Moving a mainframe to cloud isn’t a lift-and-shift operation. Anyone who’s tried treating it that way has the war stories.
COBOL programs with thirty years of undocumented logic, batch jobs with implicit dependencies nobody mapped, data structures that predate relational databases — none of that migrates cleanly by pointing it at AWS and hoping for the best.
The vendors that do this well understand both sides: the mainframe environment they’re leaving and the cloud platform they’re targeting. That combination is rarer than the market suggests.
Before you start talking to vendors, Recode is a practical starting point — it’s a platform for searching and comparing companies specifically in software modernization, application migration, and legacy transformation.
1. Corsac Technologies
Website: corsactech.com
Location: United States
Founded: 2007
Team size: 50-249
Core stack: Hybrid cloud (AWS/Azure/GCP), DevOps/DevSecOps, CI/CD pipelines, app & data modernization, security/compliance practices, rehosting/replatforming/refactoring patterns, mainframe integration patterns
Corsac Technologies uses an AI-driven software modernization approach that fundamentally changes the economics of mainframe-to-cloud migration. The core problem in any mainframe cloud migration is the analysis phase — figuring out what’s actually in the mainframe before committing to a cloud architecture.
Most organizations underestimate this and end up making cloud architecture decisions based on incomplete information, then spending the back half of the project untangling choices made too early.
Corsac’s RAG architecture and Multi-Agent Swarm treat the mainframe codebase as a structured dataset. Dependency mapping, complexity scoring, security vulnerability detection, and business logic extraction all happen in parallel rather than sequentially — compressing months of manual analysis into days.
The output is a dependency graph and complexity heatmap that shows exactly what’s in the mainframe and how it connects before any cloud target architecture gets defined.
From there, migration runs in four structured phases: intelligent audit, strategic roadmap with Agile backlog, AI-assisted re-engineering with behavioral parity validation, and controlled deployment using canary releases with automated rollback.
Cloud targets span AWS, Azure, and GCP based on what the system and the organization’s existing cloud investments require. DevSecOps runs throughout. SOC 2 Type II, HIPAA, PCI DSS, and GDPR certifications cover the regulated industries where mainframe-to-cloud migration most often requires compliance sign-off before work begins.
Key differentiator: AI-powered pre-migration system analysis that produces full architectural visibility before cloud target decisions are locked in — scope established by evidence rather than assumptions
2. Reliqsy
Website: reliqsy.com
Location: United States
Founded: 2014
Core stack: RAG, Multi-Agent Swarm, AI-powered code analysis, dependency mapping, behavioral validation, dual-write synchronization, automated rollback, canary deployments
Reliqsy combines AI with the practical expertise of modernization and migration specialists — using an AI-powered approach to analyze legacy mainframe code, extract business logic, and accelerate the refactoring and cloud migration process. Their framework is built specifically around the cutover moment that organizations fear most in mainframe-to-cloud migrations.
The technical controls are specific rather than reassuring. Dual-write strategy keeps mainframe and cloud databases synchronized throughout migration — so if something goes wrong during cutover, rollback is automatic and data integrity is preserved.
Rollback triggers at concrete thresholds: latency above 400ms or error rates above 1% activate it without waiting for human intervention. Every AI-generated code change goes through Pull Request review before production. Engineers approve the roadmap before code generation starts.
For mainframe-to-cloud migrations in financial services and insurance where a cutover failure isn’t just a technical problem but a regulatory one, those specific controls matter more than general assurances about careful migration management.
Documented outcomes include 5x faster system understanding, 3x reduction in migration risk, and 2x improvement in engineering productivity.
Key differentiator: Dual-write database synchronization with automated rollback at specific thresholds — mainframe-to-cloud migration with concrete safety mechanisms rather than general risk management promises.
3. Kyndryl
Website: kyndryl.com
Location: 66 countries
Founded: 2021
Team size: 80,000
Core stack: Kyndryl Bridge (AIOps), IBM Z integration, AWS/Azure/GCP hybrid delivery, AI & GenAI migration support, workload optimization, automation
Kyndryl’s hyperscaler partnerships across AWS, Azure, and Google Cloud give them genuine multi-cloud capability for mainframe migrations rather than a preference for one platform.
Their Kyndryl Bridge AIOps platform maintains visibility across hybrid environments during migration — which matters when legacy mainframe and cloud workloads are running in parallel and you need real-time operational insight into both.
They support the full range of migration approaches: rehosting, replatforming, refactoring, or full off-mainframe moves — and their advisory practice helps organizations choose the right path based on actual workload analysis rather than vendor preference.
