Mactores is the agent-native AWS modernization firm. Your COBOL, JCL, CICS, and Db2 estate lands on AWS, the business keeps running, and the MIPS you're paying for get released on a day you already know, because that day is written into the contract alongside a fixed fee. That's how our mainframe modernization services are sold: the way a CFO wishes every IT program were.
If we're the reason the date slips, the extra cost lands on our side of the ledger, not yours. That clause is in the statement of work, in plain English, before a single line of code moves.
Talk to us
The mainframe has had its decades. Give it a last day.
Bring your MIPS report and your contract renewal date, and we'll sketch what a dated exit looks like.
60–70%
Hours of discovery, conversion, and test work carried by agents rather than by an analyst bench
94%
Share of Mactores engagements that landed on the committed date or earlier
90 days
Production warranty on every deliverable we ship
7
Consulting competencies granted by AWS, one of them for migration work
2008
First year of production work on AWS
A mainframe to cloud migration rarely dies in the architecture review. It dies eighteen months later, in a conference room, when someone asks whether the nightly settlement batch on the new platform produces the same totals as the old one, and nobody can prove it does.
The tooling got better. The failure rate didn't.
Gartner predicts that more than 70% of mainframe exit projects initiated in 2026 will fail to produce the intended benefits, because buyers overestimate what generative AI conversion tools can do on their own. We've watched that play out. Converting COBOL is the easy part. Proving the converted system behaves identically, inside your batch windows and in front of your regulators, is where the money and the months go.
Code can convert in weeks. Then the parallel run starts, the first control-total mismatch shows up in a packed-decimal field, and the program slides into an open-ended testing phase that has no exit criteria.
Every month of dual running means paying monthly license charges, ISV fees priced by MIPS, and the new AWS footprint at the same time. Nobody owns the date when the first bill stops.
The COBOL developers who understand why a claims rule fires on the third Tuesday are retiring or already gone. If their knowledge isn't captured and signed off, it gets rediscovered in production, usually by a customer.
Every one of these is avoidable, and not with a better conversion tool. It takes an exit date, a written definition of "identical," and a rulebook signed by the people who actually know the system.
Put a date on the exit
Tell us which renewal, refresh, or retirement is driving the timeline.
We'll show you the waves, the proof points, and the date they add up to.
A contracted exit date moves the risk of a mainframe program from your budget to the delivery firm's. It forces agreement on scope, on what counts as identical output, and on the decommission day before any code converts, and it gives the firm a financial reason to find every hidden dependency early.
Most mainframe migration services are sold so the vendor can't lose. The estimate is a range, the testing phase is "as required," and the meter keeps running until the mainframe is actually off. If that takes an extra year, the proposal still worked for the people who wrote it.
Our economics don't depend on that meter, and the reason is how the work gets done. On a Mactores program, agents we've built and hardened over years of production cutovers read the COBOL, trace JCL job streams, extract business rules, map copybooks to schemas, generate test cases, and run the comparisons. Stack that against a conventional bid for the same estate, and the agents are covering 60 to 70 percent of its hours. You don't buy a platform from us, and nothing we leave behind needs our tooling to keep running.
Judgment stays with people. Our FDEs work inside your team, in your standups and your war room, and they make the calls that decide the outcome: which workloads refactor and which retire, how the waves are sequenced, what counts as equivalent, and when a cutover is safe. Nobody hands your program to a different crew once the ink dries.
Agents · 60–70% of hours: Code reading, job-stream tracing, rule extraction, copybook mapping, test generation, comparison runs
FDE judgment: Workload disposition, wave sequencing, equivalence rules, cutover and rollback calls
Your team: Mainframe access, SME review of extracted rules, business sign-off at every gate
| How mainframe exits are usually sold | What you get in writing from Mactores |
|---|---|
| A multi-year range with "phase two" left open | One exit date, with the decommission of MIPS named in writing |
| Testing billed until someone feels comfortable | Equivalence criteria agreed per workload before conversion starts |
| Dual running paid for as long as it takes | A dual-run end date, with the cost of our overruns carried by us |
| Business rules "documented as needed" | Every extracted rule reviewed by your SMEs and handed over as a rulebook you keep |
| Rollback as a slide in the risk register | A rehearsed rollback path with triggers agreed before cutover |
"Automate the repeat work. Keep the judgment work for senior engineers. Everything the firm did in the next ten years followed from that one decision."
