What is a mainframe?

These qualities have made mainframes the computing foundation of many organizations, unrivaled as a single platform for live transaction processing, batch processing and authoritative record keeping. They remain the transactional core of industries like banking, insurance and government: according to an IBM Institute for Business Value report, 43 of the world’s top 50 banks and 8 of the top 10 payment companies rely on the mainframe as their core platform. Today, mainframes handle over 70% of transactional workloads, while accounting for less than 10% of the total IT spend.

Beyond the business world, mainframes touch our everyday lives. When you swipe your credit card at the grocery store, buy an airline ticket or submit a medical claim, there’s a good chance a mainframe is quietly handling the transaction behind the scenes, processing it reliably and securely at a scale and speed that few other systems can match.

Historically, “Big Iron” mainframes filled entire rooms—as large as 10,000 square feet. These enormous machines required vast amounts of electrical power, air conditioning and reams of input/output (I/O) devices. Today’s models are much smaller—about the size of a large refrigerator.

The most recent mainframe systems are drivers of artificial intelligence (AI) innovation. They let organizations run AI where the data lives and are capable of processing 450 billion AI inferences per day at one-millisecond response times.

A combination of reliability and performance—massive throughput with hardware-guaranteed consistency—has helped mainframes outlast the PC revolution and the rise of cloud computing. And recent research suggeststhey will remain crucial to enterprise IT architectures: 78% of executives agree that mainframe-based applications will continue to have an important role in digital transformation (up from 66% in 2021 and 64% in 2023). 

Increasingly, organizations rely on hybrid architectures that ground execution on premises for predictability and control, while using cloud environments for agility and edge computing for immediacy. This provides the flexibility to choose the appropriate computing platform for a given workload based on demands and outcomes, rather than an architectural default (like cloud-first). The mainframe’s role has evolved with this shift; it’s no longer a standalone system, but a crucial component integrated with cloud and edge environments.

Mainframes remain in daily use because they guarantee reliability, security and transaction integrity in hardware, something no other platform matches at the same speed and cost. However, there’s a misconception that mainframes are old technology that’s no longer relevant. 

Analysts have predicted the mainframe’s demise for decades. In 1991, with the rise of the personal computer, technology writer Stewart Alsop famously predicted the “last mainframe” would be unplugged by 1996. But the same qualities that made them indispensable decades ago—reliability, isolation, and guaranteed transaction integrity—are why they’re still where the world’s most critical industries put their most important work.

Mainframes have driven enterprise computing since 1951, when the Eckert-Mauchly Computer Corporation released UNIVAC, the first commercial mainframe. IBM entered the market two years later with its Model 701 Electronic Data Processing Machine, then took it over almost entirely with the unveiling of the System/360 in 1964. Other manufacturers, including Fujitsu, Hitachi, RCA and Sperry Univac, also built mainframes during this period, though IBM’s dominance continued to grow.

By the mid-1960s, System/360 became the industry standard. Previously, every machine needed its own custom software; system/360 changed that. A program written for one machine could run on any other in the line, and for the first time hardware and software didn’t have to be built together.

A key reason that most large banks, card networks, insurers and airlines still rely on the mainframe to run core workloads is architectural. Mainframes provide guarantees in hardware that other solutions provide in software, or don’t provide at all. Reliability, isolation, encryption and transactional consistency are all embedded into the silicon and firmware, where they’re faster and not reliant on every application developer getting them right.

Mainframes also provide dependable execution, cost and governance, qualities that matter as organizations work to rein in runaway cloud spending. In a study from the Synergy Research group, cloud infrastructure spending reached an eight-year high in July 2026, with global enterprise spending reaching USD 143.4 billion, a 43% year-on-year increase, due to the demand for generative AI (gen AI).

“The mainframe has always been the backbone of the enterprise, but what we’re seeing now is a redefinition of its role to fit modern mainframe optimization practices,” said Steven Dickens, CEO and principal analyst of HyperFRAME Research, in a report for BMC. “With a digitally native workforce, the rise of cloud and AI-driven workflows, and the emergence of AIOps as a top priority, the mainframe is no longer just about stability—it’s about fueling innovation and driving competitive advantage.”

