Top AI SaaS Boilerplates With Built-In AI 2026

Shipping a SaaS in 2026 without AI features is a strategic decision, not a default. Users expect AI-powered functionality. Investors ask about AI differentiation. The infrastructure question — which models, how to stream, how to meter usage, how to handle costs — takes weeks to architect correctly from scratch.

The SaaS boilerplates in this guide solve that infrastructure problem. They ship with LLM integration pre-wired: streaming UI, multi-model support, token tracking, credit systems, and the conversation history patterns that production AI features require.

Quick Comparison

Starter	Price	Models	RAG	Streaming	Token Billing	Auth	Payments
Shipfast AI	$249	OpenAI + Anthropic	✅	✅	✅ Credits	NextAuth	Stripe
Vercel AI Chatbot	Free	Multi	❌	✅	❌	NextAuth	❌
Open SaaS	Free	OpenAI	❌	✅	❌	Wasp Auth	Stripe/Polar
MakerKit AI	$299	Multi-provider	✅	✅	✅	Supabase	Stripe
SaaS AI Starter	$199	OpenAI	✅	✅	✅ Credits	NextAuth	Stripe
v1.run (Midday)	Free	OpenAI	✅	✅	❌	Better Auth	Polar

What "Built-In AI" Actually Means

Not all AI integrations are equal. A boilerplate that just pre-installs the OpenAI SDK isn't "AI-ready" — you still build the entire feature. Production AI SaaS requires:

1. Streaming responses — Users expect character-by-character output, not a 10-second wait then a wall of text.

// The right pattern: streaming via Vercel AI SDK
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

export async function POST(req: Request) {
  const { messages, userId } = await req.json();

  // Check user has credits before calling the API
  await requireCredits(userId, estimateTokens(messages));

  const result = streamText({
    model: openai('gpt-4o'),
    messages,
    onFinish: async ({ usage }) => {
      // Deduct actual tokens used after completion
      await deductCredits(userId, usage.totalTokens);
    },
  });

  return result.toDataStreamResponse();
}

2. Conversation history — Threads that persist between sessions, accessible across devices.

3. Token/credit metering — Track API costs per user, enforce limits, convert API spend to user-facing credits.

4. Multi-model support — Switch between GPT-4o, Claude, Gemini without refactoring.

5. RAG pipeline (for document-aware AI) — Chunking, embedding, vector search, context injection.

1. Shipfast AI — Best Complete Commercial Kit

Price: $249 one-time | Stack: Next.js 15 + OpenAI + Anthropic + Stripe

Shipfast is the most popular paid SaaS boilerplate with AI features. The base Shipfast kit is $149; the AI add-on brings it to $249 and adds:

Multi-model support (OpenAI, Anthropic, toggle in config)
Streaming chat component with conversation history
Credit system: users buy credits, each AI interaction deducts credits
Admin dashboard showing per-user AI usage and costs
RAG pipeline setup with pgvector (PostgreSQL)
Rate limiting to prevent API cost abuse

Credit system architecture:

// Shipfast's credit model
// lib/credits.ts
export async function checkAndDeductCredits(
  userId: string,
  estimatedTokens: number
) {
  const creditsRequired = Math.ceil(estimatedTokens / 100); // 1 credit = 100 tokens

  const user = await prisma.user.findUnique({
    where: { id: userId },
    select: { credits: true }
  });

  if (!user || user.credits < creditsRequired) {
    throw new InsufficientCreditsError(creditsRequired, user?.credits ?? 0);
  }

  // Reserve credits optimistically
  await prisma.user.update({
    where: { id: userId },
    data: { credits: { decrement: creditsRequired } }
  });

  return creditsRequired;
}

Best for: Indie hackers who want to ship an AI product quickly and don't want to figure out the credit/billing/streaming architecture themselves.

