⟐ Dev Tools/2026-03-28Advanced

Building Production Full-Stack AI Apps with Gemini API & Supabase

A practical guide to building production-grade full-stack AI apps with Gemini API and Supabase—covering auth, pgvector, Edge Functions, RLS, and cost control, plus the tuning lessons (IVFFlat to HNSW recall recovery, the service_role RLS bypass) you only learn in production.

Gemini API¹⁸¹ Supabase pgvector⁴ Edge Functions Full-Stack Development²

✦ Premium Article

A RAG chat endpoint I had running happily on a Supabase Edge Function suddenly started returning visibly worse matches the moment my document set grew from 10,000 to 120,000 rows — without a single line of code changing. The culprit was the pgvector index configuration, the kind of "only shows up at scale" trap that quickstart docs never mention.

I've been building iOS and Android apps solo since 2014, and the Gemini API + Supabase combination is, in my experience, one of the few stacks an independent developer can actually run in production alone. This guide walks through wiring up auth, pgvector, Edge Functions, RLS, and cost control end to end — and then goes into the tuning decisions you only discover once real traffic hits.

Setup and context

Combining Gemini API with Supabase creates an exceptionally powerful platform for building modern AI applications. Supabase provides an integrated foundation with PostgreSQL, authentication, real-time subscriptions, and Edge Functions, while Gemini API handles text generation, multimodal processing, and embeddings. Together, they enable you to construct scalable, feature-rich AI applications rapidly—from AI chatbots and RAG systems to semantic search platforms.

This guide walks you through building production-grade full-stack AI applications using this combination. You'll learn proven architecture patterns, authentication flows, database design with pgvector, security implementation, and performance optimization techniques that real applications rely on.

Supabase & Gemini Architecture Patterns

A well-designed Supabase + Gemini architecture consists of several interconnected layers:

Frontend Layer

React, Next.js, or similar client application
Real-time UI updates via Supabase Realtime client
Streaming response handling from Gemini API

API & Edge Functions Layer

Supabase Edge Functions (TypeScript/Deno runtime)
Authenticated requests to Gemini API
Request validation and rate limiting
Caching strategies

Data Layer

PostgreSQL (Supabase-hosted)
pgvector extension for semantic vector storage
User data, conversation history, document metadata
Row Level Security (RLS) for multi-tenant isolation

External Services

Gemini API (text generation, embeddings)
Storage (Supabase Storage or S3)
Optional: Redis or Vercel KV for caching

Why This Architecture Works

PostgreSQL with pgvector eliminates the need for a separate vector database—semantic search runs natively in your primary database. Edge Functions enable you to manage Gemini API authentication securely at the edge, minimizing latency. The RLS model ensures data isolation without additional middleware.

This architecture scales gracefully from prototype to millions of users while keeping operational costs reasonable. You get native transaction support, complex queries, and relational integrity that pure vector databases can't match.

✦

Thank you for reading this far.

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What follows includes implementation code, benchmarks, and practical content we hope you'll find useful. This site runs without ads — server and development costs are supported entirely by members like you. If it's been helpful, we'd be truly grateful for your support.

WHAT YOU'LL LEARN

✦The exact pgvector parameters and trade-offs for moving from IVFFlat to HNSW to recover search recall from 0.78 to 0.93

✦The trap where a service_role key silently bypasses RLS, and how to scope permissions correctly with a user-scoped client

✦Avoiding 429s in embedding batches (concurrency cap + exponential backoff) and the real monthly cost at 8,000 MAU

Secure payment via Stripe · Cancel anytime

Setting Up Supabase and Authentication

Creating Your Supabase Project

Start by creating a new project at supabase.com. Once initialized, capture these credentials in your .env.local:

# .env.local
NEXT_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key
SUPABASE_SERVICE_ROLE_KEY=your-service-role-key
GEMINI_API_KEY=YOUR_GEMINI_API_KEY

Install the Supabase JavaScript client:

npm install @supabase/supabase-js @supabase/ssr

Email/Password Authentication

Implement email-based sign-up with email confirmation:

// lib/auth.ts
import { createClient } from '@supabase/supabase-js'
 
const supabase = createClient(
  process.env.NEXT_PUBLIC_SUPABASE_URL!,
  process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY!
)
 
export async function signUpWithEmail(email: string, password: string) {
  const { data, error } = await supabase.auth.signUp({
    email,
    password,
    options: {
      emailRedirectTo: `${process.env.NEXT_PUBLIC_APP_URL}/auth/callback`,
    },
  })
 
  if (error) throw new Error(error.message)
  return data
}
 
export async function signInWithEmail(email: string, password: string) {
  const { data, error } = await supabase.auth.signInWithPassword({
    email,
    password,
  })
 
  if (error) throw new Error(error.message)
  return data.session
}
 
export async function signOut() {
  const { error } = await supabase.auth.signOut()
  if (error) throw new Error(error.message)
}

For production, always enable email confirmation or OAuth to prevent unauthorized account creation.

User Profiles Table

Store additional user information beyond Supabase auth:

CREATE TABLE profiles (
  id UUID REFERENCES auth.users(id) ON DELETE CASCADE PRIMARY KEY,
  display_name TEXT,
  avatar_url TEXT,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
 
ALTER TABLE profiles ENABLE ROW LEVEL SECURITY;
 
CREATE POLICY "Users read own profile"
  ON profiles FOR SELECT
  USING (auth.uid() = id);
 
CREATE POLICY "Users update own profile"
  ON profiles FOR UPDATE
  USING (auth.uid() = id);
 
-- Auto-create profile on signup
CREATE FUNCTION handle_new_user()
RETURNS TRIGGER AS $$
BEGIN
  INSERT INTO public.profiles (id, display_name)
  VALUES (new.id, new.email);
  RETURN new;
END;
$$ LANGUAGE plpgsql SECURITY DEFINER;
 
CREATE TRIGGER on_auth_user_created
  AFTER INSERT ON auth.users
  FOR EACH ROW EXECUTE FUNCTION handle_new_user();

Building Semantic Search with pgvector

Enabling pgvector

Supabase includes pgvector by default. Enable it via the SQL editor:

CREATE EXTENSION IF NOT EXISTS vector;

Documents and Embeddings Schema

Design tables for storing documents and their vector embeddings:

CREATE TABLE documents (
  id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
  user_id UUID REFERENCES auth.users(id) ON DELETE CASCADE NOT NULL,
  title TEXT NOT NULL,
  content TEXT NOT NULL,
  source_url TEXT,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
 
CREATE TABLE document_chunks (
  id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
  document_id UUID REFERENCES documents(id) ON DELETE CASCADE NOT NULL,
  chunk_index INT NOT NULL,
  content TEXT NOT NULL,
  -- Gemini embedding-001 produces 768-dimensional vectors
  embedding vector(768),
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
 
-- Create HNSW index for fast semantic search
CREATE INDEX ON document_chunks
  USING hnsw (embedding vector_cosine_ops)
  WITH (m = 16, ef_construction = 200);
 
-- Enable RLS
ALTER TABLE documents ENABLE ROW LEVEL SECURITY;
ALTER TABLE document_chunks ENABLE ROW LEVEL SECURITY;
 
CREATE POLICY "Users access own documents"
  ON documents FOR SELECT
  USING (auth.uid() = user_id);
 