Here's the commercial rule, stated once. If a delay traces back to Mactores, Mactores pays for the extra work. The reverse holds too: when the hold-up is on your side, say an LPAR that isn't provisioned, test data that never shows up, or an SME pulled onto an audit, those hours are billed on a time-and-materials basis at the rate already in your contract. No surprises at the back end.
See the clause
The exit date, the fee, and the rule for who pays when the date moves.
The same language governs every legacy exit we put our name to.
Mactores' mainframe exit work covers portfolio discovery and rule extraction, a disposition for every workload, code and data conversion, batch and online replatforming on AWS, functional-equivalence testing, a rehearsed cutover, and decommissioning. Every workstream hands over something your team signs, so you track the program by what's been accepted, not by a vendor's percent-complete slide.
Leaving it. A good share of what ranks for mainframe modernization is about keeping workloads on IBM Z and modernizing in place: newer hardware, DevOps pipelines on z/OS, APIs in front of CICS. That's a legitimate choice for some estates, and Gartner itself recommends it for the largest ones. This page is for the other camp: organizations that want the machine's last day on a calendar, with the workloads already running on AWS well before it.
Agents inventory every program, copybook, JCL member, PROC, CICS transaction, Db2 table, VSAM file, and scheduler definition, then trace what calls what. Business rules come out in plain language for your SMEs to confirm or correct while they're still around to do it.
You get
A signed inventory and a rulebook your team owns.
Each application gets a path: refactor to Java, replatform on a COBOL runtime, reimagine as new services, replace with SaaS, retain for now, or retire. The reason for every call is written down, so nobody relitigates it in month six.
You get
A disposition register signed by your architecture lead.
COBOL and JCL convert with AWS Transform for mainframe while our agents pick up whatever it can't convert cleanly, and PL/I gets the same agent-assisted treatment under FDE review. Db2, VSAM, and IMS data move to Amazon Aurora PostgreSQL or Amazon RDS, with EBCDIC, packed-decimal, and REDEFINES handling settled field by field.
You get
Converted code and data, each object with comparison evidence and a green test run.
JCL job streams become orchestrated workflows on AWS Step Functions or the scheduler you already license, and CICS transactions become services on Amazon ECS or Amazon EKS. Both are sized against your real peak, not a vendor benchmark.
You get
A batch run that fits the window and online response times on record.
Production inputs are recorded on the mainframe, replayed on AWS, and compared output by output. Cutover follows only after the comparison classes are signed, with the rollback path rehearsed.
You get
An equivalence ledger and a go-live record.
Once the target is stable, capacity comes off the mainframe wave by wave, ISV contracts are terminated on schedule, and historical data is archived to Amazon S3 under your retention rules.
You get
A signed switch-off record and a list of closed contracts.
Mainframe exits are the hardest corner of our application modernization services, and they run under the same contract terms as the rest of that practice.
COBOL migration gets the headlines, but it's one row in a table of seven. The rows that tend to wreck schedules are further down.
| Mainframe component | What makes it hard | Where it usually lands on AWS |
|---|---|---|
| COBOL and PL/I programs | Decades of patched logic, dead code, and rules nobody wrote down | Java services from AWS Transform for mainframe and agent-assisted conversion, or COBOL kept on a replatform runtime |
| JCL batch and schedulers | Job dependencies, restart points, GDG datasets, and a batch window that can't grow | AWS Step Functions, AWS Batch, or an enterprise scheduler, with datasets on Amazon S3 or Amazon EFS |
| CICS and IMS TM online | Sub-second response expectations and pseudo-conversational state | Services on Amazon ECS or Amazon EKS behind Amazon API Gateway |
| Db2 for z/OS | Stored procedures, EBCDIC collation, and packed-decimal columns | Amazon Aurora PostgreSQL or Amazon RDS, loaded and kept in sync with AWS DMS |
| VSAM and IMS DB | Record layouts defined only in copybooks, often with REDEFINES | Relational tables in Aurora PostgreSQL, with hierarchical IMS segments mapped explicitly |
| Assembler, Easytrieve, and 4GLs | Low tool coverage and thin documentation | Rewritten by hand under FDE review, or retired where usage data says nobody needs them |
| MQ, file transfer, and print | Partners and downstream systems that expect exact formats and schedules | Amazon MQ, AWS Transfer Family, and output management rebuilt to the same contracts |
Send us your estate profile
MIPS, languages, data stores, and batch window: four numbers that shape the whole plan.