Today’s mainframes continue to play an integral part in enterprise computing, driving benefits such as:

- Greater reliability and availability
- Higher processing power and performance
- Stronger data and transaction integrity (systems of record)
- Tighter security
- Robust regulatory compliance
- Better cost efficiency for high-volume workloads

A modern mainframe spreads functions across different processor types rather than relying on general-purpose cores to do everything. A mainframe uses central processors to run the operating system, transactions and application workloads, while using specialized engines and dedicated hardware for specific tasks, such as running Linux, handling I/O or encryption. Offloading these routine tasks keeps the main processors free for primary workloads, rather than bogging them down with work that dedicated hardware can do more efficiently.

Here’s a rundown of the major components and capabilities:

- Hardware and infrastructure
- Operating systems (OS)
- Workload isolation and virtualization
- Middleware
- Databases and transaction managers
- Programming languages
- Development tools

Today’s mainframes are smaller than the original machines. The latest IBM mainframe models are built into standard 19-inch enterprise rack frames, fitting neatly alongside server stacks in a data center, though they weigh significantly more, often between 1,500 and 2,600 pounds (680 to 1,200 kg).

IBM Z systems, like the z17™ single-frame system, follow this design. Organizations can add capacity within a single frame, or cluster multiple mainframes together to distribute work across systems, enabling the platform to scale alongside demand.

-I/O adapters. Dedicated cards handle networking, cryptography, storage and compression, each with its own processor and memory, so the main processors remains free for transaction and application workloads. Multiple adapters transfer data to connected devices; if one fails, another takes over automatically, so the system keeps running without interruption.

- System Assist Processor (SAP). A SAP moves data between the operating system (OS) and I/O devices, so the main processors only handle processing, not data movement.

- Control unit. A control unit manages communication between processors, I/O channels and connected devices.

A mainframe operating system handles memory, processors, devices and file systems directly, so applications never have to manage that coordination on their own. Several operating systems can run on one physical mainframe at the same time. A single OS can also run more than one workload at once. For instance, IBM’s z/OS handles large volumes of transaction processing and batch jobs together.

A mainframe can be divided into multiple isolated environments, each running its own operating system and workloads, instead of dedicating the whole physical machine to one task. As opposed to clustering, which combines several physical machines into one system,  virtualization technology does the reverse, dividing a single computing machine into many (for example, using a hypervisor like z/VM).

Middleware sits between the operating system and the applications end users interact with, handling functions the operating system doesn’t, like database management, transaction processing, web serving and message routing. Mainframes typically run many of these functions at once, often several instances of the same one.

Two examples on IBM Z illustrate this: CICS® (Customer Information Control System) runs and coordinates business transactions in real time, and IMS™ (Information Management System) pairs a database with its own transaction manager, letting applications store data and process transactions through the same system.

Two of the most important kinds of mainframe middleware are databases and transaction managers. A database management system stores and organizes data so mission-critical applications can retrieve, update and share it. A transaction manager handles the processing side, making sure each transaction completes fully or not at all, even when multiple applications read and write at the same time. Because the mainframe provides both as shared services, applications can rely on them for data integrity instead of each building its own consistency logic.

Mainframe applications run on languages like COBOL, which is deeply embedded in the world’s financial infrastructure. By widely-cited estimates, COBOL still underpins the majority of ATM and in-person credit card transactions and a large percentage of core banking systems.

But COBOL isn’t the only mainframe language option. Java and other modern languages run on the mainframe, too. Whichever language an application is written in, it runs on the same underlying platform, with the same guarantees around resilience, security and performance.

Writing and running mainframe code takes its own set of tools. Developers write COBOL much like any other code, then use tools built for the language, like IBM’s Enterprise COBOL for z/OS compiler, to turn it into something the mainframe can run. Generative AI tools now help developers maintain and update existing code.

There is often confusion surrounding the differences between “mainframe,” “server,” “cloud server” and “supercomputer.” Each one is built for a different job. Here’s what sets them apart.

A commodity server handles a specific task, such as file hosting, web hosting or running a database, for a defined set of users. Organizations typically run many together to divide the workload. In contrast, a mainframe consolidates that work onto one machine, handling massive transaction volumes and heavy, continuous use without the performance drop a server sees under load, due to its high-bandwidth I/O and workload management capabilities.

A cloud server sits on infrastructure a provider hosts remotely, scaled on demand and paid for on a usage basis. A mainframe sits on infrastructure an organization owns and operates itself, on premises or in a rack-integrated hybrid setup. Most enterprise organizations run both, mainframes for core transactional workloads, cloud for everything that benefits from on-demand elasticity.

A mainframe runs a huge number of small, real-time transactions simultaneously. A supercomputer works on fewer—but far larger—computational problems, executing quadrillions or even quintillions of floating-point calculations per second, built for work like weather forecasting, climate modeling and physical simulations rather than transaction processing.