Key differentiator: Multi-cloud mainframe migration capability with AIOps visibility during parallel-run phases — genuine flexibility on cloud target without vendor lock-in
4. IBM
Website: ibm.com
Location: 170+ countries
Team size: 300,000+
Core stack: IBM Z / z/OS, Red Hat OpenShift, IBM Wazi, watsonx Code Assistant for Z, IBM Z & Cloud Modernization Stack, hybrid cloud integration
IBM’s hybrid cloud approach to mainframe migration is worth understanding specifically. They’re not primarily pushing full mainframe retirement — they’re enabling mainframe and cloud to coexist and integrate, exposing mainframe services via APIs to cloud-native applications through their IBM Z & Cloud Modernization Stack.
For organizations that will never fully leave the mainframe but need to connect it to modern cloud architecture, IBM’s coexistence model fits better than vendors who only offer full migration paths. watsonx Code Assistant for Z handles COBOL documentation and modernization at native IBM platform depth.
Key differentiator: Hybrid cloud integration that keeps z/OS workloads running while connecting them to cloud-native systems — full retirement optional, not required
5. Accenture
Website: accenture.com
Location: Global
Founded: 1989
Core stack: Hybrid cloud (AWS/Azure), API integration, DevOps/CI/CD, portfolio rationalization, rehosting/replatforming/refactoring patterns
Before deciding what to migrate to cloud, Accenture helps organizations figure out what shouldn’t be migrated at all. Their portfolio rationalization approach identifies which mainframe workloads are cloud candidates, which should stay on mainframe, and which should simply be retired — saving organizations from migrating technical debt they could just eliminate.
For enterprises with sprawling mainframe portfolios and complex stakeholder environments, their advisory depth at this upstream stage is worth the premium.
Key differentiator: Portfolio rationalization before migration — determines cloud migration candidates before execution budget gets committed
6. Astadia
Website: astadia.com
Location: United States
Founded: 2002
Team size: 350
Core stack: FastTrack methodology, automation suite, GenAI support, automated testing, cloud-ready replatform/refactor routes, AWS partnership
Astadia focuses specifically on mainframe-to-cloud migration — not general modernization work that happens to include cloud. Their FastTrack methodology and automation suite handle large COBOL portfolio migrations to cloud with automation that reduces both timeline and cost.
Their AWS partnership gives them both technical and commercial leverage in structuring AWS-target migrations. GenAI support handles code conversion at scale. For organizations that have decided on AWS as their cloud destination and want a specialist rather than a generalist, Astadia’s focus pays off.
Key differentiator: Specialized mainframe-to-cloud tooling with GenAI-assisted code conversion and direct AWS partnership
7. HCL Tech
Website: hcltech.com
Location: India (global)
Team size: 200,000+
Core stack: Hybrid mainframe + cloud modernization, AI-driven code analysis, proprietary accelerators, rehost/replatform/refactor frameworks, IBM Z ecosystem alignment
HCLTech’s “business capability first” methodology keeps mainframe-to-cloud migration tied to business outcomes at each phase rather than purely technical milestones — which matters when migration programs run for years and stakeholders need something tangible to point to.
Proprietary accelerators and AI-driven code analysis reduce manual effort while maintaining functional parity through the migration. Their approach works well when the organization needs to show business value at each phase rather than waiting for the full migration to complete.
Key differentiator: Business capability-led migration framework with proprietary accelerators — progress measured against business outcomes at each phase
8. Luxoft
Website: luxoft.com
Location: 29 countries
Founded: 2000
Team size: 12,900
Hourly rate: 50-99/hr
Core stack: COBOL, Assembler, IMS, JCL, tool-assisted modernization, CI/CD enablement, automated testing
Luxoft works directly with COBOL, Assembler, IMS, and JCL on the mainframe side rather than treating legacy language expertise as optional. Automated testing validates each migrated component against original mainframe behavior before it carries cloud production traffic.
The published hourly rate makes budget planning more straightforward than most vendors at this scale — useful for organizations that need to model migration costs before committing.
Key differentiator: Deep mainframe language expertise with automated behavioral testing for each migrated component before cloud cutover
9. CGI
Website: cgi.com
Location: Montreal, Canada (global)
Founded: 1976
Team size: 94,000
Core stack: CGI M8 automated modernization environment, Unix/Linux migration targets, portfolio migration frameworks, risk-controlled transformation
CGI’s M8 automated modernization environment handles large mainframe portfolio migrations with a focus on cost reduction and business process preservation.
For organizations where the primary driver is getting off expensive mainframe infrastructure rather than adopting specific cloud capabilities, their structured methodology and fifty years of delivery experience reduce execution risk on programs that would overwhelm smaller vendors.