We'll come back with the waves we'd propose and the risks we'd price first.
Each mainframe workload gets its own path. Stable, well-understood COBOL often refactors to Java; code that must stay COBOL for now can replatform on a runtime; heavily changed areas are worth reimagining as new services; and anything usage data shows nobody touches should simply retire. One estate usually needs all four, sequenced in waves.
A single pattern for the whole estate is how programs overspend. Refactoring the batch job nobody has run in three years is money set on fire, and forcing a volatile claims engine through line-for-line conversion just moves the technical debt to Java. So the mainframe modernization strategy we sign is workload by workload, with the reasoning attached. If that portfolio decision is still open and you want an outside view before committing, our application modernization consulting practice runs the assessment on the same fixed terms.
| Path | What happens | When it fits | Watch out for |
|---|---|---|---|
| Refactor | COBOL and JCL converted to Java and orchestrated workflows | Stable logic, high transaction volume, a desire to hire Java rather than COBOL talent | Generated code that works but nobody wants to maintain; agree readability standards up front |
| Replatform | COBOL kept, recompiled, and run on a mainframe-compatible runtime on Amazon EC2 | A hard date, low appetite for code change, a later refactor planned | Runtime licensing that quietly recreates the cost you were escaping |
| Reimagine | Business rules extracted, then rebuilt as new services | Areas the business wants to change anyway, such as pricing or onboarding | Scope creep; the rulebook has to be signed before design starts |
| Replace | A SaaS or packaged product takes over the function | Commodity functions like general ledger or HR | Data migration and interface rework are still real work |
| Retire | The application is switched off and its data archived | Usage data shows no meaningful activity | Regulatory retention; archive before you delete |
| Retain | The workload stays on the mainframe for a defined period | A dependency that can't move until another wave lands | Needs an end date, or it becomes permanent |
A lot. AWS mainframe modernization tooling has been reshuffled, and many pages ranking for this topic haven't caught up.
AWS has closed its managed mainframe runtime to new customers, and its self-managed replatform experience followed. New replatform projects now run vendor-direct, with a licensed COBOL runtime on Amazon EC2, while AWS Transform for mainframe carries analysis, documentation, refactoring, and reimagining. AWS now offers the code conversion step in AWS Transform for mainframe refactor at no charge.
Most mainframe modernization solutions on the market still lead with the conversion tool. When the conversion itself costs nothing, what you're really paying a partner for is everything around it: the disposition calls, the data rules, the equivalence proof, and the cutover. That's where our FDEs spend their time.
Pressure-test the plan
Already have a refactor-or-replatform recommendation from someone else?
We'll review it workload by workload and tell you where the risk really sits.
Moving a mainframe estate to AWS runs in five gated stages: map and commit, prove the hardest wave, convert in waves, run in parallel against production, then cut over and release capacity. Your team signs off each stage before the next one opens, so the decommission date rests on accepted evidence rather than optimism.
Mainframe application modernization rarely stops at the mainframe's edge. Distributed Java or .NET systems that call CICS move alongside the waves under our software modernization services, so interfaces cut over on the same weekend instead of breaking a week later.
Agents read the codebase, SMF records, and scheduler definitions while FDEs build the wave plan and target architecture. Your SMEs confirm the extracted rules that matter most. The stage ends with a mainframe migration strategy you can defend, an exit date, and a fixed fee.
Phase exit
Signed: wave plan, equivalence rules, exit date, and fee.
We don't start with the easy stuff. The first wave includes the program everyone warns you about, the batch chain with the tightest window, and the data store with the worst REDEFINES. If the plan survives that, it survives the estate.
Phase exit
Hardest-wave results accepted; target architecture locked.
Applications move in dependency-ordered waves, so nothing on AWS waits on something still stuck on z/OS. Shared data stays synchronized in both directions during the transition, which keeps the business on one version of the truth.