CREATE POLICY "Users access own chunks"
  ON document_chunks FOR SELECT
  USING (
    document_id IN (SELECT id FROM documents WHERE user_id = auth.uid())
  );

Semantic Search Query

Query similar chunks using cosine distance:

SELECT
  dc.id,
  dc.content,
  1 - (dc.embedding <=> query_embedding) AS similarity
FROM document_chunks dc
WHERE dc.document_id IN (
  SELECT id FROM documents WHERE user_id = auth.uid()
)
ORDER BY dc.embedding <=> query_embedding
LIMIT 5;

Wrap this as an RPC function for easier access from Edge Functions:

CREATE FUNCTION search_documents(
  query_embedding vector,
  user_id uuid,
  match_limit int DEFAULT 5
)
RETURNS TABLE (id uuid, content text, similarity float8) AS $$
BEGIN
  RETURN QUERY
  SELECT dc.id, dc.content, 1 - (dc.embedding <=> query_embedding)::float8
  FROM document_chunks dc
  WHERE dc.document_id IN (
    SELECT id FROM documents WHERE documents.user_id = search_documents.user_id
  )
  ORDER BY dc.embedding <=> query_embedding
  LIMIT match_limit;
END;
$$ LANGUAGE plpgsql;

Gemini Embeddings Pipeline

Document Upload → Embedding Workflow

When users upload documents, automatically chunk and embed them:

// supabase/functions/embed-document/index.ts
import { serve } from 'https://deno.land/std@0.168.0/http/server.ts'
import { createClient } from 'https://esm.sh/@supabase/supabase-js@2'
 
const supabaseUrl = Deno.env.get('SUPABASE_URL')!
const supabaseKey = Deno.env.get('SUPABASE_SERVICE_ROLE_KEY')!
const geminiApiKey = Deno.env.get('GEMINI_API_KEY')!
 
const supabase = createClient(supabaseUrl, supabaseKey)
 
// Split text into chunks (~500 tokens each)
function chunkText(text: string, maxTokens: number = 500): string[] {
  const words = text.split(/\s+/)
  const chunks: string[] = []
  let current = ''
 
  for (const word of words) {
    if ((current + ' ' + word).split(' ').length > maxTokens) {
      chunks.push(current)
      current = word
    } else {
      current += (current ? ' ' : '') + word
    }
  }
 
  if (current) chunks.push(current)
  return chunks
}
 
// Call Gemini Embedding API
async function generateEmbedding(text: string): Promise<number[]> {
  const response = await fetch(
    'https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent',
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'x-goog-api-key': geminiApiKey,
      },
      body: JSON.stringify({
        model: 'models/embedding-001',
        content: { parts: [{ text }] },
      }),
    }
  )
 
  if (!response.ok) {
    throw new Error(`Embedding API error: ${response.statusText}`)
  }
 
  const data = await response.json()
  return data.embedding.values
}
 
serve(async (req) => {
  const { documentId, content, userId } = await req.json()
 
  const chunks = chunkText(content, 500)
 
  for (let i = 0; i < chunks.length; i++) {
    const embedding = await generateEmbedding(chunks[i])
 
    const { error } = await supabase.from('document_chunks').insert({
      document_id: documentId,
      chunk_index: i,
      content: chunks[i],
      embedding,
    })
 
    if (error) {
      console.error('Insert error:', error)
      return new Response(`Error: ${error.message}`, { status: 500 })
    }
  }
 
  return new Response(
    JSON.stringify({ success: true, chunkCount: chunks.length }),
    { headers: { 'Content-Type': 'application/json' } }
  )
})

Batch Processing & Rate Limiting

Implement batching to stay within Gemini API rate limits:

serve(async (req) => {
  // Process pending chunks (max 10 per invocation)
  const { data: pending } = await supabase
    .from('document_chunks')
    .select('id, content')
    .is('embedding', null)
    .limit(10)
 
  for (const chunk of pending || []) {
    const embedding = await generateEmbedding(chunk.content)
    await supabase
      .from('document_chunks')
      .update({ embedding })
      .eq('id', chunk.id)
 
    // Add small delay between requests
    await new Promise((r) => setTimeout(r, 100))
  }
 
  return new Response('OK')
})

Building RAG with Edge Functions

RAG Chat Endpoint

Create an Edge Function that retrieves relevant documents and uses Gemini to generate answers:

// supabase/functions/rag-chat/index.ts
import { serve } from 'https://deno.land/std@0.168.0/http/server.ts'
import { createClient } from 'https://esm.sh/@supabase/supabase-js@2'
 
const supabaseUrl = Deno.env.get('SUPABASE_URL')!
const supabaseKey = Deno.env.get('SUPABASE_SERVICE_ROLE_KEY')!
const geminiApiKey = Deno.env.get('GEMINI_API_KEY')!
 
const supabase = createClient(supabaseUrl, supabaseKey)
 
async function generateEmbedding(text: string): Promise<number[]> {
  const response = await fetch(
    'https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent',
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'x-goog-api-key': geminiApiKey,
      },
      body: JSON.stringify({
        model: 'models/embedding-001',
        content: { parts: [{ text }] },
      }),
    }
  )
 
  const data = await response.json()
  return data.embedding.values
}
 
async function ragChat(
  userId: string,
  question: string,
  conversationId: string
): Promise<string> {
  // Step 1: Embed the user's question
  const questionEmbedding = await generateEmbedding(question)
 
  // Step 2: Semantic search for relevant documents
  const { data: relevantChunks } = await supabase.rpc('search_documents', {
    query_embedding: questionEmbedding,
    user_id: userId,
    match_limit: 5,
  })
 
  // Step 3: Build context from retrieved chunks
  const context = (relevantChunks || [])
    .map((chunk: any) => chunk.content)
    .join('\n\n')
 
  // Step 4: Call Gemini with context
  const response = await fetch(
    'https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent',
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'x-goog-api-key': geminiApiKey,
      },
      body: JSON.stringify({
        contents: [
          {
            parts: [
              {
                text: `Use the following context to answer the user's question.
 
Context:
${context}
 
Question: ${question}
 
Answer:`,
              },
            ],
          },
        ],
        generationConfig: {
          temperature: 0.7,
          maxOutputTokens: 1024,
        },
      }),
    }
  )
 
  const result = await response.json()
  return result.candidates[0].content.parts[0].text
}
 
serve(async (req) => {
  if (req.method !== 'POST') {
    return new Response('Method not allowed', { status: 405 })
  }
 
  const { userId, question, conversationId } = await req.json()
 
  try {
    const answer = await ragChat(userId, question, conversationId)
 
    // Save conversation history
    await supabase.from('messages').insert({
      conversation_id: conversationId,
      user_id: userId,
      role: 'user',
      content: question,
    })
 
    await supabase.from('messages').insert({
      conversation_id: conversationId,
      user_id: userId,
      role: 'assistant',
      content: answer,
    })
 
    return new Response(JSON.stringify({ answer }), {
      headers: { 'Content-Type': 'application/json' },
    })
  } catch (error: any) {
    return new Response(JSON.stringify({ error: error.message }), {
      status: 500,
      headers: { 'Content-Type': 'application/json' },
    })
  }
})

