Most apps that grew an "AI" feature in the last two years talk to one of a handful of cloud APIs, and almost all of them speak the same dialect: the OpenAI Chat Completions format. That one detail is the reason you can pull the cloud out and run the whole thing locally on a Mac without the app ever noticing.
Here is the trick, why it works, and the gotchas that bite.
OpenAI's /v1/chat/completions
endpoint became the de facto standard. So when an app lets you "use your own key" or "set a custom base URL," it is almost always going to POST to {base_url}/chat/completions
with a JSON body of messages and read back the same shape. It does not care what is on the other end, only that the response matches.
Local runners leaned into this. Both popular Mac ones expose exactly that endpoint:
http://localhost:11434/v1
(its native API lives on /api
, but the /v1
path speaks the OpenAI dialect).http://localhost:1234/v1
.So "make this app local" usually reduces to: point its base URL at one of those, put any non-empty string where it wants an API key, and pick a model you have pulled.
Ollama:
brew install ollama # or the .dmg from ollama.com
ollama serve & # server on :11434
ollama pull llama3.1:8b # pull a model once
Confirm it speaks OpenAI:
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.1:8b",
"messages": [{"role": "user", "content": "say hi in 3 words"}]
}'
If that returns a choices[0].message.content
, any OpenAI-compatible client can use it. In the app, set:
http://localhost:11434/v1
ollama
(or literally anything; it is ignored)llama3.1:8b
LM Studio is the same idea with a GUI: load a model, toggle the server on, and use base URL http://localhost:1234/v1
.
The pattern shows up everywhere once you look for it. The official OpenAI SDKs are the clearest example: change one field.
from openai import OpenAI
client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
r = client.chat.completions.create(
model="llama3.1:8b",
messages=[{"role": "user", "content": "summarize this in one line: ..."}],
)
print(r.choices[0].message.content)
Same code, cloud or local. Only the base_url
changed. In JavaScript it is the baseURL
option; in a lot of CLI and editor tools it is an OPENAI_BASE_URL
environment variable. Some end-user apps expose it too: DEVONthink 4, for instance, lets its Chat point at Ollama or LM Studio directly, so search and summarize run over your own documents with nothing leaving the machine.
It is not always drop-in. The places it breaks, roughly in order of how often they catch people:
num_ctx
). If an app sends a big document and the model only reads the last 2k tokens, it looks like the model "ignored" your content. Raise the context in the model config, or send smaller chunks.Two reasons that actually matter. It is free after the download, so there is no per-token meter running while you iterate on a prompt fifty times. And nothing leaves the machine, which for anything sensitive (client data, personal notes, a private codebase) is the difference between "I can use AI on this" and "I am not allowed to."
The best part is that because it is one interface, you do not have to commit. Keep a cloud base URL for the genuinely hard reasoning and a local one for the bulk, private, high-volume work, and switch between them by changing a single string.
Written with AI assistance and edited by a human. Endpoint details reflect public docs as of July 2026 and move quickly, so check each project's current docs for exact paths and defaults.