Phase exit
Your team accepts each wave after watching it run on AWS.
Recorded production inputs replay on AWS every day, and every output gets compared against the mainframe's. The parallel run stops on its contracted end date, not when people run out of objections.
Phase exit
Equivalence ledger signed by the named business owners.
Cutover follows a runbook rehearsed at full volume, with a rollback path held open for the agreed window. Then mainframe capacity comes down, ISV contracts end, and the archive lands on Amazon S3.
Phase exit
Go-live signed; MIPS release and contract terminations logged.
Start with the proof wave
The hardest workload goes first, and the date gets signed once it lands.
You find out early whether the plan holds, while changing course is still cheap.
Proving a migrated system behaves exactly like the mainframe means recording real production inputs, replaying them on AWS, and comparing every output: datasets, database changes, reports, and online screens. Each difference gets classified and signed off against rules agreed in advance, and batch timing and response times are measured against the mainframe's own records.
Of all the mainframe migration challenges, this is the one proposals skip. "Functional equivalence" shows up on every proposal and gets defined on almost none. We define it in the contract, per workload, before conversion starts. That definition is what your auditors will ask for, and it's what stops the parallel run from turning into a permanent fixture.
| Proof | How it's measured | Signed by |
|---|---|---|
| Output equivalence | Recorded production inputs replayed on AWS; every dataset, table change, report, and screen classed as identical, equivalent by an agreed rule, or different | The business owner of each workload |
| Numeric fidelity | Packed-decimal and zoned fields compared at full precision, with rounding rules written down before the first run | Your finance or actuarial lead |
| Sort and collation | EBCDIC ordering checked wherever reports, keys, or downstream files depend on it | Your data owners |
| Batch window | Critical-path job timings compared with the mainframe's own SMF baseline, with a target agreed per chain | Your operations lead |
| Online response | CICS transaction response times under peak load compared with current service levels | Your application owner |
| Control totals | Record counts and hash totals reconciled on every run, for every file that leaves the system | Your controllers or auditors |
It rolls back, calmly, because the rollback was rehearsed. Changes made on AWS after go-live replicate back to the mainframe until the fallback window closes, so switching traffic back doesn't lose a single transaction. The triggers that force a rollback are agreed with your team beforehand, so nobody has to invent that decision in the middle of the night.
Define "identical" first
Ask for a sample equivalence ledger for your first wave.
Built from your own programs and batch schedule during scoping.
Mactores has retired a branded card payments platform built on mainframe-era logic, moved a capital-markets pricing engine without touching its valuation logic, and taken licensed legacy platforms off the books on fixed dates, all on AWS. The case studies are public, so you can read what shipped before you ever take a call with us.
Legacy logic in regulated finance is where these programs live or die, and it's where most of our proof comes from. These are the mainframe modernization case studies closest to that ground.
Application & Database · Payments
Retired
Legacy payments platform switched off
PCI DSS
Aligned, with zero audit incidents at cutover
Twice, this branded-payments leader had paid for discovery and never reached cutover. On the third attempt, agents absorbed the code and dependency analysis that had drained the earlier budgets, and the FDEs spent the engagement where regulated payments needs it: risk-weighted sequencing, a parallel-run exit plan the audit committee could sign, and production sign-off. Today the same payments logic serves customers from AWS, rebuilt cloud-native.
Read the case studyData Platform · Capital Markets
100%
No pricing breaks between old and new
Real time
Pricing freed from the nightly batch
A capital-markets firm had wanted real-time MBS pricing for years, but the audit and lineage work always turned the budget math against it. The pricing engine moved from Oracle to Aurora with its valuation logic intact, lineage is captured as the system runs, and the evidence lines up with FFIEC, SEC, and FINRA expectations. For a COBOL rating or pricing engine, keeping the math untouched is the whole job.
Read the case studyApplication & Database · Manufacturing
Fixed date
Migration landed on the day agreed
Renewal
Budget redirected to the next initiative
Synaptics had a licensed legacy database behind its design analytics and a renewal bearing down on it. The move to Amazon RDS ran on a fixed date without borrowing Synaptics' own product teams, and the money earmarked for the renewal went to the next initiative instead. A mainframe contract renewal is the same lever, only with more zeros on it.