Real-Time Updates & Streaming

Supabase Realtime for Live Chat

Subscribe to new messages as they arrive:

// hooks/useRealtimeMessages.ts
import { useEffect, useState } from 'react'
import { supabase } from '@/lib/supabase'
 
export function useRealtimeMessages(conversationId: string) {
  const [messages, setMessages] = useState<any[]>([])
 
  useEffect(() => {
    // Fetch existing messages
    const loadMessages = async () => {
      const { data } = await supabase
        .from('messages')
        .select('*')
        .eq('conversation_id', conversationId)
        .order('created_at', { ascending: true })
 
      setMessages(data || [])
    }
 
    loadMessages()
 
    // Subscribe to new messages
    const channel = supabase
      .channel(`messages:${conversationId}`)
      .on(
        'postgres_changes',
        {
          event: 'INSERT',
          schema: 'public',
          table: 'messages',
          filter: `conversation_id=eq.${conversationId}`,
        },
        (payload) => {
          setMessages((prev) => [...prev, payload.new])
        }
      )
      .subscribe()
 
    return () => {
      supabase.removeChannel(channel)
    }
  }, [conversationId])
 
  return messages
}

Streaming Gemini Responses

Handle token-by-token streaming from Gemini API:

// lib/gemini-stream.ts
export async function* streamGeminiResponse(
  prompt: string,
  apiKey: string
) {
  const response = await fetch(
    'https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:streamGenerateContent?alt=sse',
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'x-goog-api-key': apiKey,
      },
      body: JSON.stringify({
        contents: [{ parts: [{ text: prompt }] }],
        generationConfig: {
          temperature: 0.7,
          maxOutputTokens: 1024,
        },
      }),
    }
  )
 
  // Parse Server-Sent Events
  const reader = response.body!.getReader()
  const decoder = new TextDecoder()
 
  while (true) {
    const { done, value } = await reader.read()
    if (done) break
 
    const chunk = decoder.decode(value)
    const lines = chunk.split('\n')
 
    for (const line of lines) {
      if (line.startsWith('data: ')) {
        const json = JSON.parse(line.slice(6))
        const text = json.candidates?.[0]?.content?.parts?.[0]?.text || ''
        if (text) yield text
      }
    }
  }
}

Use in a React component:

// components/StreamingChat.tsx
'use client'
 
import { useState } from 'react'
import { streamGeminiResponse } from '@/lib/gemini-stream'
 
export function StreamingChat() {
  const [response, setResponse] = useState('')
 
  const handleChat = async (prompt: string) => {
    setResponse('')
 
    for await (const chunk of streamGeminiResponse(
      prompt,
      process.env.NEXT_PUBLIC_GEMINI_API_KEY!
    )) {
      setResponse((prev) => prev + chunk)
    }
  }
 
  return (
    <div>
      <div className="whitespace-pre-wrap mb-4">{response}</div>
      <button onClick={() => handleChat('Hello!')}>Start Chat</button>
    </div>
  )
}

Row Level Security & Access Control

RLS Policies for Multi-Tenancy

Enable RLS on all tables and define policies:

CREATE TABLE conversations (
  id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
  user_id UUID REFERENCES auth.users(id) ON DELETE CASCADE NOT NULL,
  title TEXT,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
 
ALTER TABLE conversations ENABLE ROW LEVEL SECURITY;
 
CREATE POLICY "Users access own conversations"
  ON conversations FOR SELECT
  USING (auth.uid() = user_id);
 
CREATE POLICY "Users create conversations"
  ON conversations FOR INSERT
  WITH CHECK (auth.uid() = user_id);
 
CREATE POLICY "Users update own conversations"
  ON conversations FOR UPDATE
  USING (auth.uid() = user_id);
 
CREATE POLICY "Users delete own conversations"
  ON conversations FOR DELETE
  USING (auth.uid() = user_id);
 
-- Messages table
CREATE TABLE messages (
  id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
  conversation_id UUID REFERENCES conversations(id) ON DELETE CASCADE NOT NULL,
  user_id UUID REFERENCES auth.users(id) ON DELETE CASCADE NOT NULL,
  role TEXT CHECK (role IN ('user', 'assistant')),
  content TEXT NOT NULL,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
 
ALTER TABLE messages ENABLE ROW LEVEL SECURITY;
 
CREATE POLICY "Users access own conversation messages"
  ON messages FOR SELECT
  USING (
    conversation_id IN (
      SELECT id FROM conversations WHERE user_id = auth.uid()
    )
  );

Token Verification in Edge Functions

Verify JWT tokens before processing requests:

import * as jose from 'https://deno.land/x/jose@v4.14.1/index.ts'
 
async function verifyToken(token: string) {
  try {
    const secret = new TextEncoder().encode(Deno.env.get('SUPABASE_JWT_SECRET')!)
    const verified = await jose.jwtVerify(token, secret)
    return verified.payload.sub // User ID
  } catch {
    throw new Error('Invalid token')
  }
}
 
serve(async (req) => {
  const authHeader = req.headers.get('Authorization')
  if (!authHeader) return new Response('Unauthorized', { status: 401 })
 
  const token = authHeader.replace('Bearer ', '')
  const userId = await verifyToken(token)
 
  // Now safely process request with verified userId
})

Deployment & Optimization

Caching Strategy

Cache embeddings to reduce API costs:

// lib/embedding-cache.ts
export async function getOrCreateEmbedding(text: string) {
  const hash = await hashText(text)
 
  // Check cache
  const { data: cached } = await supabase
    .from('embedding_cache')
    .select('embedding')
    .eq('text_hash', hash)
    .single()
 
  if (cached) return cached.embedding
 
  // Generate and cache
  const embedding = await generateEmbedding(text)
  await supabase.from('embedding_cache').insert({
    text_hash: hash,
    text,
    embedding,
  })
 
  return embedding
}
 
async function hashText(text: string): Promise<string> {
  const buffer = await crypto.subtle.digest(
    'SHA-256',
    new TextEncoder().encode(text)
  )
  return Array.from(new Uint8Array(buffer))
    .map((b) => b.toString(16).padStart(2, '0'))
    .join('')
}

Index Optimization

Use HNSW indexes for fast semantic search:

-- Drop old IVFFLAT and create HNSW
DROP INDEX IF EXISTS document_chunks_embedding_idx;
 
CREATE INDEX ON document_chunks
  USING hnsw (embedding vector_cosine_ops)
  WITH (m = 16, ef_construction = 200);
 
-- Update statistics
ANALYZE document_chunks;

Monitoring & Observability

Log API usage and track performance:

CREATE TABLE api_usage (
  id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
  user_id UUID REFERENCES auth.users(id),
  api_type TEXT CHECK (api_type IN ('embedding', 'generation')),
  tokens_used INT,
  cost DECIMAL(10, 6),
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
 
-- Monthly aggregation
CREATE VIEW monthly_usage AS
SELECT
  user_id,
  DATE_TRUNC('month', created_at) AS month,
  api_type,
  SUM(tokens_used) AS total_tokens,
  SUM(cost) AS total_cost
FROM api_usage
GROUP BY user_id, DATE_TRUNC('month', created_at), api_type;

Cost Management & Monitoring

Reducing Gemini API Expenses

Batch embeddings: Process multiple texts in one request

async function batchEmbeddings(texts: string[]) {
  const requests = texts.map((text) => ({
    model: 'models/embedding-001',
    content: { parts: [{ text }] },
  }))
 
  const response = await fetch(
    'https://generativelanguage.googleapis.com/v1beta/models/embedding-batch:batchEmbedContent',
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'x-goog-api-key': Deno.env.get('GEMINI_API_KEY')!,
      },
      body: JSON.stringify({ requests }),
    }
  )
 
  return (await response.json()).embeddings
}

Model selection: Use gemini-1.5-flash for generation and embedding-001 for embeddings—both are cost-effective.

