When people think about the cost of software, they usually picture developers writing code. That’s where the action is: new features, pull requests, tests. But if you look at the total cost of a software system or app over its lifetime, the picture changes. Coding is just one part of a much larger whole – and often not the biggest one.

The total cost is split into different areas. There is the direct build cost: developing the software, writing code, and testing. Then there is deployment or shipping: getting the system onto servers or delivering it to end users as apps. After that come the operational costs: hardware, cloud infrastructure, monitoring, backups, and everything needed to keep it running. Over time, there are also continuous changes: new features, bug fixes, regulatory updates, and adaptations as requirements evolve. Around all of this sits the cost of the organization itself: people, coordination, support functions, management, and the processes to keep everything moving. The list just continues.

If you look at the total cost over time, the pure programming part is actually quite small. The rest – deployment, operations, change, and organizational overhead – quietly dominates. Language models are very good at automating all or parts of the programming work, and many teams already use them for that: generating code, writing tests, refactoring, or explaining tricky parts of the codebase. That is useful, but it only touches a small piece of the total cost.

Deployment and shipping is one area where there is a lot of manual work that could be reduced. Setting up pipelines, handling configuration, managing environments, preparing releases, and communicating changes all take time. A language model can help generate and update deployment scripts, explain existing setups, and create clearer release notes and runbooks based on commits and tickets.

Operations and infrastructure is another big cost center. Running servers and cloud resources, handling incidents, looking at logs and metrics, doing routine maintenance – all of this adds up. Here, a language model can help by turning scattered technical data into understandable summaries, suggesting possible root causes, and drafting or updating operational documentation.

Change over time is often where costs really grow. Every system accumulates history and complexity. People leave, documentation goes out of date, and nobody fully remembers why things were done in a certain way. A language model can help developers understand existing code, answer “where is this implemented?” questions, generate overviews from code and configuration, and support safer refactoring and impact analysis. Making it easier and safer to change a system can be more valuable than simply speeding up initial coding.

Then there is everything around the actual building and running of the system: the organization. Product management, security, legal, finance, HR, customer support, and internal support all contribute to the total cost. A lot of this work is about communication and information: writing and reading documents, reporting status, answering questions, and coordinating between roles. Language models can help by summarizing long threads and reports, drafting documentation and FAQs, assisting support staff with suggested replies, and helping people find relevant information faster.

If you only think of a language model as a “coding assistant”, you automatically limit its impact to a small slice of the cost. A better approach is to first ask: where do we actually spend time and money across the whole lifecycle of our systems? The biggest opportunities are often in repetitive processes, communication-heavy workflows, and areas where knowledge is locked in the heads of a few people.

Programming is just one part of the total cost of a software system. Over its lifetime, deployment, operations, change, and organizational work take up a much larger share. Language models are excellent for helping with code, but they might be even more valuable when used across these other areas that represent a bigger part of the real cost.