Challenges of Cloud Migration and How to Overcome Them

The cloud migration journey is rarely straightforward. In fact, the BCG study reveals that more than half of all transformation efforts fail to deliver their intended benefits within three years. Even more concerning, once a cloud migration plan deviates from its intended course, reversing that trajectory can be notoriously difficult.

Research by McKinsey highlights the scope of the challenge: up to 38% of cloud migration initiatives fall behind schedule, and 75% exceed their budget. The numbers prove that to benefit from cloud migration, you should be aware of what could go wrong and build a step-by-step strategy.

In this article, we’ll explore the most common cloud migration risks and, more importantly, how to overcome them.

Knowing what challenges might be waiting around the corner helps you prepare and avoid costly mistakes. Below are the most common cloud migration issues companies may face, along with practical advice on how to address them.

According to McKinsey, a lack of clear cloud migration plan can result in an average annual overspend of 14% on migration costs. Moreover, 38% of companies report delays exceeding a full quarter (see the table below).

A successful cloud migration starts with a clear, realistic, and business-aligned strategy. It requires a thorough assessment of your current IT infrastructure to define migration goals and prioritize workloads based on complexity and business impact. If you lack the required experience in-house, feel free to engage cloud migration experts or partners early in the planning stage. This can also help you avoid costly missteps and keep the initiative on track from day one.

According to Accenture, 40% of companies identify the need to modernize legacy applications as a top barrier to achieving their desired cloud outcomes. The reasons often lie in the incompatibility of the obsolete architecture with modern cloud-native platforms. To function effectively, they may often require modifications.

One effective approach to modernizing legacy systems is digital decoupling. Instead of overhauling a tightly coupled legacy system all at once, digital decoupling allows you to build a new, cloud-native system alongside the old one. Both systems run in parallel, with data synchronized in near real time. This way, you avoid the limitations of the legacy setup while gradually introducing modern capabilities.

Addressing cloud security and compliance starts with a clear understanding of the shared responsibility model (i.e., which security tasks are handled by the cloud service provider and which are handled by the customer).

The provider (such as Azure or AWS) is responsible for securing the cloud infrastructure itself. This includes the physical data centers, network, hardware, and foundational software like virtualization and storage services.

Key areas your organization should manage include:

• Data encryption and access controls
• Identity and access management (IAM)
• Application-level security
• Regulatory compliance (e.g., GDPR, HIPAA, PCI-DSS)
• Network security configuration

One way to achieve this is to adopt a zero-trust architecture, which assumes no implicit trust within or outside the network. A 2023 Gartner survey found that 63% of organizations have already adopted a zero-trust strategy to enhance their cloud security.

While the cloud offers the promise of lower infrastructure expenses and flexible pay-as-you-go pricing, many companies struggle to control and predict their actual spend. Unused resources, overprovisioned services, and a lack of visibility into usage patterns often lead to substantial budget overruns.

In fact, according to the 2025 Flexera State of the Cloud report, 84% of organizations identify managing cloud spend as their top cloud challenge. To avoid unexpected costs and keep cloud expenses under control, your company needs to adopt effective cost optimization strategies.

One proven approach is to establish a cross-functional FinOps team that brings together finance, operations, and engineering. This team works collaboratively to create a shared understanding of cloud usage and costs as well as ensure that every department makes informed, cost-efficient decisions about cloud resources.

According to the 2023 Cloud Skills Report by SoftwareOne, 95% of businesses worldwide report a cloud and IT skills gap. On average, this deficit has delayed digital transformation projects by five months, with one-third of companies saying it has significantly impacted their financial goals.

Cloud security, in particular, remains a high-risk area. A recent O’Reilly survey found that 38.9% of respondents identify cloud security as the most critical skills shortage. This can lead to inappropriate data handling as organizations migrate sensitive workloads.

To address the cloud skills gap, consider partnering with experienced cloud development providers. Leobit can help you move your app and data from on-premises to the cloud or shift from one cloud provider to another.

One of the most underestimated challenges is ensuring alignment between business goals and IT strategy while managing the human side of change. Without strong change management practices, even technically sound migration projects can fail due to poor coordination and employee resistance.