Retry strategy: Implement exponential backoff for rate limits

async function callGeminiWithRetry(
  url: string,
  options: RequestInit,
  maxRetries: number = 5
) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    const response = await fetch(url, options)
 
    if (response.status === 429) {
      const delay = Math.pow(2, attempt) * 1000
      await new Promise((r) => setTimeout(r, delay))
      continue
    }
 
    return response
  }
 
  throw new Error('Max retries exceeded')
}

Usage Dashboard

Display API usage to users:

// lib/usage.ts
export async function getMonthlyUsage(userId: string) {
  const { data } = await supabase
    .from('monthly_usage')
    .select('*')
    .eq('user_id', userId)
    .eq('month', new Date().toISOString().slice(0, 7))
 
  return data || []
}

Error Logging & Alerts

Capture and monitor Edge Function errors:

serve(async (req) => {
  try {
    // Main logic
  } catch (error: any) {
    await supabase.from('function_logs').insert({
      function_name: 'rag-chat',
      error_message: error.message,
      error_stack: error.stack,
      created_at: new Date(),
    })
 
    return new Response(JSON.stringify({ error: error.message }), {
      status: 500,
      headers: { 'Content-Type': 'application/json' },
    })
  }
})

What the docs don't tell you: lessons from production

Everything above runs as-is, but once your data grows or real users show up, you'll need adjustments the quickstart never covers. Here they are, in the order I actually hit them.

1. Revisit your pgvector index once row counts grow

IVFFlat with lists = 100 was fine at first. But somewhere around 120,000 documents (up from 10,000), the same query started surfacing noticeably worse results. Measuring recall by hand (how many of my known top-5 came back), it had dropped from ~0.95 to ~0.78.

IVFFlat requires you to scale lists with your data (a rough rule is rows / 1000); leave it alone and the search clusters get too coarse and miss hits. For an indie app where row counts are unpredictable, switching to HNSW — which needs no count-dependent tuning — made operations much simpler.

-- Move from IVFFlat (needs lists re-tuning as rows grow)
-- to HNSW (count-independent, stable recall)
DROP INDEX IF EXISTS documents_embedding_idx;
 
CREATE INDEX documents_embedding_idx
  ON documents
  USING hnsw (embedding vector_cosine_ops)
  WITH (m = 16, ef_construction = 64);
 
-- Tune precision/speed per session at query time
SET hnsw.ef_search = 40;  -- default 40; raise to 80-100 only when you need more recall

After the switch, recall recovered to ~0.93 on the same data. The trade-off: roughly 2x index build time and ~30% more storage. For a read-heavy RAG workload, that's an easy trade.

2. The service_role key silently bypasses RLS

This one gave me a scare. If you use a service_role Supabase client inside an Edge Function, the Row Level Security you carefully set up is completely ignored. One day, reading logs, I realized the setup could return one user's conversations mixed into another's.

The fix: verify the request JWT and touch the database through a user-scoped client that carries that JWT. Reserve service_role for admin-only work like embedding generation.

// This bypasses RLS — a multi-tenant accident waiting to happen
const admin = createClient(SUPABASE_URL, SERVICE_ROLE_KEY)
 
// Forward the request's Authorization header
//   -> RLS automatically scopes queries to that user's rows
const userClient = createClient(SUPABASE_URL, ANON_KEY, {
  global: { headers: { Authorization: req.headers.get('Authorization')! } },
})
 
const { data: { user } } = await userClient.auth.getUser()
if (!user) {
  return new Response('Unauthorized', { status: 401 })
}
// All reads/writes via userClient are now confined to this user by RLS

To stay "deny by default," separate your admin and user clients clearly and keep the number of functions that touch service_role small enough to count.

3. Design embedding batches assuming 429s

During the initial bulk load, text-embedding-004 returned 429 (rate limited) almost immediately. Running without a concurrency cap, I plateaued around 1,500 requests per minute. Capping concurrency at 5 and adding exponential backoff let the job run to completion without stalling.

// Cap concurrency and absorb 429s with exponential backoff
async function embedWithRetry(text: string, attempt = 0): Promise<number[]> {
  try {
    const res = await ai.models.embedContent({
      model: 'text-embedding-004',
      contents: text,
    })
    return res.embeddings[0].values
  } catch (e: any) {
    if (e.status === 429 && attempt < 5) {
      const waitMs = Math.min(2 ** attempt * 500, 16000) + Math.random() * 300
      await new Promise((r) => setTimeout(r, waitMs))
      return embedWithRetry(text, attempt + 1)
    }
    throw e
  }
}

The key detail is the small random jitter on the retry delay. If several workers back off on the same cycle, they'll all hit 429 again together.

4. Stream from Edge Functions — return early

Supabase Edge Functions (Deno) have an execution-time ceiling, and waiting for Gemini's entire response before returning can get truncated on long generations. Piping tokens out through a ReadableStream as they arrive lowers perceived latency and leaves headroom under the limit. In my RAG chat, time-to-first-token ran around 0.9s median and ~2s at p95 with Flash.

Pre-launch checklist I always run

Is the pgvector index HNSW — or, if IVFFlat, is lists sized to the row count?
Does every DB access in an Edge Function use a user-scoped client (no service_role abuse)?
Do embedding batches have a concurrency cap and backoff?
Are Gemini responses streamed and returned early?
Do errors land in something like function_logs and feed an alert?

What it actually costs

For reference, a small app with around 8,000 monthly active users runs me roughly ¥4,000-6,000 / month combined for Gemini (embeddings + Flash for RAG) and Supabase (Pro plan). I deliberately design the in-app AdMob revenue to cover this infrastructure, so each month I check this number to confirm per-user inference cost stays under ad ARPU.

Embeddings are a one-time cost you reuse, so most of the spend is on the RAG response side. Caching frequent questions, using Flash for summaries, and routing only genuinely hard reasoning to Pro kept costs down 30-40% with no noticeable quality drop.

Wrapping up — your next step

Wire up one RAG chat endpoint on a small dataset, then deliberately grow your documents 10x and measure how recall and response time shift. The issues that only appear when scale changes are exactly the ones that matter in production.

I hope this helps anyone trying to take an indie app all the way to production on their own. Thanks for reading.

Setup and context — Why Nuxt 3 × Gemini API?

Nuxt 3, the flagship full-stack framework for the Vue.js ecosystem, continues to attract a wide range of developers in 2026. Its file-system-based routing, automatic code splitting, and powerful SSR capabilities allow you to manage frontend and backend in a unified codebase — making it a compelling choice for AI-powered applications.