Mainframes are critical to industries that depend on fast, uninterrupted, secure performance such as banking, healthcare and government. Far from disappearing, the global mainframe market is projected by one industry analysis to expand from USD 6.11 billion in 2026 to USD 10.53 billion by 2034, a CAGR of ~7%. Major industries that rely on mainframes include:

- Banking and finance
- Healthcare
- Insurance
- Government and public services
- Retail
- Air travel and logistics

Many modern organizations run cloud and distributed architectures alongside mainframes, not instead of them. Modernizing mainframe-based applications is now a core part of enterprise hybrid cloud strategy, which combines on-premises, public cloud, private cloud and edge settings into a single, flexible IT infrastructure. Seventy-five percent of executives are moving away from cloud-first defaults, placing workloads based on outcomes instead, according to the IBM Institute for Business Value.

Managing that kind of environment means giving IT teams a consistent way to govern applications, data and AI models across environments, and not treating on-premises, cloud and edge as separate systems bolted together. Security practices must also meet a consistent standard: for example, encryption and access controls that meet requirements whether data sits on the mainframe or in the cloud.

Extending mainframe capabilities into a hybrid cloud environment lets businesses pick the best environment for each workload. For example, an airline can build a customer-facing app for managing travel information, like cloud-based reservations, that also pulls real-time data held on the mainframe, such as changes to flight arrival and departure times. In such a scenario, the mainframe is not replaced, but rather assigned workloads it was purpose-built for while cloud apps handle work that doesn’t require a mainframe.

Mainframes can run more than proprietary operating systems; for instance, they also run Linux, the free, open-source Unix-like OS that powers most of the internet’s infrastructure today. According to the Linux Foundation, Linux runs 90% of public cloud workloads.

Running Linux on the mainframe combines the openness of an open-source OS with the reliability mainframes are built for. On IBM Z, for example, consolidating many x86 Linux servers onto a single mainframe replaces many standard server cores, cutting energy use and cost.

Mainframe modernization updates or transforms legacy mainframe applications and systems to take advantage of the agility, developer productivity gains, cost savings and competitive edge that newer technologies can offer. The most common approach, mainframe application modernization, updates specific applications rather than overhauling the entire system. This approach enables an organization to leverage new technologies while retaining the foundational benefits of the platform.

Code translation alone rarely solves the modernization problem. Modernization also must address data architecture—how data is structured and accessed; runtime replacement—swapping out the systems that execute the code; and transaction processing—the guarantees that keep multi-step operations consistent. Each of those sits underneath the code itself, which is why language translation is but a small piece of the project.

Mainframe applications often run an organization’s most critical processes: transaction processing, fraud detection, regulated workloads. Enterprises often modernize these applications to reduce operational costs, bridge talent gaps, or better integrate legacy systems with cloud and AI technologies. That usually means updating an application’s interface, its code or how it connects to other systems, while leaving the core platform in place.

“Enterprises that rely on legacy systems now prioritize continuity over replacement,” said Bill Huber, partner, ISG Digital Platforms and Solutions in a statement from ISG News. “They seek structured, low-risk transformations that align with long-term operational goals.”

Refactoring breaks a large, monolithic application into smaller, independent pieces, without necessarily changing the platform it runs on. Organizations refactor when they need features the current architecture can’t support.

Replatforming moves an application to a new environment, often the cloud, with only minor code changes. A pre-built, off-the-shelf solution, usually off the mainframe entirely, replaces an older system outright.

Learn more about the different approaches to modernization.

Refactoring includes cleaning up existing code and improving an application’s internal structure so that both internal and external communication and integration are improved. This often means adding or adjusting application programming interfaces (APIs) that connect microservices within the application or enable communication between the application and external systems.

AI tools can scan large volumes of COBOL and show developers how an application’s code and data connect, which speeds up refactoring work. For example, the National Organization for Social Insurance (NOSI) cut the time needed to analyze outdated COBOL code by 94%, from about 8 hours down to roughly 30 minutes, and used that analysis to plan its modernization work.

For more than a decade, cloud-first was the default choice for enterprise technology. As AI moves from pilots into live production, running inside real transactions, architecture decisions have become business decisions, not just technical ones.

A report from the IBM Institute for Business Value found 72% of organizations report higher-than-expected cloud costs once applications reach production. As AI starts approving payments, detecting fraud and enforcing compliance in real time, tolerance for cost surprises or gaps in control plummets.

