Mainframes Walked So Platforms Could Run [A Brief History of IT Ops]

ON THIS PAGE:

—The pre-cloud era [When software was physical]  —the cloud revolution [Speed, but complexity]  —The emergence of DevOps  —How platform engineering + AI change everything

TL;DR

Before the cloud, building software meant buying physical servers, manually installing updates, and enduring long delays to scale or fix issues. It was slow, costly, and physically exhausting.

• The cloud then brought speed and flexibility, but also complexity, security gaps, and compliance headaches.

• DevOps improved collaboration and automation, but required hard-to-find expertise and still left critical issues unsolved.
  

Now, platform engineering + AI are redefining how startups build and scale by standardizing infrastructure, automating security and removing complexity. With Sequolia’s AI agent acting like a virtual CTO, even small teams can move fast, stay compliant, and scale like pros without needing a massive engineering team. 

—The pre-cloud era [When software was physical]

Picture this: A fast-growing B2B SaaS company is onboarding a new enterprise customer. But the night before launch day, things go wrong:

• The production database crashes due to unexpected load.
• The CTO scrambles to reach the lead engineer—who is sound asleep.
• The engineer finally rushes to the data center, manually debugging issues.
• Six hours later, the system is restored—but the customer is frustrated, and the deal is at risk.

This was normal before cloud computing and literally physically draining. Imagine a world where: Software updates were mailed to clients on CDs, IT teams manually installed programs on every machine and scaling meant physically buying more servers—which took weeks or even months.

—The cloud revolution [speed, but complexity]

The rise of cloud providers like AWS, Azure, and Google Cloud in the 2010s was a game-changer. Suddenly, startups didn’t need to buy physical servers or worry about capacity planning. The cloud solved some of the biggest problems in tech:

• Easier distribution
• Lower infrastructure costs
• On-demand scalability

But as deployment speed accelerated, complexity quickly followed.

• “Works on my machine” issues – Code that ran perfectly in the development environment (where developers build and test features) often broke in the production environment (the live system used by customers), due to mismatched configurations.

• Operational bottlenecks – Developers had to wait days for ops teams to provision infrastructure—that is, to set up servers, databases, or cloud services needed to run the application.

• Security left behind – In the race to ship faster, security was often bolted on at the end—leading to vulnerabilities, breaches, and costly cleanups.

• Compliance chaos – Enterprise buyers demanded detailed documentation, security audits, and regulatory proof, which many startups weren’t ready for.

For years, 70% of IT budgets were spent on maintenance rather than innovation (Gartner). SaaS startups, in particular, faced an uphill battle: it took 6-12 months just to set up infrastructure before they could even start shipping features. This slowed time to market and drained resources that could have been used for growth.

—The emergence of DevOps↗

To break free from these constraints, the industry started evolving fast. A series of new approaches and tools emerged, aiming to streamline delivery and reduce friction.

1. Virtual Machines (VMs): Cutting Hardware Costs, But Not Complexity

Before virtualization, companies had to buy and maintain physical servers, often over-provisioning to handle peak demand (the highest level usage the system might experience during busy times). That meant buying more servers than they usually needed, just to prepare for occasional traffic spikes. VMs reduced hardware costs by allowing multiple virtual servers to run on a single physical machine, making resource use more efficient and flexible.

→ However, they still required manual scaling and management, making them a partial solution.

2. DevOps: Bridging the Gap Between Development and Operation ↗

Traditional IT operations teams were separate from developers, creating bottlenecks. Developers wrote code, then "threw it over the wall" to ops teams, who had to deploy and maintain it. This led to misalignment, slow releases, and unpredictable failures.

DevOps emerged as a new philosophy, integrating development and operations into one collaborative process. It emphasized:

• Automation – Tools like Infrastructure as Code (IaC) replaced manual server setup. IaC means defining and managing infrastructure (like servers and networks) through code, making it repeatable, consistent, and version-controlled - just like software.
• Collaboration – Devs and ops worked side by side to increase reliability, referring to how consistently and predictably a system runs without failure, even as it scales or changes.
• Speed – Teams could release faster and fix issues in real time

→ But there was a catch: DevOps required highly skilled engineers who understood both software and infrastructure management. Many startups struggled (and still are struggling) to find and retain this kind of talent, making DevOps adoption uneven.

3. CI/CD Pipelines: Speeding Up Deployments, But Missing Security

DevOps gave birth to Continuous Integration/Continuous Deployment (CI/CD) pipelines, allowed companies to automate code testing, integration, and deployment, significantly reducing time-to-market. Startups could ship updates daily or even multiple times per day, instead of waiting weeks for a major release.

→ But there was a gap: Security wasn’t built into early CI/CD pipelines. Instead, security reviews happened after deployment, often delaying releases or exposing applications to vulnerabilities. This led to breaches, compliance headaches, and additional costs.

—How platform engineering + AI change everything

Eager to learn more?

Read The Platform Shift: Adapt or Get Left Behind or let's book a call! ↗