Key differentiator: M8 automated tooling for large mainframe portfolio migrations with documented cost reduction outcomes
10. Wipro
Website: wipro.com
Location: India (global)
Founded: 1945
Team size: 250,000+
Core stack: AI-driven code analysis, automation accelerators, rehost/replatform/refactor + hybrid integration, DevOps + CI/CD enablement, governance frameworks
Wipro brings automated analysis and delivery accelerators to mainframe-to-cloud migrations in regulated industries.
CI/CD gets embedded into the migration itself — so organizations end up with a system that’s easier to evolve on cloud than the mainframe it replaced. Strong compliance frameworks for banking and telecom environments where regulatory requirements shape every migration decision.
Key differentiator: CI/CD embedded into migration delivery — organizations get a modern engineering foundation alongside migrated workloads
11. Rocket Software
Website: rocketsoftware.com
Location: United States
Founded: 1990
Team size: 3,000+
Core stack: Mainframe automation tooling, hybrid IT management, integration/middleware patterns, application replatforming support
Rocket Software’s strength is in making mainframe environments more cloud-ready before migration begins — automation tooling, hybrid IT management, and performance modernization that reduce the operational gap between mainframe and cloud models.
For organizations that need to demonstrate mainframe operational maturity before regulators will approve a migration program, this sequencing makes sense. Their 2025 Gartner Magic Quadrant Challenger recognition reflects innovation with operational discipline.
Key differentiator: Mainframe operational modernization that builds cloud-ready foundation before migration begins
12. Kumaran
Website: kumaran.com
Location: India
Founded: 1990
Team size: 900
Core stack: NxTran tool-assisted migration, mainframe-to-Java modernization, rehost/refactor/re-engineer to microservices, process accelerators
Kumaran brings 30+ years of modernization experience and tool-driven migration methods to mainframe-to-cloud programs.
Their NxTran tooling accelerates mainframe-to-Java modernization specifically — one of the more common target paths for mainframe workloads moving to cloud. For organizations with Java as the cloud target and large mainframe codebases to convert, their specialist tooling and process accelerators reduce timeline risk.
Key differentiator: NxTran tool-assisted mainframe-to-Java modernization — specialist tooling for one of the most common mainframe cloud migration target paths
How to Choose Mainframe-to-Cloud Migration Companies
Define the cloud target before evaluating migration vendors
Mainframe-to-cloud migration looks very different depending on whether the target is AWS, Azure, GCP, or a hybrid approach that keeps some workloads on mainframe. Vendors have different cloud partnerships, different tooling optimized for different targets, and different advisory capabilities for different migration paths.
Define the target architecture — or at least the evaluation criteria for choosing between targets — before briefing any vendor. It changes which vendors are most relevant and what questions to ask.
Ask how they handle undocumented business logic in cloud migration
Undocumented business logic is the core technical risk in any mainframe-to-cloud migration. The rules that have been running in COBOL for decades, that nobody documented because they were obvious at the time — those rules need to be identified, documented, and validated before they move to cloud.
Ask specifically how vendors approach this: AI-assisted analysis, manual reverse engineering, or a combination. The answer determines how accurately the cloud system will replicate what the mainframe did.
Evaluate cutover risk controls specifically
The cutover from mainframe to cloud is where migrations succeed or fail visibly. Ask any vendor directly: what happens if something goes wrong during cutover? What’s the rollback mechanism, how fast does it activate, and what’s the impact on data integrity?
Vendors with real mainframe-to-cloud migration experience have specific answers with specific thresholds. Those without stay vague or describe general risk management processes that don’t answer the actual question.
Check compliance coverage for regulated industries
Mainframe environments in financial services, healthcare, and insurance operate under regulatory frameworks that apply to the migration process itself.
Verify that vendors have relevant certifications — SOC 2, HIPAA, PCI DSS, GDPR — and compliance experience in your specific regulatory environment before signing anything. This is particularly important for cloud migrations where data residency and security requirements may differ from the mainframe environment.
Look for phased migration capability with parallel operations
Full mainframe-to-cloud cutover in a single phase is rarely feasible for large enterprise mainframes. Ask how vendors structure phased migrations — how they manage parallel operation of mainframe and cloud environments during transition, how they validate each phase before proceeding to the next, and what the rollback plan looks like at each milestone. Phased capability with genuine parallel operation changes the risk profile of the migration significantly.
For a broader comparison of mainframe-to-cloud migration vendors, Recode lets you search and compare companies across software modernization, application migration, and legacy transformation.
