What Is Mainframe on Cloud?
Mainframe on cloud refers to the integration or migration of traditional on-premise mainframe systems into cloud environments. This approach combines the stability, processing power, and scalability of mainframe systems with the flexibility and cost-effectiveness offered by cloud platforms. By bridging these two technologies, enterprises can optimize workloads, improve productivity, and future-proof legacy infrastructure without entirely abandoning mainframe solutions.
Mainframe to cloud integration is a strategic move driven by businesses’ need to modernize their IT ecosystems. It enables access to modern tools, promotes innovation, and allows better utilization of existing infrastructure. Additionally, with global trends favoring flexibility and decentralization in computing, merging mainframes with the cloud ensures businesses remain competitive in rapidly evolving markets.
This is part of a series of articles about mainframe modernization
Key Statistics and Drivers of Mainframe to Cloud Integration
According to IBM’s Mainframe Digital Transformation report, mainframes continue to play a critical role in enterprise IT strategies, even as organizations shift toward cloud environments:
- Prevalence of mainframes: 43 of the world’s top 50 banks and 8 of the top 10 payment companies still rely on mainframes as core computing platforms.
- Future of mainframe applications: 78% of executives acknowledged that mainframe-based applications will remain integral to their digital strategies—an increase from 66% in 2021 and 64% in 2023.
- Total cost of ownership: 75% of over 2,500 global IT executives said mainframes are equal to or better than cloud solutions in terms of total cost of ownership.
- Hybrid environments: 88% of executives believe modernizing mainframe applications for hybrid environments is crucial to long-term business success.
- Integration of AI with mainframes: 78% of IT executives report that their organizations are piloting or running AI initiatives on mainframes, 84% consider AI integration into mainframe transactions important, and 79% view mainframes as essential for enabling AI-driven value creation.
Generative AI for modernization: 61% of executives see generative AI as an important tool for modernizing applications and improving developer productivity in mainframe environments.
Advantages of Mainframes in a Cloud Environment
High Resilience and Availability
Mainframes are known for their ability to handle high availability and maintain consistent performance under heavy workloads. Hosting mainframes within a cloud infrastructure further enhances resilience by ensuring redundancy across regions, reducing the risk of downtime. This resiliency is crucial for industries such as finance, healthcare, and retail that handle critical or time-sensitive data.
Cloud-based infrastructures also ensure system availability through real-time failover mechanisms and distributed architectures. By combining the innate robustness of mainframes with dynamic cloud capabilities, enterprises gain a dependable IT environment, crucial for uninterrupted operations. These scalable setups further bolster fault tolerance and disaster recovery practices, keeping businesses prepared for potential contingencies.
Security Capabilities
Mainframe systems have always emphasized security, ensuring data integrity through encrypted storage, access controls, and real-time monitoring. Moving mainframes to cloud environments fortifies these features by layering in multifactor authentication, threat detection, and compliance tools. For organizations dealing with sensitive transactions, this pairing strengthens their cybersecurity posture.
Cloud providers often integrate privacy-enhancing technologies like zero trust policies and encrypted data transmission channels. Such measures further complement the already strong mainframe security framework. Businesses gain enhanced control and transparency over their data while remaining compliant with international data protection laws like GDPR and HIPAA.
Optimized Transaction Handling
Mainframes are optimized to handle millions of transactions per second, a capability that remains invaluable in sectors like banking and e-commerce. Migrating these functions to the cloud amplifies this efficiency by distributing workloads across elastic resources, ensuring scalability during peak usage. Furthermore, the transition reduces operational bottlenecks, enabling smoother transaction processing.
This combination also reduces maintenance overhead by utilizing cloud-native orchestration tools. Enterprises can manage transaction-heavy processes without constant human intervention. Additionally, smarter load balancing across cloud regions ensures that operations are completed seamlessly, maintaining customer satisfaction regardless of demand cycles.
Modern Workloads and Scalability
Modernizing mainframes in the cloud introduces the flexibility required to handle diverse workloads beyond traditional transactional tasks. Businesses can integrate AI models, big data platforms, and real-time analytics seamlessly with their legacy systems, driving innovation across operational areas. This integration breathes new life into core mainframe workloads while opening new avenues for digital transformation.