According to a McKinsey survey of 450 CIOs and IT leaders, organizations that completed their cloud migrations on time and within budget had one crucial factor in common: effective change management, characterized by clear communication, defined responsibilities, and stakeholder alignment. In contrast, poor orchestration and change management missteps can result in $100 billion in wasted migration spending over three years.

The challenges we’ve covered so far apply to almost any cloud migration, no matter the industry or size of the company. But not all cloud migrations are the same. Moving from on-premises to the cloud, setting up a hybrid cloud, or switching from one cloud provider to another each come with their own specific difficulties. In the next chapters, we’ll take a closer look at the unique challenges of each type.

On-premises to cloud migration is often associated with legacy software modernization. However, even a modern on-premises setup may not be designed for the distributed, scalable nature of the cloud.

Let’s take a look at the most common challenges you may encounter when migrating your on-premises applications to the cloud.

Your on-premises application might rely on outdated frameworks or hardcoded configurations that simply don’t translate well to cloud infrastructure. For example, some applications may require low-latency access to local file systems or depend on specific versions of databases or middleware that cloud providers do not support.

A common obstacle is the tight coupling of services and systems. It happens when applications, databases, and infrastructure components are interdependent and deeply integrated. So, moving individual components to the cloud without disrupting the entire system can become a real challenge.

To do it right, you should do a technical audit of your current IT environment. During this process, your team should perform dependency mapping to understand how systems interact and identify what can be decoupled or rearchitected.

To be on the safe side, consider using a phased migration strategy. You can start with less critical components or those that are easier to rehost, replatform, or refactor, thereby minimizing disruption and reducing risks.

Migrating data from on-premises systems to the cloud is often one of the most technically demanding and risk-prone parts of the cloud journey. It’s because data stored in legacy systems may be siloed, poorly structured, or tied to specific formats that don’t easily align with modern cloud architectures. These formats can include proprietary file types (e.g., .nsf, custom binaries), mainframe encodings such as EBCDIC, flat files with inconsistent delimiters, or legacy database systems like IBM DB2 or FoxPro.

Integration adds another layer of difficulty. As companies adopt a phased migration approach, they often need legacy systems and cloud applications to work in parallel. This is where the Strangler Facade pattern comes in.

The Strangler Facade acts as a smart routing layer that sits in front of both the old and new applications. Its job is to intercept incoming requests and intelligently route them to either the legacy system or the new cloud-based component, depending on which part of the application has already been modernized. This allows you to gradually replace legacy functionality with modern services while minimizing disruption and avoiding a risky “big bang” cutover.

Modern on-premises infrastructure often relies on overprovisioning to ensure performance and availability. Suppose you follow this pattern when moving to the cloud. In that case, it can result in significant cost inefficiencies. According to CAST AI, in cloud clusters with 50 or more CPUs, organizations use only about 13% of the CPU capacity and just 20% of the allocated memory on average.

To address this challenge, your company needs to shift toward a usage-based approach, rather than overprovisioning as you would in a traditional on-premises setup. This involves adopting real-time monitoring, automated resource right-sizing, and other cloud cost optimization techniques.

Cloud-to-cloud migration often introduces a new set of technical and architectural challenges. Even though both environments are “clouds,” they can differ significantly in how they manage compute, storage, networking, security, and automation.

Each cloud provider offers proprietary services and APIs, meaning that applications and infrastructure designed for one platform may not work seamlessly on another. For instance, a workload that heavily relies on AWS-specific services, such as Lambda, DynamoDB, or IAM roles, may require significant rework to function correctly in Microsoft Azure, which offers similar but not identical alternatives.

​​To minimize disruption, consider investing in automation tools that support multi-cloud environments. You can also use containerization (e.g., Docker and Kubernetes) to decouple workloads from any single provider’s infrastructure. This abstraction makes future transitions smoother and supports a more portable, vendor-neutral architecture.

While cloud providers typically offer free or low-cost ingress (data entering their platform), they often charge significantly for egress (data leaving their environment). These charges can add up quickly and result in unexpected costs.

