Mainframe
COBOL to Java, C#
& Cloud
Many financial institutions continue to depend on IBM mainframes for core banking and transaction processing. While these platforms are stable and highly reliable, they incur substantial annual operating costs driven by software licensing, MIPS-based pricing models, and specialised infrastructure support.
When defining a modernization strategy, organizations typically evaluate two primary paths:
Each approach carries materially different cost structures, operational responsibilities, and long-term vendor exposure.
AWS provides a managed runtime model that reduces on-premise infrastructure management, but long-term expenditure remains dependent on sustained cloud consumption and service pricing. SoftwareMining delivers automated COBOL to Java translation with full deployment flexibility, allowing organizations to run on-premise or in any cloud environment while retaining architectural control and predictable annual support costs.
The table below compares key financial and operational factors across IBM mainframe, AWS Mainframe Modernization, SoftwareMining, and IBM watsonx approaches for a representative mid-sized system.
The comparison focuses specifically on translation and runtime cost differentials. In most modernization programmes, system testing and business validation represent the largest cost component, often exceeding translation costs by a multiple.
The following comparison provides a structured overview of the principal cost and operational dimensions associated with each approach.
| Category | IBM Mainframe (CICS-based) | SoftwareMining - Automated COBOL to Java | AWS Mainframe Modernization | IBM watsonx-based Modernization |
|---|---|---|---|---|
| Translation / Modernization Fee | Not applicable (no modernization) |
Per-line pricing based on application size
(total programme cost typically driven by testing and validation) |
Modernization service with cloud-based pricing model
(translation cost generally small relative to testing and validation) |
AI-assisted modernization with model and token-based pricing
(AI generation cost typically minor compared to testing and validation) |
| Typical Annual Runtime Cost |
Often exceeds $1M annually for mid-sized systems
(CICS, MIPS consumption, software licensing, hardware support) |
Typically 10 to 20 percent of comparable mainframe runtime cost
(client-managed hosting; annual ASM support only) |
Combination of modernization software and cloud infrastructure,
typically 30 to 70 percent of mainframe runtime cost
(dependent on workload profile and AWS services utilised) |
Typically 25 to 75 percent of mainframe runtime cost,
depending on AI model usage and cloud infrastructure
(model calls, storage, hybrid runtime components) |
| Cloud Hosting and Runtime Flexibility | Dedicated mainframe infrastructure | Client-controlled deployment: on-premise, private cloud, or public cloud | Runs within AWS cloud ecosystem | Primarily IBM Cloud or hybrid configurations |
| Support Model | Vendor-managed enterprise SLAs | Annual SLA-based support for translation tools and runtime libraries | AWS support tiers and partner-based services | IBM Cloud support tiers and standard enterprise SLAs |
| Cost Predictability | High fixed cost (MIPS-based pricing) | Defined per-LOC translation + predictable annual ASM tiers | Variable (usage-based cloud billing) | Variable (model usage, token consumption, cloud runtime) |
| Vendor Lock-in Risk | Very high (platform dependency) | Low (portable Java runtime, minimizing vendor lock-in) | High (cloud ecosystem dependency) | Medium to High (IBM cloud and AI service dependency) |
The values shown represent approximate industry ranges for comparison purposes only. Actual IBM and AWS pricing varies based on licensing agreements, workload characteristics, and cloud consumption patterns.
While cloud modernization offers clear benefits, it is important to consider long-term lock-in risks. AWS Mainframe Modernization typically results in applications that rely on AWS-native orchestration, storage, and runtime components. IBM watsonx, although described as hybrid-cloud capable, depends on IBM Cloud services, AI runtime models, and proprietary data pipelines. Transitioning an AI-assisted or cloud-managed system to another provider may require substantial rework, retesting, and architectural redesign.
In contrast, SoftwareMining produces structured Java code designed to run on any Java-compliant platform. The translated application depends on standard Java together with a defined set of SoftwareMining runtime libraries delivered as part of the project, rather than on cloud-native services or proprietary platform features.