Cloud scalability ensures resources grow alongside increasing workload demands, eliminating the need for recurrent infrastructure upgrades. Whether scaling horizontally or vertically, the dynamic adjustment of resources helps enterprises remain cost-efficient while ensuring growing operational demands receive adequate computing capacity.
Approaches to Mainframe Modernization in the Cloud
Rehosting
Rehosting, often referred to as “lift and shift,” involves migrating mainframe workloads—including COBOL, JCL, CICS, or IMS applications—to cloud environments with minimal to no changes in application code. This typically includes rehosting the mainframe runtime environment on virtual machines or emulation platforms that replicate the original mainframe operating environment (such as z/OS or z/VSE) on x86-based cloud infrastructure.
For mainframes, rehosting preserves business-critical logic and application behavior while eliminating reliance on expensive, proprietary hardware. It enables organizations to quickly retire aging mainframe infrastructure, reduce operational costs, and centralize workload management within cloud ecosystems.
Replatforming
Replatforming involves modifying components of mainframe applications to better align with cloud-native environments, while preserving business logic. This may include switching legacy databases to cloud-based relational or NoSQL systems, replacing proprietary middleware with open-source alternatives, or containerizing workloads for deployment in Kubernetes.
For mainframes, replatforming is often used to modernize high-throughput batch processing or transaction systems without rewriting COBOL or PL/I code entirely. This enables organizations to adopt modern CI/CD pipelines, integrate with cloud APIs, and support dynamic scaling. The result is improved system performance and maintainability, along with better integration with cloud services such as data lakes, analytics platforms, and AI engines.
Refactoring
Refactoring transforms legacy mainframe applications into cloud-native architectures by rearchitecting the software from the ground up. This typically involves rewriting COBOL, PL/I, or Assembler code in modern languages like Java, Go, or Python, and decomposing monolithic applications into microservices or event-driven functions.
In mainframe environments, refactoring is used to retire outdated frameworks and unlock cloud-native features such as serverless execution, streaming analytics, and AI model integration. These changes improve agility, support faster development cycles, and enhance responsiveness to business needs. While refactoring requires substantial investment in time, skills, and planning, it unlocks the highest potential for innovation, agility, and long-term ROI.
Hybrid Approach
A hybrid approach combines on-premise mainframes with cloud infrastructure, enabling selective offloading of workloads. Core systems of record and latency-sensitive applications often remain on the mainframe, while analytics, mobile interfaces, customer-facing portals, or integration services run in the cloud.
This model is particularly effective for financial institutions or government bodies where uptime, compliance, and data residency are paramount. It allows these organizations to retain control over critical systems. Hybrid architectures also support gradual modernization. Teams can implement APIs to expose mainframe functions to cloud apps or use data replication tools to mirror mainframe data in cloud warehouses.
Challenges of Mainframe to Cloud Integration
Migration Risks and Costs
Migrating mainframe workloads to the cloud involves significant upfront planning and investment. Large-scale systems often have interdependent applications and complex legacy code, making full assessment and migration a high-risk endeavor. Missteps in the process can result in service disruptions, data loss, or compliance violations.
Costs include not only cloud service fees and new tools but also the resources needed for testing, validation, and rollback plans. Additionally, legacy licensing constraints or proprietary architectures can complicate migration and inflate costs. Without a clearly defined strategy, the financial and operational risks can outweigh the intended benefits.
Learn more in our detailed guide to mainframe migration
Workforce Skill Gaps
One of the most persistent barriers to mainframe modernization is the scarcity of professionals who are skilled in both legacy systems and modern cloud technologies. Mainframe expertise is becoming rare, while cloud specialists may lack understanding of legacy application architectures, leading to gaps in execution.
This talent mismatch can hinder modernization progress, inflate project timelines, and increase dependency on external consultants. Organizations must invest in upskilling existing staff or onboarding hybrid-skilled personnel who can bridge the gap between traditional and modern IT practices.
Technical Risks and Performance Issues
Mainframes are optimized for high-throughput, low-latency processing. Replicating this level of performance in cloud environments can be challenging due to differences in architecture, data transfer bottlenecks, or latency in multi-region deployments.