Typically, providers charge anywhere from 5 to 16 cents per gigabyte when data is transferred out of the cloud to an external location, such as an on-premises data center or another cloud. For individual users or small workloads, this might not make a big difference. However, for large businesses moving terabytes of data in and out of services like Azure or AWS, the costs can become substantial.

For instance, egress charges can quickly escalate when you need to move data-intensive workloads, such as video streaming, real-time analytics, or machine learning pipelines. This can minimize the cost-saving benefits that often motivate the migration in the first place.

To manage this risk, carefully calculate the total cost of ownership (TCO) before initiating the migration. Understanding the costs associated with moving data in and out of the cloud will help you prevent unpleasant financial surprises. Additionally, verify if your cloud provider offers discounts or waivers for large migrations or has special agreements to reduce egress charges under specific conditions.

Source: AWS and Azure official websites

While leading cloud providers like AWS and Microsoft Azure all offer robust security and compliance frameworks, their policies, certifications, and default configurations can vary. These differences can create legal and operational issues during or after migration if not carefully managed.

For instance, data residency laws require that certain types of data (e.g., healthcare records or financial information) remain within specific geographic boundaries. Azure offers customers more than 60 regions worldwide, with built-in tools that enable them to select specific geographic zones to meet data residency requirements.

In contrast, AWS also provides a global infrastructure; however, its approach to region-specific compliance and data storage policies may require different configurations and governance steps. Migrating workloads from Azure to AWS, or vice versa, may unintentionally violate data locality requirements if regions are mismatched or defaults are not adjusted.

Similarly, encryption policies and key management differ across providers. Azure, for example, uses Azure Key Vault to manage customer-owned keys and supports various compliance standards, including FedRAMP and ISO/IEC 27001. AWS offers the AWS Key Management Service (KMS) and supports both customer-managed and AWS-managed keys. However, its encryption workflows and default settings may differ. A mismatch in encryption standards, key rotation policies, or data access controls during migration can expose sensitive information or lead to non-compliance with regulations such as GDPR or HIPAA.

To avoid these pitfalls, you should conduct a compliance gap analysis before initiating the migration. This involves reviewing the compliance certifications, region-specific offerings, and security features of both the source and destination cloud platforms.

Each cloud platform offers its own unique set of tools and managed services, which are often not directly compatible with those of another provider. For example, an application built initially using Azure Functions (serverless compute) and Cosmos DB (NoSQL database) may require substantial reengineering to work with AWS equivalents, such as AWS Lambda and DynamoDB.

Although these services serve similar purposes, they differ in their APIs, configurations, pricing models, and operational behaviors.

Even infrastructure management tools can introduce complexity. AWS uses CloudFormation for infrastructure-as-code, while Azure relies on ARM templates or Bicep. A company that has built automation and CI/CD pipelines around these tools may need to rebuild large parts of its deployment workflows when moving between platforms.

To minimize vendor lock-in, you can adopt a cloud-agnostic architecture wherever possible. This includes using open-source tools, standard APIs, and containerization technologies like Docker and Kubernetes, which allow workloads to run consistently across different cloud environments.

When building or refactoring applications, it’s wise to avoid hard dependencies on proprietary services unless they provide a clear, long-term strategic advantage. In cases where migration is necessary, start by identifying all platform-specific components and evaluating alternatives on the target platform.

Involving experienced cloud migration professionals and using abstraction layers (e.g., Terraform for IaC, or service mesh technologies like Istio) can further reduce friction and increase portability across cloud platforms.

Cloud platforms differ not only in the services they offer but also in how those services are exposed and consumed through APIs. Custom-built applications that rely on a specific provider’s APIs or SDKs often require significant redevelopment when shifting to another cloud environment.

For example, a workload tightly integrated with Azure services, such as API Management, Azure Monitor, or RBAC, may not seamlessly translate to AWS equivalents, like API Gateway, CloudWatch, or IAM policies. Even if similar services exist on the target cloud, differences in authentication, request formats, throttling policies, or event handling can force teams to rewrite parts of the application or develop new integration layers from scratch.
So, how can you deal with it?