When it comes to integrating AI features, Nuxt 3 has some distinct advantages. The "server routes" feature — where you drop a TypeScript file under server/api/ and it instantly becomes a secure API endpoint — provides an ideal foundation for calling Gemini API without exposing API keys to the client side. Additionally, the Composable pattern (useXxx) abstracts state management in a way that dramatically reduces the complexity of implementing multi-turn chat and real-time streaming UIs.

Nuxt 3's Nitro server engine is another key differentiator. Nitro compiles your server code to run on a wide range of targets — Node.js, Cloudflare Workers, Vercel Edge Functions, AWS Lambda, and more — without changing a single line of application code. This makes Nuxt 3 exceptionally well-suited for global-scale AI applications where latency and deployment flexibility are paramount.

This guide sets out to help you build a Nuxt 3 × Gemini API full-stack AI app that runs stably in production — not just a working demo. We'll walk through the full engineering lifecycle: project setup, secure server route design, SSE streaming, multi-turn conversation management, rate limiting, authentication middleware, deployment to three different hosting targets, error handling with exponential backoff, and cost optimization strategies. By the end, you'll have a blueprint you can adapt to build your own AI products.

Project Setup and Environment Configuration

Creating a Nuxt 3 Project

Start by scaffolding a fresh Nuxt 3 project using the official nuxi CLI. The Gemini JavaScript/TypeScript SDK (@google/genai) is the recommended way to interact with the Gemini API.

# Create a new Nuxt 3 project
npx nuxi@latest init gemini-nuxt-app
cd gemini-nuxt-app
 
# Install the Gemini API SDK
npm install @google/genai
 
# Install Pinia for state management (optional but recommended)
npm install pinia @pinia/nuxt
 
# Start the dev server
npm run dev

Your project now has a working Nuxt 3 setup. Open http://localhost:3000 to confirm everything is running.

Project Structure Overview

A well-organized Nuxt 3 AI project typically looks like this:

gemini-nuxt-app/
├── server/
│   ├── api/
│   │   ├── chat.post.ts          # Basic chat endpoint
│   │   ├── chat-stream.post.ts   # Streaming chat endpoint
│   │   └── chat-multi.post.ts    # Multi-turn chat endpoint
│   ├── middleware/
│   │   ├── rate-limit.ts         # Rate limiting
│   │   └── auth.ts               # JWT authentication
│   └── utils/
│       └── retry.ts              # Retry helper
├── composables/
│   ├── useGeminiStream.ts        # Streaming composable
│   └── useChat.ts                # Chat state composable
├── stores/
│   └── chat.ts                   # Pinia chat store
├── components/
│   ├── ChatWindow.vue
│   └── ChatInput.vue
├── pages/
│   └── index.vue
├── nuxt.config.ts
└── .env

Environment Variable Setup

Never hardcode your Gemini API key. Store it in a .env file and access it exclusively from the server side using Nuxt's runtimeConfig.

# .env — add this to .gitignore immediately
GEMINI_API_KEY=YOUR_GEMINI_API_KEY

// nuxt.config.ts
export default defineNuxtConfig({
  modules: ['@pinia/nuxt'],
 
  runtimeConfig: {
    // Server-side only — never exposed to the client bundle
    geminiApiKey: process.env.GEMINI_API_KEY,
    rateLimitMax: 10,        // Requests per window
    rateLimitWindowMs: 60000, // Window size in ms
 
    // Public config: accessible on both client and server
    public: {
      appName: 'Gemini Nuxt App',
      streamingEnabled: true
    }
  },
 
  nitro: {
    // Target Cloudflare Pages for production (change per deploy target)
    preset: 'cloudflare-pages'
  }
})

With this configuration, runtimeConfig.geminiApiKey is only accessible from within server routes. Attempting to read it in a Vue component returns undefined, so the key can never leak into the client bundle — a critical security property for any production AI application.

Secure Gemini API Integration via Server Routes

Basic Chat Endpoint

The foundation of any Gemini-powered Nuxt app is a secure server route. Here's a production-ready implementation with proper input validation, error mapping, and response shaping.

// server/api/chat.post.ts
import { GoogleGenAI } from '@google/genai'
 
export default defineEventHandler(async (event) => {
  const config = useRuntimeConfig()
  const body = await readBody(event)
 
  // --- Input Validation ---
  if (!body.message || typeof body.message !== 'string') {
    throw createError({
      statusCode: 400,
      statusMessage: 'Bad Request',
      message: 'message field is required and must be a string'
    })
  }
  if (body.message.trim().length === 0) {
    throw createError({ statusCode: 400, message: 'message cannot be empty' })
  }
  if (body.message.length > 8000) {
    throw createError({ statusCode: 400, message: 'message exceeds maximum length of 8000 characters' })
  }
 
  // --- Gemini API Call ---
  const genai = new GoogleGenAI({ apiKey: config.geminiApiKey })
 
  try {
    const response = await genai.models.generateContent({
      model: 'gemini-2.5-flash',
      contents: body.message,
      config: {
        systemInstruction: body.systemInstruction || 'You are a helpful, knowledgeable assistant.',
        maxOutputTokens: 2048,
        temperature: body.temperature ?? 0.7,
        candidateCount: 1
      }
    })
 
    return {
      text: response.text,
      usageMetadata: {
        promptTokens: response.usageMetadata?.promptTokenCount,
        outputTokens: response.usageMetadata?.candidatesTokenCount,
        totalTokens: response.usageMetadata?.totalTokenCount
      },
      model: 'gemini-2.5-flash',
      timestamp: new Date().toISOString()
    }
  } catch (error: any) {
    // Map Gemini API errors to appropriate HTTP status codes
    const statusMap: Record<number, number> = {
      400: 400, // Invalid request
      401: 401, // Bad API key
      403: 403, // Permission denied
      404: 404, // Model not found
      429: 429, // Quota exceeded
      500: 502, // Gemini server error → 502 Bad Gateway
      503: 503  // Service unavailable
    }
 
    throw createError({
      statusCode: statusMap[error.status] ?? 500,
      message: error.status === 429
        ? 'Gemini API quota exceeded. Please wait before retrying.'
        : `AI generation failed: ${error.message}`
    })
  }
})

Calling the Route from Vue Components

<!-- pages/index.vue -->
<script setup lang="ts">
const message = ref('')
const response = ref<{text: string; usageMetadata: any} | null>(null)
const loading = ref(false)
const error = ref('')
 
async function sendMessage() {
  if (!message.value.trim() || loading.value) return
 
  loading.value = true
  error.value = ''
  response.value = null
 
  try {
    // $fetch is Nuxt's type-safe fetch wrapper
    response.value = await $fetch('/api/chat', {
      method: 'POST',
      body: { message: message.value }
    })
    message.value = ''
  } catch (err: any) {
    error.value = err.data?.message || 'An unexpected error occurred'
  } finally {
    loading.value = false
  }
}
</script>
 
<template>
  <div class="container">
    <div v-if="response" class="response-card">
      <p>{{ response.text }}</p>
      <small v-if="response.usageMetadata">
        Tokens: {{ response.usageMetadata.totalTokens }}
      </small>
    </div>
    <div v-if="error" class="error-banner">{{ error }}</div>
    <div class="input-row">
      <textarea
        v-model="message"
        placeholder="Ask Gemini anything..."
        @keydown.ctrl.enter="sendMessage"
        rows="3"
      />
      <button @click="sendMessage" :disabled="loading || !message.trim()">
        {{ loading ? 'Generating...' : 'Send' }}
      </button>
    </div>
  </div>
</template>

Implementing Streaming Responses with Server-Sent Events

Streaming is one of the most impactful factors for AI app UX. Instead of waiting for the full response before rendering, displaying tokens as they're generated dramatically improves perceived speed and keeps users engaged. With Nuxt 3's sendStream helper and the browser's native ReadableStream API, implementing SSE streaming is surprisingly clean.

