Serverless LLM APIs: Host an LLM Backend with Cloud Functions

By / October 16, 2025

Large Language Models (LLMs) are becoming the heart of modern applications, powering everything from customer chatbots to intelligent document assistants. But hosting your own LLM backend can be expensive and hard to scale.

Traditional deployment often means spinning up GPU servers, managing Docker containers, and worrying about uptime, even when no users are active.
That’s where Serverless LLM APIs come in.

With serverless cloud functions like AWS Lambda, Google Cloud Functions, or Vercel Edge Functions, you can deploy lightweight APIs that scale automatically, cost nothing when idle, and still handle production traffic efficiently.

This guide will walk you through how to host an LLM backend on serverless infrastructure, using OpenAI, Anthropic, or open-source APIs, step by step.

1. What Are Serverless LLM APIs?

A serverless LLM API is a lightweight backend function that takes a request (like a prompt), calls an LLM API (like OpenAI or Anthropic), and returns the model’s response, all without managing any servers.

Instead of paying for always-on infrastructure, your function runs only when called and automatically scales with demand.

In short:

You get an AI backend that’s always ready, infinitely scalable, and affordable.

2. Why Go Serverless for LLMs

AdvantageDescription
Auto-scalingHandles 1 to 1M users automatically
Pay-per-useNo cost when idle; only pay per request
Fast setupDeploy APIs in minutes using templates
Secure & IsolatedEach call runs in a sandboxed environment
Global Edge DeploymentFunctions can run close to the user for low latency

Perfect for:

  • Startups testing LLM prototypes
  • Freelancers building AI tools
  • Enterprises deploying microservices
  • Educators or researchers building demos

3. Architecture Overview

Let’s visualise the basic flow:

Frontend (React / Flutter / HTML)
        ↓
Serverless Function (Vercel / Cloud Function)
        ↓
LLM Provider API (OpenAI, Anthropic, Hugging Face)
        ↓
Response → Client

The function acts as a secure proxy that:

  1. Receives user input from the frontend
  2. Adds API keys securely
  3. Calls the model endpoint
  4. Returns the LLM output to the user

4. Step-by-Step Tutorial: Deploy on Google Cloud Functions

Let’s deploy a simple LLM API using Google Cloud Functions and OpenAI’s API.

Step 1: Set up your project

Create a new folder:

mkdir llm-serverless-api && cd llm-serverless-api

Initialize with npm:

npm init -y
npm install openai

Step 2: Write the Function

Create index.js:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

export const llmHandler = async (req, res) => {
  try {
    const { prompt } = req.body;

    const completion = await client.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [{ role: "user", content: prompt }]
    });

    res.status(200).json({
      response: completion.choices[0].message.content
    });
  } catch (err) {
    res.status(500).json({ error: err.message });
  }
};

Step 3: Add package.json Entry

{
  "name": "llm-serverless-api",
  "main": "index.js",
  "type": "module",
  "dependencies": {
    "openai": "^4.0.0"
  }
}

Step 4: Deploy Function

Run the following to deploy:

gcloud functions deploy llmHandler \
  --runtime nodejs20 \
  --trigger-http \
  --allow-unauthenticated \
  --region=us-central1

Once deployed, you’ll get a public URL like:

https://us-central1-yourproject.cloudfunctions.net/llmHandler

Step 5: Test Your API

Send a test request:

curl -X POST https://us-central1-yourproject.cloudfunctions.net/llmHandler \
-H "Content-Type: application/json" \
-d '{"prompt": "Write a short poem about AI and creativity"}'

You should get a JSON response with the generated text.

5. Alternative: Deploy with Vercel or AWS Lambda

Vercel Edge Functions

  • Great for frontend frameworks (Next.js, React)
  • Automatically caches responses
  • Example in api/llm.js:
export default async function handler(req, res) {
  const { prompt } = req.body;
  const response = await fetch("https://api.openai.com/v1/chat/completions", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${process.env.OPENAI_API_KEY}`
    },
    body: JSON.stringify({
      model: "gpt-4o-mini",
      messages: [{ role: "user", content: prompt }]
    })
  });
  const data = await response.json();
  res.status(200).json(data);
}

Deploy instantly with:

vercel deploy

6. Optimise for Speed & Cost

To make your function efficient:

  • Use smaller, cheaper models like gpt-4o-mini or Claude Haiku
  • Add caching for repeated prompts
  • Stream responses for real-time chat
  • Keep API keys in cloud environment variables
  • Log token usage for cost tracking

7. Optional: Self-Host Open-Source LLMs

If you want to avoid paid APIs, deploy open models like Mistral, LLaMA-2, or Gemma using serverless GPUs (e.g., RunPod, Modal, or Replicate).

A simple approach:

  • Host inference endpoint via Replicate or Hugging Face Spaces
  • Connect it to your serverless function
  • Return responses to your app — just like with OpenAI

8. Monitoring & Scaling

Use these tools to track performance:

  • Google Cloud Monitoring / AWS CloudWatch — measure cold start times
  • Vercel Analytics — see request patterns
  • Prometheus + Grafana — for advanced dashboards

KPIs to track:

  • API response time
  • Function cold start frequency
  • Monthly invocation count
  • Cost per 1K requests

9. Extend with Real-World Use Cases

You can easily extend this setup for:

  • Chatbots: Connect to your web app or WhatsApp bot
  • Text summarisation tools: Upload and process docs via API
  • AI Q&A assistants: Combine with vector DB for retrieval
  • Image captioning apps: Add vision models to the same function

10. Wrap-Up

Serverless LLM APIs are the perfect way to bring AI capabilities into production, without breaking your budget or managing servers.

By combining cloud functions with powerful language models, you can:

  • Scale effortlessly
  • Pay only when used
  • Securely manage keys and requests
  • Deploy globally in minutes

Whether you’re building a side project or scaling enterprise workloads, serverless AI backends are the fastest, cheapest, and smartest way to get started.

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