sense, both financially and strategically, for an organization. This is where we start to realize the power of generative AI technology. Analyzing massive amounts of data from on-premises environments can provide amazing insights and services, like the AWS Application Discovery Service.

This service can help build inventory lists and map connections and uncover hidden dependencies between applications and servers that may not be documented. Application Discovery Service can also generate migration plans with rightsized Amazon EC2 instances based on current CPU trends and usage, providing meaningful business case inputs, such as total cost of ownership.

Feeding this data into AI models can help determine optimal architectural patterns from network traffic analysis via VPC flow logs, identify unknown risks from application logs and map optimal cloud services based on resource use from existing applications. Knowing what potential cloud-native patterns are ideal for a given application can influence decision-making around which migration strategy to implement and what risks are associated with a strategy by modernizing during or after the migration. 

Migrate smarter, not harder

The next phase of a cloud migration is aligning resources, creating migration waves and beginning to execute the migration. Ideally, organizations will choose to modernize their applications during the migration phase. Leveraging services like AWS Transform, users can reduce weeks or months’ worth of effort down to hours or even days. Not only does this greatly reduce timelines, but it also minimizes risk, allowing modernization activities to occur in flight. This helps organizations realize the benefits of operating in the cloud immediately.

Even if your organization is not ready to modernize immediately, generative AI services, like Amazon Q Developer, can still help reduce risks and improve your chance of a successful migration. For example, Q Developer can analyze existing legacy code bases and generate missing unit tests that can be used to validate applications post-migration, giving your team the confidence that applications will continue to operate as expected when production load is shifted. Q Developer generates missing documentation and inspects legacy code bases, providing developers with a natural language-based interactive experience while exploring both existing and new code. 

Many migrations are time-boxed activities with a hard deadline that require speed and efficiency, such as a data center exit. Modernization can happen post-migration, but it is critical to use best practices, like deploying all cloud infrastructure as code (IaC) from the start. The days of writing AWS CloudFormation or Terraform from scratch are over. Leveraging tools like Amazon Q Developer to generate IaC via natural language or even from an architecture diagram will greatly improve the speed at which your application is launched in the cloud and validation testing can begin.

Make Amazon Q Developer your superpower

Here is an example prompt that demonstrates the power of Amazon Q Developer CLI to not only generate code but also to provide configuration recommendations and architecture diagrams. Please give it a try.

The AWS CloudFormation template generated included Amazon CloudWatch alarms for the Amazon API Gateway and AWS Lambda function as well as an AWS WAF with a rate limit rule preconfigured. And by using prompt chaining, you can converse with Amazon Q to update the code and continually refine the responses until they meet your exact needs. 

Begin, advance and accelerate your cloud journey

Generative AI tools are becoming more capable by the day, and their ability to assist in all phases of cloud migrations is a game-changer. These tools, when used appropriately and responsibly, can supercharge any cloud migration. Rackspace Technology has been helping customers migrate to the cloud for over a decade. Combining this deep knowledge and expertise with the AI tooling available today has created one of the most compelling times to contact Rackspace and begin, advance and accelerate your cloud migration journey — no matter where you stand in the process today.

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