Streaming-Enabled Server Route

// server/api/chat-stream.post.ts
import { GoogleGenAI } from '@google/genai'
 
export default defineEventHandler(async (event) => {
  const config = useRuntimeConfig()
  const body = await readBody(event)
 
  if (!body.message) {
    throw createError({ statusCode: 400, message: 'message is required' })
  }
 
  // Configure SSE headers
  setHeader(event, 'Content-Type', 'text/event-stream')
  setHeader(event, 'Cache-Control', 'no-cache, no-transform')
  setHeader(event, 'Connection', 'keep-alive')
  setHeader(event, 'X-Accel-Buffering', 'no') // Critical: disables nginx/proxy buffering
 
  const genai = new GoogleGenAI({ apiKey: config.geminiApiKey })
 
  return sendStream(event, async (stream) => {
    let totalTokens = 0
 
    try {
      const result = await genai.models.generateContentStream({
        model: 'gemini-2.5-flash',
        contents: body.message,
        config: {
          systemInstruction: body.systemInstruction,
          maxOutputTokens: body.maxTokens ?? 2048,
          temperature: body.temperature ?? 0.7
        }
      })
 
      for await (const chunk of result) {
        const text = chunk.text
        if (text) {
          stream.write(`data: ${JSON.stringify({ type: 'text', text })}\n\n`)
        }
        // Accumulate usage from the final chunk
        if (chunk.usageMetadata?.totalTokenCount) {
          totalTokens = chunk.usageMetadata.totalTokenCount
        }
      }
 
      // Send completion event with metadata
      stream.write(
        `data: ${JSON.stringify({ type: 'done', totalTokens })}\n\n`
      )
    } catch (error: any) {
      stream.write(
        `data: ${JSON.stringify({ type: 'error', message: error.message })}\n\n`
      )
    } finally {
      stream.end()
    }
  })
})

Consuming the Stream with a Composable

Encapsulating stream consumption in a composable makes it reusable across different UI components:

// composables/useGeminiStream.ts
export function useGeminiStream() {
  const streamText = ref('')
  const isStreaming = ref(false)
  const streamError = ref('')
  const tokenCount = ref(0)
 
  async function startStream(
    message: string,
    options: { systemInstruction?: string; maxTokens?: number } = {}
  ) {
    isStreaming.value = true
    streamText.value = ''
    streamError.value = ''
    tokenCount.value = 0
 
    try {
      const response = await fetch('/api/chat-stream', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ message, ...options })
      })
 
      if (!response.ok) {
        throw new Error(`HTTP ${response.status}`)
      }
 
      const reader = response.body!.getReader()
      const decoder = new TextDecoder()
      let buffer = ''
 
      while (true) {
        const { value, done } = await reader.read()
        if (done) break
 
        buffer += decoder.decode(value, { stream: true })
        const lines = buffer.split('\n')
        buffer = lines.pop() ?? ''
 
        for (const line of lines) {
          if (!line.startsWith('data: ')) continue
          try {
            const event = JSON.parse(line.slice(6))
            if (event.type === 'text') {
              streamText.value += event.text
            } else if (event.type === 'done') {
              tokenCount.value = event.totalTokens ?? 0
              isStreaming.value = false
            } else if (event.type === 'error') {
              streamError.value = event.message
              isStreaming.value = false
            }
          } catch {
            // Skip malformed JSON
          }
        }
      }
    } catch (err: any) {
      streamError.value = err.message
      isStreaming.value = false
    }
  }
 
  function reset() {
    streamText.value = ''
    streamError.value = ''
    tokenCount.value = 0
  }
 
  return { streamText, isStreaming, streamError, tokenCount, startStream, reset }
}

Streaming UI Component

<!-- components/StreamingChat.vue -->
<script setup lang="ts">
const { streamText, isStreaming, streamError, tokenCount, startStream, reset } = useGeminiStream()
const message = ref('')
 
async function handleSend() {
  if (!message.value.trim() || isStreaming.value) return
  const msg = message.value
  message.value = ''
  reset()
  await startStream(msg)
}
</script>
 
<template>
  <div class="streaming-chat">
    <!-- Live token display with cursor indicator -->
    <div class="response-area" v-if="streamText || isStreaming">
      <span>{{ streamText }}</span>
      <span v-if="isStreaming" class="cursor">▍</span>
    </div>
    <div class="error-area" v-if="streamError">{{ streamError }}</div>
    <div class="meta" v-if="!isStreaming && tokenCount">
      Tokens used: {{ tokenCount }}
    </div>
    <div class="input-area">
      <textarea
        v-model="message"
        @keydown.ctrl.enter="handleSend"
        placeholder="Message (Ctrl+Enter to send)"
      />
      <button @click="handleSend" :disabled="isStreaming">
        {{ isStreaming ? 'Streaming...' : 'Send' }}
      </button>
    </div>
  </div>
</template>

Multi-Turn Chat and State Management with Pinia

Managing conversation history correctly is essential for a coherent multi-turn experience. Here's a production-ready Pinia store that handles message persistence and token budgeting.

// stores/chat.ts
import { defineStore } from 'pinia'
 
interface Message {
  id: string
  role: 'user' | 'model'
  content: string
  timestamp: number
  tokenCount?: number
}
 
const MAX_HISTORY_MESSAGES = 20  // Limit to control token costs
 
export const useChatStore = defineStore('chat', () => {
  const messages = ref<Message[]>([])
  const sessionId = ref(crypto.randomUUID())
  const totalTokensUsed = ref(0)
 
  function addMessage(role: Message['role'], content: string, tokenCount?: number) {
    messages.value.push({
      id: crypto.randomUUID(),
      role,
      content,
      timestamp: Date.now(),
      tokenCount
    })
 
    // Trim history to limit token usage
    if (messages.value.length > MAX_HISTORY_MESSAGES) {
      messages.value = messages.value.slice(-MAX_HISTORY_MESSAGES)
    }
 
    if (tokenCount) totalTokensUsed.value += tokenCount
  }
 
  function clearChat() {
    messages.value = []
    sessionId.value = crypto.randomUUID()
    totalTokensUsed.value = 0
  }
 
  // Shape history for the Gemini API `contents` field
  const apiHistory = computed(() =>
    messages.value.map(m => ({
      role: m.role,
      parts: [{ text: m.content }]
    }))
  )
 
  return {
    messages,
    sessionId,
    totalTokensUsed,
    addMessage,
    clearChat,
    apiHistory
  }
})

// server/api/chat-multi.post.ts
import { GoogleGenAI } from '@google/genai'
 
export default defineEventHandler(async (event) => {
  const config = useRuntimeConfig()
  const { message, history = [], systemInstruction } = await readBody(event)
 
  if (!message || typeof message !== 'string') {
    throw createError({ statusCode: 400, message: 'message is required' })
  }
 
  const genai = new GoogleGenAI({ apiKey: config.geminiApiKey })
 
  // Build the contents array from history + current message
  const contents = [
    ...history.slice(-20), // Safety cap on history length
    { role: 'user', parts: [{ text: message }] }
  ]
 
  const response = await genai.models.generateContent({
    model: 'gemini-2.5-flash',
    contents,
    config: {
      systemInstruction: systemInstruction || 'You are a helpful, concise assistant.',
      maxOutputTokens: 2048
    }
  })
 
  return {
    text: response.text,
    tokenCount: response.usageMetadata?.totalTokenCount
  }
})

Rate Limiting and Authentication Middleware

Layered Rate Limiting

For a real production deployment, rate limiting should be applied in two layers: per-IP (to prevent abuse) and per-user (to enforce subscription tiers).

