I added AI to my app in 30 minutes. Here's the actual timeline.
Not "weeks of integration." Not "reading 4 API docs." Thirty minutes.
0–2 min: Try before committing
No signup. Opened the playground, picked Kimi K3, typed my actual
use case — "review this error message and suggest a fix." It worked.
Response in ~1 second. That's when I decided to integrate.
2–5 min: One-click signup
GitHub → authorize → dashboard. Five seconds. No email. No password.
No verification code.
5–7 min: Everything handed to me
After login, an onboarding page showed my API key, a copyable curl
command, and Python SDK code. Copied the Python. Done.
7–30 min: The actual code
from openai import OpenAI
client = OpenAI(api_key="mb-xxx", base_url="https://aibridge-api.com/v1")
def classify(text):
return client.chat.completions.create(
model="deepseek-chat", # $0.27/M for the easy stuff
messages=[{"role":"user","content":f"Classify: {text}"}],
).choices[0].message.content
def review(code):
return client.chat.completions.create(
model="kimi-k3", # 1M context for the hard stuff
messages=[{"role":"user","content":f"Review this for bugs:\n{code}"}],
).choices[0].message.content
Two functions. Same client. Cheap model for classification, reasoning
model for review. Switching is one string change.
What I didn't have to build
- Streaming — worked out of the box (stream=True)
- Function calling — worked with tools=[...]
- JSON mode — response_format={"type":"json_object"}
- Caching — one header, X-Cache-TTL: 3600
-
Billing for 4 providers — one key, one bill
What I used it for, in order
Classification — deepseek-chat, $0.27/M
Code review — kimi-k3, 1M context, always reasoning
Translation — qwen-max, multilingual
Complex reasoning — glm-4-plus
Four jobs. One endpoint. Zero new SDKs.
Free tier is 500K tokens/month. No credit card. The playground needs
no signup, so you can do my "0–2 minutes" step right now.
→ aibridge-api.com/playground.html
→ aibridge-api.com/prompts.html (24 prompts to steal)





Top comments (0)