Technical Context
I dug into this use case not as an onlooker, but as someone who constantly builds AI integrations in workflows. The core idea is simple: in Cursor, you can specify a custom URL for an OpenAI-compatible provider in Settings → Models, enter the key, and turn on the override base URL. On paper, it looks nearly ideal.
But then real life happens. If you want to route a GPT subscription or a request-translation proxy through Cursor, you run into the API that the client actually fires. Based on feedback and documentation analysis, Cursor's custom path often goes through /chat/completions, whereas newer GPT scenarios in some places already expect the Responses API.
And here, I wouldn't promise any miracles. For standard OpenAI-compatible endpoints, this often works quickly. For fresh GPT models or subscription setups, you either need a carefully designed proxy that translates the request format, or you have to accept that some features will behave glitchily.
I completely understand why people do this in the first place. It is not for the benchmarks, but for the UX: Composer 2.5, exploration, UI work, and rapid code edits within a single environment. In this mode, building AI automation inside the IDE is genuinely much nicer than jumping between the CLI and the web.
As for Composer's consumption, I caught an important practical signal: on a new codebase, it eats significantly more during the first few days, after which some form of smart caching kicks in. In discussions, there was a baseline: over two weeks of average use, about 60% was gone, with the first 30% burning through very quickly. Not a scientific test, but a very helpful field guideline.
What This Changes for Business and Automation
In short, the winners are teams that prioritize speed of thought over pristine architecture. Cursor with a good model inside truly speeds up code exploration, UI iterations, and minor engineering routines.
The losers are those who hope that a custom endpoint will automatically provide a cheap, seamless replacement for the official integration. It won't, if the protocols don't match. One incorrect compatibility layer, and the agent already behaves strangely, while costs rise without any benefit.
I would suggest two options here: either design a proxy for the specific request format from the start, or avoid touching this fragile setup where the team needs a predictable production UX. At Nahornyi AI Lab, we analyze exactly these bottlenecks: where AI implementation is needed for team speed, and where it is better to build proper custom tooling so that automation with AI does not turn into an expensive experiment. If you are using Cursor, internal tools, or a code agent that is already starting to drain your time and budget, let's look at your scenario and build a solution without unnecessary magic.