// server/middleware/rate-limit.ts
interface RateLimitRecord {
  count: number
  resetAt: number
}
 
// In production: replace with Cloudflare KV or Redis
const ipCounters = new Map<string, RateLimitRecord>()
 
function getRateLimitConfig(path: string) {
  // Stricter limits for the streaming endpoint
  if (path.includes('stream')) return { max: 5, windowMs: 60_000 }
  return { max: 15, windowMs: 60_000 }
}
 
export default defineEventHandler((event) => {
  if (!event.path.startsWith('/api/chat')) return
 
  const { max, windowMs } = getRateLimitConfig(event.path)
  const ip =
    getHeader(event, 'cf-connecting-ip') ||           // Cloudflare real IP
    getHeader(event, 'x-forwarded-for')?.split(',')[0].trim() ||
    event.node.req.socket.remoteAddress ||
    'unknown'
 
  const now = Date.now()
  const record = ipCounters.get(ip)
 
  if (!record || now > record.resetAt) {
    ipCounters.set(ip, { count: 1, resetAt: now + windowMs })
    return
  }
 
  if (record.count >= max) {
    const retryAfter = Math.ceil((record.resetAt - now) / 1000)
    setHeader(event, 'Retry-After', String(retryAfter))
    setHeader(event, 'X-RateLimit-Limit', String(max))
    setHeader(event, 'X-RateLimit-Remaining', '0')
    setHeader(event, 'X-RateLimit-Reset', String(Math.ceil(record.resetAt / 1000)))
    throw createError({
      statusCode: 429,
      message: `Rate limit exceeded. Please retry after ${retryAfter} seconds.`
    })
  }
 
  record.count++
  setHeader(event, 'X-RateLimit-Remaining', String(max - record.count))
})

API Key Authentication for Machine Clients

// server/middleware/auth.ts
const PUBLIC_PATHS = ['/api/health', '/api/status']
 
export default defineEventHandler(async (event) => {
  if (!event.path.startsWith('/api/chat')) return
  if (PUBLIC_PATHS.includes(event.path)) return
 
  const apiKey = getHeader(event, 'x-api-key')
  const authHeader = getHeader(event, 'authorization')
  const bearerToken = authHeader?.startsWith('Bearer ') ? authHeader.slice(7) : null
 
  if (!apiKey && !bearerToken) {
    throw createError({
      statusCode: 401,
      message: 'Authentication required. Provide X-API-Key or Authorization: Bearer header.'
    })
  }
 
  // Validate against your user database or KV store
  // const isValid = await validateApiKey(apiKey ?? bearerToken!)
  // if (!isValid) throw createError({ statusCode: 403, message: 'Invalid credentials' })
})

Production Deployment Strategies

Cloudflare Pages + Workers (Recommended)

Cloudflare Pages with Workers offers edge-distributed execution, meaning your Nuxt server routes run in data centers close to your users worldwide — ideal for latency-sensitive AI streaming applications.

# Build for Cloudflare Pages
NITRO_PRESET=cloudflare-pages npm run build
 
# Deploy using Wrangler
npm install -D wrangler
npx wrangler pages deploy .output/public --project-name=gemini-nuxt-app

# wrangler.toml
name = "gemini-nuxt-app"
compatibility_date = "2026-04-01"
compatibility_flags = ["nodejs_compat"]
 
[vars]
APP_ENV = "production"
NODE_ENV = "production"
 
# Never put secrets here — use `wrangler secret put` instead

# Store the API key securely as a Workers Secret
npx wrangler secret put GEMINI_API_KEY
# Paste your key when prompted (it's encrypted at rest)

For the rate limiter in a Workers environment, replace the in-memory Map with Cloudflare KV:

// server/middleware/rate-limit.ts (Cloudflare KV version)
import { getCloudflareContext } from '@opennextjs/cloudflare'
 
export default defineEventHandler(async (event) => {
  if (!event.path.startsWith('/api/chat')) return
 
  const { env } = getCloudflareContext()
  const ip = getHeader(event, 'cf-connecting-ip') ?? 'unknown'
  const key = `rate:${ip}`
 
  const current = Number(await env.KV.get(key) ?? '0')
  if (current >= 10) {
    throw createError({ statusCode: 429, message: 'Rate limit exceeded' })
  }
 
  await env.KV.put(key, String(current + 1), { expirationTtl: 60 })
})

Vercel Deployment

npm install -g vercel
vercel --prod

Set the GEMINI_API_KEY environment variable in the Vercel dashboard under Project Settings → Environment Variables. Vercel automatically injects it at build and runtime.

// vercel.json
{
  "framework": "nuxtjs",
  "buildCommand": "npm run build",
  "outputDirectory": ".output/public",
  "functions": {
    "server/**/*.ts": {
      "maxDuration": 30
    }
  }
}

Docker for Self-Hosted Deployments

Self-hosting gives you full control over infrastructure and is often the most cost-effective option at scale.

# Dockerfile — multi-stage build for minimal image size
FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --frozen-lockfile
COPY . .
ENV NITRO_PRESET=node-server
RUN npm run build
 
FROM node:20-alpine AS runner
WORKDIR /app
RUN addgroup -S appgroup && adduser -S appuser -G appgroup
COPY --from=builder --chown=appuser:appgroup /app/.output ./.output
USER appuser
ENV NODE_ENV=production
ENV PORT=3000
ENV HOST=0.0.0.0
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=3s \
  CMD wget -qO- http://localhost:3000/api/health || exit 1
CMD ["node", ".output/server/index.mjs"]

docker build -t gemini-nuxt-app:latest .
docker run -d \
  -e GEMINI_API_KEY=YOUR_KEY \
  -p 3000:3000 \
  --name gemini-nuxt \
  gemini-nuxt-app:latest

Error Handling and Cost Optimization

Exponential Backoff for Transient Errors

Transient errors from the Gemini API (HTTP 429 and 503) should be handled with exponential backoff and jitter. For a detailed treatment of retry patterns, see the Gemini API Error Handling and Retry Patterns Complete Guide.

