How I Built and Deployed an AI Vision API from Scratch with YOLOv8 and FastAPI

A CS and Mathematics student from Ujjain, India, built and deployed LARA, a lightweight computer vision API that runs YOLOv8 nano on a CPU-only server, eliminating the need for GPUs. The API, built with FastAPI, Supabase, Razorpay, and Railway, allows developers to send an image and receive object detections with confidence scores and bounding boxes in a simple API call.

I am a CS and Mathematics student from Ujjain, India. A few months ago I was trying to learn computer vision and build real projects with it. Then I hit a wall. Every tutorial, every library, every framework assumed you had an NVIDIA GPU. CUDA this. GPU memory that. "Just spin up a cloud GPU instance" — at $2-3 per hour, which adds up to hundreds of dollars per month. I have an Intel integrated graphics chip. 8GB RAM. No NVIDIA card. No budget for cloud GPUs. I couldn't do the thing I wanted to learn. So I decided to build the solution I wished existed. LARA — Lightweight Adaptive Recognition API A computer vision API where you send an image and get back detected objects, confidence scores, and bounding box coordinates in one simple API call. The heavy AI computation runs on my server. You get the results instantly. No GPU needed on your end. Ever. Here is all the code a developer needs to use LARA: python import requests response = requests.post "https://web-production-41d94.up.railway.app/detect", headers={"x-api-key": "your-api-key"}, files={"file": open "image.jpg", "rb" } print response.json That's it. Three lines. You get back: { "status": "success", "count": 2, "detections": {"label": "person", "confidence": 0.92, "box": 10, 20, 150, 300 }, {"label": "car", "confidence": 0.87, "box": 200, 50, 500, 400 } } Here is exactly what I used to build LARA: AI Model — YOLOv8 nano I chose YOLOv8 nano specifically because it is the smallest and fastest variant of YOLOv8. It detects 80+ object classes accurately while being light enough to run on a basic CPU server without timing out. API Framework — FastAPI FastAPI auto-generates beautiful API documentation, handles file uploads cleanly, and is fast enough for production workloads. It also generates OpenAPI specs automatically which made listing on RapidAPI easy. Database — Supabase Three tables: users, usage, billing. Every API call gets logged. Every developer gets tracked. Supabase gave me a production-grade PostgreSQL database with a clean Python client and Row Level Security — for free. Payments — Razorpay For Indian developers and businesses. Razorpay handles the billing so I don't have to. Hosting — Railway Deployed via Docker. Railway auto-deploys every time I push to GitHub. The entire deployment pipeline took about 30 minutes to set up. I started with a basic FastAPI app: python from fastapi import FastAPI, File, UploadFile, Header, HTTPException from PIL import Image from ultralytics import YOLO import io app = FastAPI title="LARA API" model = YOLO "yolov8n.pt" @app.post "/detect" async def detect file: UploadFile = File ... , x api key: str = Header ... : contents = await file.read img = Image.open io.BytesIO contents .convert "RGB" results = model img, verbose=False detections = for box in results 0 .boxes: label = model.names int box.cls confidence = round float box.conf , 3 x1, y1, x2, y2 = round float v for v in box.xyxy 0 detections.append { "label": label, "confidence": confidence, "box": x1, y1, x2, y2 } return {"status": "success", "detections": detections, "count": len detections } I added API key validation and usage logging with Supabase: python def get user api key: str : result = supabase.table "users" .select " " .eq "api key", api key .execute if result.data: return result.data 0 return None def log usage api key: str, endpoint: str : supabase.table "usage" .insert { "api key": api key, "endpoint": endpoint, "timestamp": datetime.now .isoformat } .execute YOLOv8 nano sometimes detects the same object twice with slightly different bounding boxes. I implemented NMS Non-Maximum Suppression manually to remove duplicates: seen boxes = for box in results 0 .boxes: x1, y1, x2, y2 = round float v for v in box.xyxy 0 duplicate = False for seen in seen boxes: sx1, sy1, sx2, sy2 = seen inter area = max 0, min x2,sx2 -max x1,sx1 max 0, min y2,sy2 -max y1,sy1 union area = x2-x1 y2-y1 + sx2-sx1 sy2-sy1 - inter area iou = inter area / union area if union area 0 else 0 if iou 0.5: duplicate = True break if not duplicate and confidence = 0.3: detections.append {...} seen boxes.append x1, y1, x2, y2 The biggest challenge was the Linux server not having the display libraries OpenCV needs. The fix was switching to opencv-python-headless and building with a proper Dockerfile: FROM python:3.10-slim WORKDIR /app RUN apt-get update && apt-get install -y libglib2.0-0 libgl1 libxcb1 COPY requirements.txt . RUN pip install --no-cache-dir opencv-python-headless==4.10.0.84 RUN pip install --no-cache-dir -r requirements.txt COPY . . CMD "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000" If you are a developer, student, or researcher who wants to add computer vision to your project without: LARA is for you. A parking management system. A security camera app. A retail analytics dashboard. A wildlife monitoring tool. All of these need object detection. None of them should need a $3000 GPU setup to get started. Register at the landing page and get your API key instantly — no credit card, no waiting: 👉 https://lokendraparihar-9977.github.io/lara-landing https://lokendraparihar-9977.github.io/lara-landing Full API docs: 👉 https://web-production-41d94.up.railway.app/docs https://web-production-41d94.up.railway.app/docs Free tier includes 100 API calls/month. Paid plans start at ₹249/month ≈ $2.99 for 500 calls — cheaper than 30 minutes on a cloud GPU. I am building specialized models for: If you have a use case you want LARA to support, let me know in the comments. I am building this based on real developer needs. Built by Lokendra Singh Parihar — CS + Mathematics student, Vikram University, Ujjain, India.