Read the case studyData Platform · Internet & Software
Zero
Product disruption across a staged cutover
Declined → delivered
Prior partners had walked away from the job
Poshmark needed a consolidated AWS platform without pausing a live marketplace, and earlier partners had declined the job. The work moved in stages, with old and new running side by side until each piece proved itself, and shoppers never saw a disruption. Mainframe exits depend on that same coexistence discipline, because a bank can't pause either.
Read the case studyOnly one of these four started on mainframe-era logic. What all four share with a mainframe exit is the part that decides success: legacy logic or live traffic that had to behave exactly the same after the move, with somebody checking. We'll share mainframe-specific references privately, under NDA, whenever you're ready to compare notes with a peer.
Talk to a comparable reference
Tell us your industry and your core platform, and we'll find the closest engagement.
You'll speak with the people who lived through it, not a sales deck.
A mainframe exit stays compliant when controls move with the workloads rather than being rebuilt afterward. RACF rules map to AWS IAM and application roles, audit trails continue in AWS CloudTrail, change management stays unbroken for SOX, and the equivalence ledger becomes the evidence regulators and auditors review.
The mainframe's security model is decades old, and it's also battle-tested. RACF, ACF2, or Top Secret profiles encode who can touch what, and that logic has to survive the move intact. We map every profile and dataset rule to AWS IAM policies and application-level roles, and we run access reviews on the new side before anyone outside the project team gets in.
Payers and providers moving claims and eligibility systems off the mainframe can see how these controls hold up around protected health information in our healthcare data platform work, where PHI handling is part of the architecture from the first design review.
| Framework | What the exit has to preserve |
|---|---|
| SOX | Controls over financial reporting jobs and the change trail around them |
| PCI DSS | Cardholder data scope, redrawn deliberately rather than inherited by accident |
| FFIEC guidance and SEC rules | Examination-ready evidence for bank and broker-dealer systems |
| NYDFS Part 500 | Cybersecurity program controls for New York-regulated financial firms |
| HIPAA | Protected health information in payer claims and eligibility systems |
| GDPR | Any personal data about people in the EU or UK, test copies included |
| DORA | ICT risk management, third-party oversight, and a documented exit strategy for EU financial entities |
Listing a framework describes what the delivery is designed against. It isn't a certification, and no framework owner endorses it. Banks and insurers can see the same evidence model at work on our financial services data modernization page.
For European institutions, a mainframe cloud migration plan doubles as DORA evidence. The first joint report on major ICT incidents under DORA counted 3,383 major incidents reported by EU financial entities, with system failures and external events, not cyberattacks, as the main drivers, according to the EU supervisors' June 2026 report. A documented exit with signed gates, equivalence proof, and a tested rollback speaks directly to that kind of resilience question. Workloads can land in EU Regions or the AWS European Sovereign Cloud where residency rules demand it.
Bring your examiner's questions
Show us the last audit finding on your mainframe estate.
We'll show you which gate produces the evidence that closes it.
Banks, insurers, healthcare payers, manufacturers, and telco and media operators trust Mactores with their mainframe exits. The contract terms stay identical across all of them. The order of work changes: settlement batches, policy administration, claims adjudication, plant and supply systems, or subscriber billing each set a different first wave.
Core systems that clear an examination on the way out.
Card processing, deposit systems, and settlement batches move in waves timed around month-end and quarter-end, never through them. Control totals reconcile to the penny on every nightly run during the parallel period, and FFIEC and SEC expectations shape the evidence pack from the first gate. For insurers, policy administration and rating engines get the same treatment, with actuarial sign-off built into the equivalence ledger.
Financial services modernizationClaims logic that keeps paying correctly after it moves.
Payer claims adjudication, eligibility, and provider payment systems carry decades of benefit rules, many of them written in COBOL and documented nowhere else. Those rules get extracted, confirmed by your SMEs, and tested against recorded claims before cutover. Test environments use masked member data only, so HIPAA exposure doesn't grow while the migration team works, and every claim replayed during the parallel run is checked against the payment the mainframe actually made.
Healthcare modernizationThe back office catches up with the product.
Fast-growing platforms sometimes inherit a mainframe through an acquisition, running billing, ledgers, or fulfillment behind a modern front end. The exit untangles those dependencies without freezing the release train, and the APIs your product teams already call keep their contracts through cutover, down to field names and error codes. Once the logic lives on AWS, it becomes something your developers can ship against weekly instead of quarterly.