AI can run directly on transaction data on a mainframe, without moving that data somewhere else first. For instance, on-chip AI accelerators, like on the IBM z17, handle massive inference workloads at real-time speed, some processing hundreds of billions of inference operations a day at millisecond response times.

“It’s a paradigm shift: bringing the AI to the data, rather than moving the data to the AI,” said Kamal Matta, assistant vice-president of IT and security at Sonic Biochem Extractions, in Computer Weekly.

Adoption backs this up. In a survey run by the IBM Institute for Business Value and Oxford Economics, 78% of executives said their organizations are piloting or actively running AI capabilities inside mainframe applications and transactions, and 79% agreed the mainframe is essential for enabling AI-driven innovation.

The same survey reports that 90% of executives acknowledge that their organization is piloting or implementing AI-powered cybersecurity projects, with 82% citing the mainframe’s importance for monitoring and responding to cyber threats.

“I see AI as a tailwind for the mainframe, not the opposite,” said Steven Dickens, CEO of HyperFRAME Research, in CIO Dive.

Read more about how mainframes are a strategic asset in the AI era.

Mainframes can run more than one kind of AI model at a time. Traditional machine learning (ML) systems that are trained to perform specific tasks using explicit logic work alongside large language models (LLMs) built for analyzing unstructured text and images and generating new output, each handling the part of a task it’s best suited for.

With insurance claims, for example, traditional AI systems might be used to process structured data like police reports and registration forms, while a language model analyzes unstructured information, like injury descriptions or damage photos. This helps companies prioritize urgent claims. Running both models together on the same system means an organization gets faster, more complete decisions without moving data between separate platforms.

Experienced mainframe professionals are retiring, and fewer people are learning how to work with these systems. System programmers configure and maintain the platform, operators handle day-to-day operations, and developers build and update the applications running on top. None of these roles come naturally from a Java or Python background; they require understanding the mainframe’s own concepts and subsystems.

The real shortage isn’t language—Java and Python already run on mainframes. It’s finding people who understand mainframe-specific systems and design patterns, since most training budgets go toward cloud and distributed architectures instead. Decades-old application code makes this harder still: someone must understand its logic and data dependencies to work with it safely. AI is helping close this gap by making mainframe knowledge more accessible and easier to disseminate.

Conversational AI assistants are helping new mainframe professionals ask questions in plain language and get step-by-step answers, preventing them from having to interrupt (or in some cases find) a subject matter expert to get help. Experienced staff can receive similar assistance when they have questions regarding a task they haven’t done in a while. None of this replaces the tools mainframe teams already rely on, like JCL, REXX or Ansible. AI works alongside them.

Read more about how AI is bridging the mainframe skills gap.

Are mainframes still used today?

Mainframes remain the transactional backbone for banking, insurance, government and other industries running high-volume, always-on processing.

What are mainframes used for?

Mainframes handle transaction-heavy work: credit card approvals, ATM withdrawals, insurance claims, government benefits, and large-scale retail and logistics operations.

Why do so many banks and financial institutions use mainframes?

Banks need guaranteed transaction accuracy at massive scale, especially during peak times like paydays or holidays. Mainframes can handle that load without performance degradation or failed transactions.

Is a mainframe the same as a server?

A server is generally configured for a specific workload and many of them are run together. A mainframe consolidates work onto a single machine that is engineered for continuous, high-volume processing without failing or slowing down. Servers scale horizontally—additional servers are added to handle increased capacity. A mainframe scales vertically through specialized internal hardware.

What is the difference between a mainframe and a supercomputer?

A mainframe processes millions of small transactions concurrently. A supercomputer runs far fewer, much larger calculations, like weather modeling or physics research.

Can mainframes run Linux?

Mainframes run Linux the same way other hardware does, giving organizations an open-source option alongside proprietary mainframe operating systems.

Can mainframes run Windows?

Mainframes cannot run Windows on their own processors. However, some hybrid models house separate x86 processor blades inside the same cabinet to run Windows workloads alongside the mainframe.

What programming languages are used on mainframes?

COBOL remains widely used, especially in banking and finance, but mainframes also run Java and other modern languages.

Should companies migrate off mainframes?

Most organizations get more value modernizing the applications already running on the mainframe than moving workloads off it.

Are mainframes secure?

Mainframes encrypt data by default, can isolate workloads at the hardware level, and now include protections built for future threats like quantum computing. The hardware itself is highly secure, but a mainframe’s overall security still depends on its configuration and the security of the systems and networks it connects to.