I wanted to build a photo editing SaaS. Background removal, face enhancement, anime filters, OCR, content moderation. Each feature normally requires its own ML model, its own GPU, its own API subscription.
Instead, I built the whole thing with one API, one Lambda function, and zero ML code. Twelve AI features, $15/month infrastructure cost, 13x margin on every request.
The 12 Features
Every feature is a single POST request to the same API host. Same key, same auth, same response format.
| Feature | Endpoint | Credits |
|---|---|---|
| Remove background | /v1/remove-background |
1 |
| Blur background | /v1/blur-background |
1 |
| Replace background | /v1/color-background |
1 |
| Enhance faces | /v1/enhance-face |
1 |
| Anime/Ghibli filter | /v1/cartoonize |
1 |
| Colorize B&W photos | /v1/colorize-photo |
1 |
| Pencil sketch | /v1/photo-to-portrait |
1 |
| Face swap | /v1/swap-face |
1 |
| Extract text (OCR) | /v1/ocr |
2 |
| Detect objects | /v1/objects-detection |
2 |
| Face analysis | /v1/faceanalysis |
2 |
| NSFW check | /v1/nsfw-detect |
2 |
All on ai-all-in-one.p.rapidapi.com. One host, one key.
Architecture
No GPU on your side. The Lambda function is a proxy that checks credits and forwards the request.
The Backend
The entire Lambda handler:
import json, boto3, requests
API_HOST = "ai-all-in-one.p.rapidapi.com"
HEADERS = {"x-rapidapi-key": "YOUR_KEY", "x-rapidapi-host": API_HOST}
COSTS = {
"remove-bg": (1, "/v1/remove-background"),
"enhance-face": (1, "/v1/enhance-face"),
"cartoonize": (1, "/v1/cartoonize"),
"colorize": (1, "/v1/colorize-photo"),
"ocr": (2, "/v1/ocr"),
"detect-objects": (2, "/v1/objects-detection"),
"nsfw-check": (2, "/v1/nsfw-detect"),
}
table = boto3.resource("dynamodb").Table("user_credits")
def handler(event, context):
user_id = event["requestContext"]["authorizer"]["user_id"]
action = event["queryStringParameters"]["action"]
cost, endpoint = COSTS[action]
user = table.get_item(Key={"user_id": user_id}).get("Item", {})
if user.get("credits", 0) < cost:
return {"statusCode": 402, "body": json.dumps({"error": "Not enough credits"})}
resp = requests.post(f"https://{API_HOST}{endpoint}",
headers={**HEADERS, "Content-Type": "application/json"},
json=json.loads(event["body"]))
table.update_item(Key={"user_id": user_id},
UpdateExpression="SET credits = credits - :c",
ExpressionAttributeValues={":c": cost})
return {"statusCode": 200, "body": resp.text}
That is the entire backend. Every AI feature goes through the same function.
The Money Math
Your cost per credit (Pro plan $14.99/10K credits): $0.0015
You charge per credit: $0.02
Margin: 13x
| Users | Requests/mo | API cost | Revenue | Profit |
|---|---|---|---|---|
| 50 free | 500 | $0 (free tier) | $0 | -$0.10 |
| 10 paid | 5,000 | $14.99 | $99.90 | $84.41 |
| 100 paid | 50,000 | $49.99 | $999 | $944 |
Ten paying users and you are profitable.
Why This Works
- One integration: 12 features from 1 API, 1 set of docs, 1 billing dashboard
- No ML ops: no models to download, update, or host
- Scales to zero: Lambda + DynamoDB on-demand, pay nothing when idle
- Ship fast: the backend is ~50 lines of Python
👉 Read the full tutorial with screenshots and architecture diagram
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