Internet & Software modernizationPlant and supply systems that never miss a shift.
MRP, inventory, and order management jobs often still run on the mainframe, feeding plants that operate around the clock. Waves get scheduled against production calendars and planned shutdowns, and every EDI feed to suppliers and distributors is checked byte for byte before it switches over. Each wave's rollback plan gets walked through with plant operations before any job that feeds the floor changes, and planners keep their morning reports on the same schedule they've always had.
Manufacturing modernizationBilling that holds up on your biggest night.
Subscriber billing, rating, and rights management systems carry huge monthly cycles and zero tolerance for missed invoices. Bill runs are replayed at full volume on AWS before cutover, and the timing is compared against the mainframe's own records for the heaviest cycle of the year. Cutover is scheduled around product launches, playoff seasons, and holiday peaks, and the fallback path stays open until the first full bill run closes cleanly.
TMEGS modernizationAcross all five, the same principle holds: the first wave is the one your business fears most, because proving it early is what makes the exit date believable. Tell us which system that is on your side, and it goes into the wave plan on day one, ahead of anything that's merely convenient to move first.
Your sector, your first wave
Name the system your board worries about most. That's wave one.
Settlement, policy, claims, supply, or billing.
Mainframe exits are priced once, after scoping has sized the estate, and the number in the contract doesn't float. Five factors set it: MIPS and code volume, language mix, data structures, service levels, and compliance scope. The money to pay for it usually sits in the mainframe's own license, ISV, and hardware lines.
Pricing disclaimer
Use this section for planning, not procurement. Mactores publishes no prices here; the only binding figure is the fixed fee in your signed statement of work.
| Cost factor | What pushes it higher |
|---|---|
| MIPS and code volume | More programs, more copybooks, and more dead code that still has to be proven dead |
| Language mix | PL/I, Assembler, Easytrieve, and 4GLs alongside COBOL |
| Data structures | IMS hierarchies, heavy REDEFINES, and VSAM files shared across many programs |
| Service levels | Tight batch windows, sub-second online targets, and round-the-clock availability |
| Compliance scope | Cardholder data, protected health information, or examination-grade evidence |
None of the five is guessed. Each one is measured from your SMF data, source libraries, and job schedules before the fee is set.
Legacy mainframe modernization is usually paid for out of the run budget. Even the U.S. federal government spends roughly 80 percent of its IT budget operating and maintaining existing systems, according to a GAO report published in July 2026, and most enterprise budgets we review tell a similar story. McKinsey's research on deliberate modernizers found they keep the share of their technology budgets spent on run-based infrastructure at least 20 percent lower than other organizations, which is the gap a mainframe exit is designed to open.
AWS also runs a mainframe edition of its Migration Acceleration Program, organized around assess, mobilize, and modernize, with methodology guidance and investment incentives for programs that qualify. Whether yours does, and for how much, is AWS's call; our job is to hand AWS a mainframe assessment it can verify line by line. After the exit, the MIPS budget you stop spending is a natural way to pay for the AI agents that will run on the modernized estate.
| Locked in the mainframe today | Freed after the exit |
|---|---|
| Monthly license charges tied to peak MIPS | Conversion, equivalence testing, and cutover rehearsal |
| ISV tools priced by capacity: sort, scheduling, security, source control | AWS capacity sized to actual demand |
| Hardware refresh or lease renewal | Capital returned rather than reinvested in the same box |
| Outsourced COBOL maintenance contracts | Java and cloud skills your team can hire for |
| A disaster-recovery site mirroring the mainframe | Multi-AZ and cross-Region resilience on AWS |
| Months of paying for both platforms | A dual-run end date written into the contract |
Bring your license statement
Your last mainframe software invoice and your ISV renewals tell most of the story.
One session turns them into a fee, an exit date, and a funding plan.
