As businesses increasingly seek agility, cost optimization, and advanced services, cloud migration continues to be a strategic initiative. Many enterprises that started their cloud journey with Microsoft Azure are now exploring Google Cloud Platform (GCP) for its AI/ML capabilities, robust containerization tools, and competitive pricing. However, transitioning from Azure to Google Cloud isn’t always straightforward. Despite the flexibility of cloud technologies, each provider has its own ecosystem, services, and architectures, which can lead to compatibility challenges during migration.

In this blog, we’ll explore six key compatibility issues to watch for when migrating from Azure to Google Cloud, along with solutions and best practices to help mitigate these concerns.

1. Virtual Machine (VM) Configuration Differences

Azure and GCP offer various VM sizes, but they are not one-to-one compatible. Azure uses “VM series” (like B, D, E, F, etc.), while Google uses predefined machine types (e.g., e2-standard, n1-highmem). Their underlying CPUs, memory-to-core ratios, and network performance can differ, which can impact performance post-migration.

What to Watch For:

• Mismatch in CPU performance
• Different GPU options
• Licensing issues with Windows Server and SQL Server

Solution:
Map Azure VM instances to equivalent or custom Google Cloud machine types using GCP’s Compute Engine Migration Tools and carefully test performance in a staging environment before final migration.

2. Networking Architecture Variations

Azure’s virtual networking structure (VNets, Subnets, NSGs) is different from GCP’s Virtual Private Cloud (VPC) structure. For example, Azure relies heavily on Network Security Groups (NSGs), while Google uses firewall rules at the network and instance level. This discrepancy can result in misconfigured network security or accessibility problems.

What to Watch For:

• Network security rule misalignment
• IP range conflicts
• Differences in DNS management and VPN configuration

Solution:
Redesign your network architecture to align with GCP’s best practices. Make use of Google Cloud’s VPC Peering, Cloud VPN, and firewall rules to replicate the desired structure from Azure.

3. Identity and Access Management (IAM) Differences

While both Azure and Google Cloud provide robust IAM features, their models and terminology differ significantly. Azure uses Role-Based Access Control (RBAC) within the Azure AD ecosystem, while Google Cloud uses IAM policies with roles and permissions at the project, folder, and organization levels.

What to Watch For:

• Inconsistent access control policies
• Loss of fine-grained permissions
• Disruption in single sign-on (SSO) workflows

Solution:
Carefully map Azure AD roles to Google IAM roles and test policies in a secure sandbox environment. Consider integrating Google Cloud with Azure AD using federation or Google Workspace SSO if you want to maintain identity consistency.

4. Storage Services and Formats

Azure’s storage offerings (Blob, File, Table, and Queue) are different in structure and behavior compared to Google Cloud Storage services. For instance, Azure Blob Storage may not directly align with Google Cloud Storage buckets in terms of metadata handling, access permissions, or performance tiers.

What to Watch For:

• Incompatibility in data formats and metadata
• Variances in access management and ACLs
• Latency due to different regional locations

Solution:
Use data migration tools like gsutil, Storage Transfer Service, or third-party migration platforms to ensure a smooth transition. Make sure to review and replicate access policies and bucket configurations on GCP.

5. Containerization and Kubernetes

Both Azure Kubernetes Service (AKS) and Google Kubernetes Engine (GKE) offer managed Kubernetes environments, but the underlying implementations and toolchains may differ. GCP often takes the lead in Kubernetes innovation since Google originally developed Kubernetes, but AKS users may face difficulties during workload migration.

What to Watch For:

• YAML configuration mismatches
• Version compatibility issues
• CI/CD pipeline breakages

Solution:
Use tools like Anthos, Velero, or Helm for migrating Kubernetes workloads. Validate cluster versions and node configurations to ensure parity between AKS and GKE environments. Adopt Google Cloud’s Cloud Build or maintain CI/CD tools like Jenkins with minimal changes.

6. Database Migration Complexity

Azure supports a wide range of managed databases like Azure SQL Database, Cosmos DB, and MySQL for Azure. When migrating to Google Cloud, there might be no direct equivalent service or tool support, particularly for Cosmos DB, which has a unique API structure.

What to Watch For:

• Schema and datatype mismatches
• API differences for NoSQL databases
• Data consistency and latency issues

Solution:
For relational databases like Azure SQL to Cloud SQL or AlloyDB, use Database Migration Service (DMS). For NoSQL options like Cosmos DB, consider transforming data models to align with Firestore or Bigtable. Thorough testing and validation is critical for business-critical applications.

Final Thoughts

Migrating from Azure to Google Cloud can be a strategic move for businesses looking to optimize costs, leverage advanced services, or diversify cloud providers. However, ignoring the compatibility challenges can lead to performance degradation, data loss, or security risks.

A well-planned migration strategy, guided by expert consultation and thorough testing, can make this transition successful. Start with an assessment phase, leverage native and third-party tools, and ensure that your IT team or partner understands both cloud environments deeply.

For expert support, consider working with a trusted provider of Cloud Migration Services. At Exinent, we offer end-to-end solutions tailored to your cloud journey—from strategy to execution—ensuring a smooth and cost-effective migration to Google Cloud.

Ready to move from Azure to Google Cloud with confidence? Contact us today to start your cloud migration journey with Exinent.