The first step is to conduct a detailed inventory of all cloud-dependent components, including APIs, services, and third-party integrations. Identify which are proprietary and which follow open standards. For custom applications, consider redesigning key components to use cloud-agnostic interfaces or standardized protocols (like REST, gRPC, or GraphQL), which improve flexibility and reduce future rework.

Where a complete rewrite is impractical, wrapping platform-specific APIs behind an abstraction layer can help isolate differences and ease migration. For example, middleware or API gateways can serve as translation layers between old and new cloud environments during a phased migration.

In some cases, you may choose to keep specific workloads on-premises due to data sensitivity or compliance requirements. This leads to the adoption of a hybrid cloud model, which combines cloud resources with on-premises infrastructure. While this approach offers flexibility, it also introduces unique technical and organizational challenges that must be carefully managed.

Unlike fully cloud-native systems, hybrid setups require continuous interaction between on-premises and cloud-based components, which puts enormous pressure on network performance and stability.

Latency becomes a significant concern, particularly for real-time or latency-sensitive applications that require near-instantaneous data exchange. Even minor delays in connectivity can degrade user experience or disrupt workflows. Bandwidth limitations, packet loss, and inconsistent network throughput can all create performance bottlenecks that are difficult to diagnose and resolve without complete visibility across both environments.

To mitigate these issues in hybrid cloud adoption, prioritize network planning as a foundational step. You can also use dedicated connectivity solutions, such as Direct Connect or ExpressRoute. These options provide more stable and predictable performance than traditional VPNs that run over the public Internet. It’s also essential to continuously monitor network traffic with advanced observability tools that provide visibility across both cloud and on-premises environments.

When data is stored, modified, or accessed in multiple locations, your task is to ensure it remains accurate, synchronized, and up-to-date across all environments. Problems can quickly arise if systems fall out of sync. For example, a customer record updated in the cloud may not be reflected in the on-premises system immediately (or at all), leading to operational errors, reporting inconsistencies, or compliance risks. In highly regulated industries, such as healthcare and finance, even minor lapses in data consistency can have serious consequences.

Hybrid architectures also complicate transactional integrity. Distributed data systems often struggle with enforcing ACID (Atomicity, Consistency, Isolation, Durability) properties across geographic or platform boundaries. Latency, network reliability, and differing data storage formats further intensify the risk of conflicts and data loss during replication or synchronization processes.

To address this challenge, you need to adopt a robust data integration strategy tailored for hybrid environments. Technologies such as change data capture (CDC), data lakes, or streaming platforms (e.g., Apache Kafka or Azure Event Hubs) can help synchronize data across environments. In cases where real-time updates are crucial, bi-directional synchronization mechanisms may be necessary, along with conflict resolution logic to manage overlapping changes.

One of the core challenges of migrating parts of your infrastructure to the cloud lies in identity and access management (IAM). On-premises systems often utilize legacy authentication methods, such as Active Directory, whereas cloud platforms rely on modern identity and access management (IAM) tools, including Azure AD or AWS IAM. Without a unified approach, this can lead to inconsistent access rights and security gaps.

Another issue is fragmented security policies. Tools like firewalls or encryption may be configured differently across environments, making it harder to monitor and respond to threats. To address this, companies should establish centralized security governance and use federated authentication to ensure consistent user access. Adopting infrastructure-as-code and policy-as-code practices also helps apply and audit security settings uniformly, reducing risk and improving visibility.

When security configurations are defined as code, they can be version-controlled, peer-reviewed, and automatically tested, just like software. This ensures that all environments follow the same security standards. Uniform policies reduce the likelihood of human error or misconfigurations, while built-in auditing facilitates easier tracking of changes, verification of compliance, and prompt response to security incidents.

When migrating to the cloud, your destination isn’t the cloud itself, but the opportunities and improvements it enables. The cloud is a means to an end, not the end itself. To truly realize its value, you should approach migration strategically and align technology decisions with your business goals.

If you’re planning a cloud migration and want to make your journey smooth, Leobit’s cloud experts are here to help. With deep experience in cloud architecture, modernization, and secure migration, we’ll guide you through every step of the process. We always start from a technical audit to ensure your transformation delivers the results you expect. Contact us today and let’s start your cloud transformation journey together.