Applications refactored for the cloud may face performance degradation if not carefully designed, especially when moving from tightly coupled mainframe environments to distributed cloud-native frameworks. Ensuring seamless integration, stable transaction handling, and secure data flows demands robust testing, monitoring, and optimization mechanisms to prevent service degradation or security vulnerabilities.
Best Practices for a Successful Mainframe to Cloud Transition
Inventory and Dependency Mapping
A successful migration begins with a clear understanding of the existing mainframe landscape. Organizations must conduct a detailed inventory that includes applications, databases, transaction workflows, system utilities, batch jobs, middleware, and network interfaces. This process should capture technical specifications, business functions, uptime requirements, and performance baselines.
Dependency mapping goes beyond application connections. It identifies which components rely on shared data sets, how scheduling dependencies impact operations, and what inter-process communications exist. Using tools for static code analysis and system modeling can help uncover hidden dependencies and undocumented processes.
By mapping out these relationships, teams can accurately assess the complexity of migration, identify components that can be decoupled or phased out, and define the scope for refactoring. This foundational step reduces the risk of migration failures due to missed connections or overlooked business logic.
Use Generative AI to Modernize Legacy Code
Generative AI can accelerate mainframe modernization by automating the translation of legacy languages (such as COBOL or PL/I) into modern equivalents like Java, Python, or C#. These models are trained to understand legacy syntax, patterns, and idioms, and can produce equivalent high-level code that integrates with cloud-native architectures.
Additionally, AI-assisted code generation can flag obsolete constructs, suggest more efficient algorithms, and restructure monolithic functions into modular, cloud-friendly components.
Beyond code translation, generative AI can support reverse engineering efforts by analyzing legacy systems and producing architectural diagrams, API documentation, or business logic descriptions. These capabilities make it easier to identify decoupling opportunities, document undocumented workflows, and design microservices or serverless replacements.
Phased Implementation
Phased implementation allows organizations to manage risk and gather operational insights progressively. Instead of moving all workloads simultaneously, teams can pilot migration with selected applications—often those with lower criticality or fewer dependencies.
Each phase should follow a structured lifecycle: planning, testing, migration, validation, and post-migration support. Lessons learned in early phases should inform the approach to subsequent phases. For example, unexpected data transformation issues or integration failures in phase one can be mitigated in phase two with improved tooling or procedures.
This approach also gives stakeholders time to adapt, facilitates parallel development of cloud-native services, and allows for iterative improvements. Moreover, phased execution supports fallback strategies—ensuring that problematic migrations can be paused or reversed without jeopardizing the overall system.
Ensure Data Integrity and Security
Data is one of the most valuable assets in mainframe systems, and protecting its integrity during migration is critical. Every transfer should include validation checkpoints such as checksums, hash comparisons, and automated reconciliation scripts to ensure data completeness and accuracy.
Security must span both technical and procedural dimensions. Transport layer security (TLS) should be enforced for all data in transit. At rest, encryption mechanisms like AES-256 should be implemented along with role-based access controls. Identity federation services such as SAML or OAuth 2.0 can be used to centralize and secure user authentication in hybrid environments.
Beyond technical controls, organizations must update security policies to reflect new risks introduced by cloud platforms, including shared responsibility models and cross-region compliance requirements. Auditing and logging mechanisms should be in place to track access and changes, which are essential for forensic investigations and regulatory compliance.
Monitor and Optimize Post-Migration
Post-migration success depends on continuous monitoring and optimization. Organizations should deploy observability stacks—such as Prometheus, Grafana, or cloud-native tools like AWS CloudWatch and Azure Monitor—to track system metrics, logs, and traces in real-time.
Key performance indicators (KPIs) should be defined for availability, latency, throughput, and error rates. Alerts should be configured to notify teams of anomalies such as performance degradation, API failures, or unexpected usage spikes.
Optimization involves analyzing metrics to fine-tune configurations, remove inefficiencies, and scale resources dynamically. For instance, underutilized virtual machines can be rightsized or transitioned to serverless functions. Databases can be indexed or partitioned based on access patterns identified in logs.
This feedback loop ensures that the system remains aligned with business needs, maintains cost-efficiency, and supports continuous improvement in performance and user experience.
Related content: Read our guide to mainframe to cloud migration