// server/utils/retry.ts
export interface RetryOptions {
  maxAttempts?: number
  baseDelayMs?: number
  maxDelayMs?: number
  retryableStatuses?: number[]
}
 
export async function withRetry<T>(
  fn: () => Promise<T>,
  options: RetryOptions = {}
): Promise<T> {
  const {
    maxAttempts = 3,
    baseDelayMs = 1000,
    maxDelayMs = 30_000,
    retryableStatuses = [429, 503, 502]
  } = options
 
  let lastError: any
 
  for (let attempt = 1; attempt <= maxAttempts; attempt++) {
    try {
      return await fn()
    } catch (error: any) {
      lastError = error
      const isRetryable = retryableStatuses.includes(error.status)
 
      if (!isRetryable || attempt === maxAttempts) {
        throw error
      }
 
      const exponentialDelay = baseDelayMs * Math.pow(2, attempt - 1)
      const jitter = Math.random() * 1000
      const delay = Math.min(exponentialDelay + jitter, maxDelayMs)
 
      console.warn(
        `[Retry] Attempt ${attempt}/${maxAttempts} failed (${error.status}). Retrying in ${Math.round(delay)}ms`
      )
 
      await new Promise(r => setTimeout(r, delay))
    }
  }
 
  throw lastError
}

Usage in a server route:

// server/api/chat.post.ts (with retry)
import { withRetry } from '~/server/utils/retry'
 
const response = await withRetry(
  () => genai.models.generateContent({ model: 'gemini-2.5-flash', contents: body.message }),
  { maxAttempts: 3, baseDelayMs: 500 }
)

Practical Cost Optimization Strategies

Unoptimized Gemini API usage can lead to surprisingly high bills. Here are the most impactful techniques. For a comprehensive cost breakdown, see the Gemini API Cost Optimization Complete Guide.

1. Always cap maxOutputTokens

The default unlimited output can generate thousands of tokens for simple requests. Set a reasonable cap:

config: {
  maxOutputTokens: 512,   // For Q&A bots
  maxOutputTokens: 1024,  // For content generation
  maxOutputTokens: 2048,  // For long-form tasks
  temperature: 0.5        // Lower for factual tasks
}

2. Use Gemini 2.5 Flash as the default

gemini-2.5-flash is ~10× cheaper than gemini-2.5-pro and handles the majority of use cases excellently. Only escalate to Pro when you need complex multi-step reasoning.

3. Implement server-side response caching

// server/utils/cache.ts
const cache = new Map<string, { text: string; expiresAt: number }>()
const CACHE_TTL_MS = 5 * 60 * 1000  // 5 minutes
 
export function getCached(key: string): string | null {
  const entry = cache.get(key)
  if (!entry || Date.now() > entry.expiresAt) {
    cache.delete(key)
    return null
  }
  return entry.text
}
 
export function setCached(key: string, text: string) {
  cache.set(key, { text, expiresAt: Date.now() + CACHE_TTL_MS })
}

// In your server route — check cache before calling the API
const cacheKey = `chat:${JSON.stringify({ message: body.message, model: 'gemini-2.5-flash' })}`
const cached = getCached(cacheKey)
if (cached) return { text: cached, cached: true }
 
const response = await genai.models.generateContent(/* ... */)
setCached(cacheKey, response.text)
return { text: response.text, cached: false }

4. Leverage Context Caching for fixed content

When your app uses a long, fixed system prompt or knowledge base, cache it at the API level:

const cachedContent = await genai.caches.create({
  model: 'gemini-2.5-flash',
  systemInstruction: 'You are a customer support agent for Acme Corp...',
  contents: [/* your knowledge base */],
  ttl: '3600s'  // 1-hour cache
})
 
// Use the cached content in subsequent requests
const response = await genai.models.generateContent({
  model: 'gemini-2.5-flash',
  cachedContent: cachedContent.name,
  contents: userMessage
})

Monitoring Token Usage

// Log token usage for cost attribution
const usage = response.usageMetadata
console.log(JSON.stringify({
  event: 'gemini_usage',
  promptTokens: usage?.promptTokenCount,
  outputTokens: usage?.candidatesTokenCount,
  totalTokens: usage?.totalTokenCount,
  model: 'gemini-2.5-flash',
  userId: event.context.userId,
  path: event.path
}))

Testing Your Nuxt 3 AI Application

A production AI app without tests is an accident waiting to happen. Here's how to test both the server routes and Vue components effectively.

Unit Testing Server Routes with Vitest

// tests/api/chat.test.ts
import { describe, it, expect, vi, beforeEach } from 'vitest'
 
// Mock the Gemini SDK before importing anything else
vi.mock('@google/genai', () => ({
  GoogleGenAI: vi.fn().mockImplementation(() => ({
    models: {
      generateContent: vi.fn().mockResolvedValue({
        text: 'Mocked AI response',
        usageMetadata: { promptTokenCount: 10, candidatesTokenCount: 5, totalTokenCount: 15 }
      })
    }
  }))
}))
 
// Mock Nuxt's runtimeConfig
vi.mock('#imports', () => ({
  useRuntimeConfig: () => ({ geminiApiKey: 'test-key-123' }),
  defineEventHandler: (fn: Function) => fn,
  readBody: vi.fn(),
  createError: (opts: any) => new Error(opts.message)
}))
 
describe('POST /api/chat', () => {
  it('returns AI response for valid input', async () => {
    const { readBody } = await import('#imports')
    vi.mocked(readBody).mockResolvedValue({ message: 'Hello, Gemini!' })
 
    const handler = (await import('~/server/api/chat.post')).default
    const result = await handler({} as any)
 
    expect(result.text).toBe('Mocked AI response')
    expect(result.usageMetadata.totalTokens).toBe(15)
  })
 
  it('throws 400 for missing message', async () => {
    const { readBody } = await import('#imports')
    vi.mocked(readBody).mockResolvedValue({})
 
    const handler = (await import('~/server/api/chat.post')).default
    await expect(handler({} as any)).rejects.toThrow('message field is required')
  })
})

Integration Testing with Playwright

For end-to-end testing of the streaming UI, Playwright handles SSE connections gracefully:

// tests/e2e/streaming.spec.ts
import { test, expect } from '@playwright/test'
 
test('streaming chat renders tokens progressively', async ({ page }) => {
  await page.goto('/')
 
  const textarea = page.locator('textarea')
  const sendButton = page.locator('button:has-text("Send")')
  const responseArea = page.locator('.response-area')
 
  await textarea.fill('Tell me a short joke')
  await sendButton.click()
 
  // Verify streaming indicator appears
  await expect(page.locator('.cursor')).toBeVisible()
 
  // Wait for streaming to complete
  await expect(page.locator('.cursor')).toBeHidden({ timeout: 30_000 })
 
  // Verify response content is non-empty
  const text = await responseArea.textContent()
  expect(text?.length).toBeGreaterThan(10)
})

Mocking the API in Development

Use Nuxt's server/plugins/ to intercept API calls during development or testing, so you can work on the UI without consuming real API quota:

// server/plugins/mock-gemini.ts
// Only active when MOCK_AI=true in .env
export default defineNitroPlugin((nitroApp) => {
  if (process.env.MOCK_AI !== 'true') return
 
  nitroApp.hooks.hook('request', (event) => {
    if (event.path === '/api/chat') {
      event.node.res.writeHead(200, { 'Content-Type': 'application/json' })
      event.node.res.end(JSON.stringify({
        text: '[MOCK] This is a simulated AI response for development.',
        usageMetadata: { totalTokens: 0 }
      }))
    }
  })
})

Thank You for Reading

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