A handful of mainframe terms carry most of the cost and schedule risk: MIPS, monthly license charges, copybooks, packed decimal, JCL, CICS, functional equivalence, and reverse replication. Knowing what each one means for the fee makes any proposal, ours included, far easier to read and challenge.
| Term | Plain-English meaning | Effect on cost or timeline |
|---|---|---|
| Agent-native | A way of delivering in which agents handle the repetitive reading, converting, and comparing, and people make the decisions | Takes the analyst bench off your invoice, which is why the fee can be fixed |
| FDE (forward-deployed engineer) | A senior Mactores specialist embedded day to day with your people, accountable by name for the exit date | The same people scope, build, and cut over, so context never has to be rebuilt |
| MIPS | A measure of mainframe processing capacity, and the unit most mainframe software is priced on | How much MIPS you release, and how fast, is what your business case is really counting on |
| Monthly license charge | Recurring mainframe software charges tied to peak capacity usage | Every month of dual running pays it again |
| Copybook | A COBOL file that defines record layouts shared across programs | Copybooks drive data conversion effort more than table counts do |
| Packed decimal (COMP-3) | A compact numeric format common in mainframe financial data | Rounding mistakes here break control totals and delay sign-off |
| EBCDIC | The mainframe character encoding, with its own sort order | Reports and keys that rely on that order need explicit handling |
| JCL | Job Control Language, which defines how batch jobs run and in what order | Job-stream complexity sets the effort for the batch workstream |
| CICS | The transaction monitor behind most mainframe online systems | Response-time targets for CICS workloads shape the AWS architecture |
| Functional equivalence | The migrated system producing the same results as the mainframe under agreed rules | Undefined equivalence is why parallel runs never end |
| Reverse replication | Copying changes from AWS back to the mainframe after cutover | It keeps rollback possible without losing transactions |
| MIPS release | Taking capacity off the mainframe and ending the contracts priced on it | It's the moment the business case starts paying back |
Definitions with teeth
Each of these terms gets a number in your statement of work.
Read what Mactores owes you if the exit date moves.
Mactores can take a mainframe exit that has been "two years out" for a decade and give it a real last day. Five business days into scoping, you have a wave plan; when the first stage closes, you get a fixed fee, a contracted exit date, and FDEs who answer for it by name.
Mactores started on AWS in 2008, back when putting a production system in the cloud still made auditors nervous. The practice grew up on hard exits: Oracle licenses nobody wanted to renew, Hadoop clusters nobody could staff, and a payments platform built on mainframe-era logic. Along the way, our FDEs also built AI systems that had to clear HIPAA review and internal audit before launch. That's the pairing a mainframe exit needs: agents doing the reading, directed by people who have answered to auditors.
Nearly seven in ten respondents to a Harvard Business Review Analytic Services survey published in April 2026 said legacy systems are limiting their ability to scale AI. In a lot of enterprises, the mainframe sits at the center of that problem. Once its logic lives on AWS, those same rules become something agents can use. From there, the generative AI consulting practice at Mactores builds those agents for production rather than for a demo.
Judge mainframe modernization companies, us included, by one question: on the contracted date, is the mainframe switched off? Everything we've described here exists so that ours can answer yes.
Every application sorted into a path, the riskiest modules named, and an assessment AWS can use for MAP
Right for you when
The next license renewal is less than eighteen months out
A re-baselined wave plan, a committed exit date, and equivalence rules the first attempt never set
Right for you when
A previous attempt converted code but never finished testing
The hardest workload moved and proven on AWS on a contracted date
Right for you when
Leadership wants evidence before committing the whole estate
"All deliverables come with a 90-day warranty. If something breaks in production, we fix it at no additional cost."
Written into every mainframe engagement:
Claims are cheap. AWS partner credentials aren't, because AWS reviews a partner's work before granting them.
Here's the record AWS keeps on Mactores: Premier Tier status as an AWS services partner, 17 service validations, 7 consulting competencies (Migration and Modernization is the one that applies here), and the Agentic AI Specialization. For a mainframe exit, the validations that matter most are AWS Database Migration Service, Amazon RDS, and AWS Lambda, which cover much of the data and service layer your workloads land on. AWS publishes each of them, and our AWS partnership page lists them in one place.
7 Consulting Competencies
You can confirm every one of them yourself on AWS's Partner Solutions Finder, and we'd encourage you to before you sign anything with anyone.
Start with your renewal date
Send one line: your MIPS, your main languages, and when the contract renews.
A delivery lead replies within 24 hours with a time to talk.