2. Vercel AI Chatbot — Best Free Chat Reference

Price: Free (Apache 2.0) | Creator: Vercel | Stack: Next.js + Vercel AI SDK + Neon

The official reference implementation from the team that built the Vercel AI SDK. It's not a full SaaS boilerplate (no billing, no multi-tenancy), but the AI implementation is the cleanest available:

Multi-provider via Vercel AI SDK: OpenAI, Anthropic, Google, Mistral, xAI
Persistent conversation history with Neon Postgres (or Vercel KV)
Artifact system: code execution, document editing, image generation in chat
NextAuth for authentication
Tool calling (web search, weather) pre-implemented

// Vercel AI Chatbot's multi-provider pattern
import { openai } from '@ai-sdk/openai';
import { anthropic } from '@ai-sdk/anthropic';
import { google } from '@ai-sdk/google';

const MODELS = {
  'gpt-4o': openai('gpt-4o'),
  'claude-sonnet-4-5': anthropic('claude-sonnet-4-5'),
  'gemini-2.0-flash': google('gemini-2.0-flash'),
} as const;

export function getModel(modelId: keyof typeof MODELS) {
  return MODELS[modelId];
}

Start with Vercel AI Chatbot to understand the patterns, then add billing and multi-tenancy from another starter or build them yourself.

3. Open SaaS — Best Free Full-Stack AI SaaS

Price: Free (MIT) | Creator: Wasp | Stack: Wasp + React + Node.js + Prisma + OpenAI

Open SaaS is a 100% free, full-featured SaaS boilerplate powered by the Wasp framework. It includes working Stripe + Polar.sh billing, email auth, background jobs, and OpenAI integration out of the box — no paid tier required.

The AI integration is simpler than Shipfast (no RAG, no credit system) but covers the common case: an OpenAI-powered feature behind a subscription paywall.

// Open SaaS: OpenAI call wrapped in Wasp action
// src/server/actions.ts
import OpenAI from 'openai';

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

export const generateResponse = async ({ prompt }: { prompt: string }, context) => {
  // Wasp auto-injects auth context
  if (!context.user) throw new HttpError(401);

  // Check subscription (Stripe webhook updates user.subscriptionStatus)
  if (context.user.subscriptionStatus !== 'active') {
    throw new HttpError(402, 'Subscription required');
  }

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

  return response.choices[0].message.content;
};

Best for: Developers who want a free, complete foundation and are comfortable extending the AI features.

4. MakerKit AI — Best Enterprise AI Starter

Price: $299 | Stack: Next.js/React Router 7 + Supabase + Stripe + pgvector

MakerKit's AI extension adds a complete RAG pipeline to an already comprehensive SaaS foundation:

pgvector integration for embeddings storage (Supabase's built-in vector extension)
Document ingestion pipeline: upload PDF/text, chunk, embed, store
Retrieval-augmented chat: chat with your documents
Per-workspace AI usage tracking
Multi-model via Vercel AI SDK

RAG pipeline overview:

// MakerKit's document ingestion pattern
// lib/ai/ingest.ts
import { embed } from 'ai';
import { openai } from '@ai-sdk/openai';
import { chunkText } from './chunking';

export async function ingestDocument(
  content: string,
  documentId: string,
  organizationId: string
) {
  const chunks = chunkText(content, {
    chunkSize: 1000,
    overlap: 200,
  });

  const embeddings = await Promise.all(
    chunks.map(async (chunk, i) => {
      const { embedding } = await embed({
        model: openai.embedding('text-embedding-3-small'),
        value: chunk,
      });

      return {
        documentId,
        organizationId,
        chunkIndex: i,
        content: chunk,
        embedding, // pgvector stores this as vector(1536)
      };
    })
  );

  await supabase.from('document_chunks').insert(embeddings);
}

5. v1.run (Midday Port) — Best Free Modern Stack

Price: Free (MIT) | Stack: Next.js + Convex + Better Auth + Polar.sh + OpenAI

v1.run is a port of the Midday finance app codebase to Convex, providing an opinionated monorepo starter with AI features built in. The AI implementation uses Vercel AI SDK with OpenAI, with Convex handling conversation history persistence.

Notable for using Polar.sh instead of Stripe — lower fees (4% + $0.40) and developer-friendly checkout. Better Auth for self-hosted auth with no per-MAU billing.