Hosting is fully client-controlled, whether on-prem, private cloud, or public cloud. Annual ASM support covers the translation tool and runtime libraries, but there is no usage-based runtime licensing and no requirement to run on a specific cloud provider. This structure provides organizations with greater control over infrastructure strategy, cost management, and long-term architectural direction compared with mainframe-bound or cloud-managed modernization options.
Translation typically represents a relatively small portion of total modernization expenditure. For a medium size system, testing effort can exceed translation cost by a factor of five to ten.
High translation accuracy and predictable runtime behavior are more important than small differences in per-line pricing. Any change in libraries, generated code, or runtime behavior increases test cycles and project risk.
SoftwareMining's methodology has been validated across multiple large-scale modernization programmes. One major financial organization validated the generated Java code with more than 2 billion transactions. The process is secure because no COBOL code or business data leaves the client site, and all work is carried out by the client's own staff or their chosen system integrator. This ensures full control, predictable outcomes, and a safe modernization path.
Ultimately, modernization outcomes are determined more by execution confidence and testing certainty than by marginal differences in per-line pricing.
COBOL modernization costs vary based on the chosen approach. IBM mainframes carry high annual runtime and licensing charges. AWS Mainframe Modernization shifts cost to usage-based cloud billing. SoftwareMining uses a per-line pricing model with predictable support tiers. For a medium size system, the overall cost profile differs across these options, especially when long-term testing and platform dependency are included.
SoftwareMining uses a transparent per-line pricing model. The total modernization cost depends on the size of the application, the number of COBOL lines of code, and the level of support required. This makes budgeting straightforward for medium and large systems.
The generated Java code is portable and runs on any Java-compliant platform. The application relies only on standard Java and a small, stable set of SoftwareMining runtime libraries that are delivered with the project. No source code or business data leaves the client environment during translation.
Annual ASM support provides updates and maintenance for the translation tool and runtime libraries without introducing usage-based runtime licensing or cloud dependency. This deterministic process reduces regression testing effort and avoids the vendor lock-in common in cloud-managed modernization solutions.
SoftwareMining generates predictable and consistent Java code, avoiding the variability found in automated or AI-driven tools. This greatly reduces regression testing effort, which is the main cost in most modernization projects.
The result is a portable application that runs on any Java platform with a small, reliable library set, giving organizations freedom from cloud or vendor lock-in and better long-term cost control.
For a mid-sized enterprise workload, IBM mainframe runtime frequently reaches the high six to low seven figure range annually. AWS Mainframe Modernization often lowers on-premise infrastructure costs, but annual cloud runtime and storage usually fall into the mid six figure range depending on workload and usage.
AWS Mainframe Modernization also introduces platform dependency. Migrated applications rely on AWS-native components such as the managed mainframe-compatible runtime, IAM security model, CloudFormation infrastructure, and AWS data and storage services. These features simplify deployment but tightly couple the system to the AWS ecosystem.
The main lock-in factors include:
In practice, moving an AWS-modernized system to another cloud or back on-premise can require major redesign and retesting. Organizations should include these long-term factors when evaluating total modernization cost and vendor strategy.
IBM watsonx Code Assistant supports incremental modernization by generating Java components alongside existing mainframe structures. Costs depend on AI model usage, cloud resources, and the volume of COBOL processed.
For a medium size system, organizations typically incur model consumption fees together with the ongoing cost of running a hybrid environment that still depends on mainframe components. This approach can reduce initial effort but is not a full mainframe exit.
The hybrid model also increases long-term testing and integration effort, since both the Java layer and the retained mainframe components must be validated together. In addition, reliance on IBM cloud services and AI runtime pipelines can lead to vendor lock-in compared to a deterministic COBOL to Java conversion that produces fully independent, portable code.
In many projects the largest cost is regression testing. A medium size system can include many connected business processes, and proving functional equivalence may require several test cycles that exceed translation cost.
