Cursor AI Alternative: Build Your Own Code Assistant Backend

Cursor is a fantastic AI code editor, but it's a closed system — you can't control which models it uses, you can't customize the prompts, and the $20/month subscription adds up across a team. What if you could build the same AI coding capabilities into any editor — VS Code, Neovim, JetBrains — with your own backend?

Architecture Overview

A Cursor-like system has three components:

1. Editor plugin: Captures context (current file, cursor position, open files)
2. Backend API: Receives context, calls AI models, returns completions
3. AI models: The actual LLMs that generate code

We'll build the backend — the part that makes it all work.

Cost Comparison: Cursor vs Self-Hosted

Step 1: Code Completion Backend

from fastapi import FastAPI
from openai import OpenAI
from pydantic import BaseModel

app = FastAPI()
client = OpenAI(base_url="https://api.aipower.me/v1", api_key="YOUR_KEY")

class CompletionRequest(BaseModel):
    code_before: str    # Code before cursor
    code_after: str     # Code after cursor
    language: str       # Programming language
    file_path: str      # Current file path

@app.post("/complete")
async def complete_code(req: CompletionRequest):
    response = client.chat.completions.create(
        model="deepseek/deepseek-chat",  # Best coding value
        messages=[
            {
                "role": "system",
                "content": f"You are a {req.language} code completion engine. "
                           "Output ONLY the code that should be inserted at the cursor. "
                           "No explanation, no markdown, no backticks."
            },
            {
                "role": "user",
                "content": f"File: {req.file_path}\n"
                           f"Code before cursor:\n{req.code_before[-2000:]}\n"
                           f"[CURSOR]\n"
                           f"Code after cursor:\n{req.code_after[:500]}"
            },
        ],
        max_tokens=500,
        temperature=0,
    )
    return {"completion": response.choices[0].message.content}

Step 2: Inline Chat (Explain / Refactor / Fix)

class ChatRequest(BaseModel):
    code: str           # Selected code
    instruction: str    # User instruction (explain, refactor, fix)
    language: str

@app.post("/chat")
async def code_chat(req: ChatRequest):
    response = client.chat.completions.create(
        model="anthropic/claude-sonnet",  # Best for code understanding
        messages=[
            {
                "role": "system",
                "content": f"You are an expert {req.language} developer."
            },
            {
                "role": "user",
                "content": f"{req.instruction}:\n\n```{req.language}\n{req.code}\n```"
            },
        ],
    )
    return {"response": response.choices[0].message.content}

Best Models for Code Tasks

Step 3: VS Code Extension Integration

// VS Code extension — call your backend
const vscode = require("vscode");

async function getCompletion() {
  const editor = vscode.window.activeTextEditor;
  const position = editor.selection.active;
  const document = editor.document;

  const response = await fetch("http://localhost:8000/complete", {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      code_before: document.getText(new vscode.Range(0, 0, position.line, position.character)),
      code_after: document.getText(new vscode.Range(position.line, position.character, document.lineCount, 0)),
      language: document.languageId,
      file_path: document.fileName,
    }),
  });

  const data = await response.json();
  // Insert completion at cursor
  editor.edit(edit => edit.insert(position, data.completion));
}

Build your own Cursor alternative today. Start at aipower.me — access DeepSeek, Claude, GPT, and 13 more models through one API. 10 free calls to prototype your code assistant.