Token Economics: Building AI Credit Systems

Every commercial AI SaaS needs a token accounting system. Here's the architecture:

// Token credit system (applicable to any boilerplate)
// lib/credits/index.ts

const CREDIT_RATES = {
  'gpt-4o': { input: 0.01, output: 0.03 }, // Credits per 1K tokens
  'claude-sonnet-4-5': { input: 0.015, output: 0.075 },
  'gpt-4o-mini': { input: 0.0003, output: 0.0006 },
} as const;

export function calculateCreditCost(
  model: keyof typeof CREDIT_RATES,
  inputTokens: number,
  outputTokens: number
): number {
  const rates = CREDIT_RATES[model];
  return (
    (inputTokens / 1000) * rates.input +
    (outputTokens / 1000) * rates.output
  );
}

// Price your credits with margin:
// If API costs 0.05 credits, charge user 0.10 credits (2x margin)
// Users buy credits: $10 = 1000 credits
// Each chat: 50 tokens in, 200 tokens out = ~0.10 credits
// = $0.001 per chat to you, ~0.001 cost to API = healthy margin

Choosing Your AI Boilerplate

Ship fastest (paid): Shipfast AI — most complete credit system and AI UI out of the box.

Ship fastest (free): Open SaaS — full billing + auth + OpenAI, zero licensing cost.

Best AI architecture to learn from: Vercel AI Chatbot — cleanest Vercel AI SDK implementation.

Need RAG: MakerKit or SaaS AI Starter — both include document ingestion pipelines.

Modern free stack: v1.run — Convex + Better Auth + Polar.sh, no vendor lock-in.

How to Evaluate AI SaaS Boilerplates

When comparing AI boilerplates, evaluate these specific dimensions before committing to a purchase or clone:

Streaming correctness under errors. Run the demo and deliberately trigger an error — disconnect from the internet mid-stream, or send a request that hits a rate limit. Does the UI recover gracefully? Does the loading state clear? Streaming that works in the happy path but leaves the UI in a broken state on failure is common in boilerplates that demo well but fail in production.

Credit accounting accuracy. For boilerplates with credit systems, check where credit deduction happens. The correct implementation deducts credits in the onFinish callback of streamText — after the response completes, using the actual token count from the API response. Boilerplates that deduct credits before or during the stream (based on estimated tokens) will either overcharge users or lose money depending on whether estimates are high or low.

Multi-model support architecture. The LLM market changes quarterly. A boilerplate that hard-codes OpenAI throughout its streaming implementation ties you to one provider. The Vercel AI SDK's provider abstraction (anthropic('claude-3-5-sonnet'), openai('gpt-4o'), same interface) is the correct pattern — changing models means changing one string, not refactoring a streaming endpoint.

Token budget enforcement order. Budget check must happen before the API call, not after. A boilerplate that calls the LLM API and then checks if the user had credits has already incurred cost when the check fails. The request lifecycle should be: authenticate → check credits → call LLM → deduct credits on finish.

What These AI Boilerplates Have in Common

Despite different tech stacks (Next.js vs Wasp, Supabase vs Vercel KV, Stripe vs Polar), the AI boilerplates in this guide converge on the same architectural patterns:

The Vercel AI SDK is the universal LLM abstraction layer in 2026. Whether using paid (Shipfast) or free (Vercel AI Chatbot), all production AI boilerplates route their LLM calls through the ai package's streamText and generateText functions. This is the right choice — the SDK handles streaming protocol normalization, tool calling, structured output, and provider-specific quirks so you don't have to.

Conversation persistence uses the same schema regardless of boilerplate: a conversations table with user ownership, a messages table with role and content, and indexes on user ID and created timestamp. The schema is simple enough that it's not the differentiation between boilerplates — the differentiation is the streaming UI, the billing integration, and the credit management logic.

Token economics at early scale (under 100,000 active users) strongly favor AI SaaS products. The cost to serve a GPT-4o request is 10-100x cheaper than the value users perceive. The challenge is retention and value delivery, not unit economics.

For the complete picture of AI SaaS boilerplate options including purpose-built AI starters, see best AI SaaS boilerplates for shipping fast and the best AI/LLM boilerplates comparison. For the open-source options that combine full SaaS infrastructure with AI integration at no license cost, see the free open-source SaaS boilerplates guide. For chatbot-specific patterns including conversation history and RAG, see the best boilerplates for AI chatbot products guide.

Every SaaS boilerplate makes architectural decisions that become increasingly expensive to reverse as your codebase grows. The best time to evaluate whether a boilerplate's decisions match your product requirements is before you've written any custom business logic on top of it.

Browse all AI SaaS boilerplates at StarterPick.

The SaaS Boilerplate Matrix (Free PDF)