Best Boilerplates for AI SaaS Products in 2026

AI SaaS Has Different Infrastructure Requirements

Standard SaaS boilerplates (ShipFast, Supastarter, T3) work for AI SaaS — but they're missing the AI-specific infrastructure that takes weeks to build:

• LLM API integration with streaming and error handling
• Token metering and per-user usage limits
• Vector database for RAG and semantic search
• Prompt management and versioning
• AI credit system tied to Stripe billing

In 2026, developers are building this infrastructure layer on top of standard SaaS boilerplates. This article shows the patterns.

AI SaaS Architecture

User input
  ↓
Rate limit check + credit deduction (Redis)
  ↓
Context retrieval (pgvector RAG) [optional]
  ↓
LLM API call (OpenAI / Anthropic / Google)
  ↓
Token usage metering → update user credits
  ↓
Stream response to client (Vercel AI SDK)
  ↓
Store conversation history (PostgreSQL)

Best Starting Combinations

ShipFast + Vercel AI SDK

The most common 2026 stack for AI SaaS:

• ShipFast handles: auth, Stripe credits/subscriptions, email, landing page
• Vercel AI SDK handles: multi-provider LLM calls, streaming, structured output

// app/api/chat/route.ts
import { streamText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';
import { checkAndDeductCredits } from '@/libs/credits';
import { getServerSession } from 'next-auth';

export async function POST(req: Request) {
  const session = await getServerSession();
  if (!session) return new Response('Unauthorized', { status: 401 });

  // Check credits before LLM call
  const hasCredits = await checkAndDeductCredits(session.user.id, 1);
  if (!hasCredits) return new Response('No credits', { status: 402 });

  const { messages } = await req.json();

  const result = streamText({
    model: anthropic('claude-3-5-sonnet-20241022'),
    messages,
    onFinish: async ({ usage }) => {
      // Update actual token usage after completion
      await recordTokenUsage(session.user.id, usage);
    },
  });

  return result.toDataStreamResponse();
}

T3 Stack + AI SDK + pgvector

For AI SaaS with RAG (search over user documents):

// packages/api/src/router/rag.ts
import { openai } from '@ai-sdk/openai';
import { embed, embedMany } from 'ai';
import { db } from '@acme/db';
import { documents, embeddings } from '@acme/db/schema';
import { cosineDistance, gt, desc } from 'drizzle-orm';
import { sql } from 'drizzle-orm';

// Store document embedding
export async function indexDocument(userId: string, content: string) {
  const { embedding } = await embed({
    model: openai.embedding('text-embedding-3-small'),
    value: content,
  });

  await db.insert(embeddings).values({
    userId,
    content,
    embedding,  // pgvector stores float[]
  });
}

// Retrieve relevant context
export async function findRelevantContent(userId: string, query: string) {
  const { embedding } = await embed({
    model: openai.embedding('text-embedding-3-small'),
    value: query,
  });

  const similarity = sql<number>`1 - (${cosineDistance(embeddings.embedding, embedding)})`;

  return db.select({ content: embeddings.content, similarity })
    .from(embeddings)
    .where(gt(similarity, 0.5))
    .orderBy(desc(similarity))
    .limit(5);
}

Token Billing Patterns

Credit System (Most Common)

// $0.01 = 1 credit. Bundle credits into Stripe products.
const PRICING = {
  starter: { credits: 1000, price: 10 },   // $10 = 1000 credits
  pro: { credits: 5000, price: 40 },        // $40 = 5000 credits
  scale: { credits: 25000, price: 150 },    // $150 = 25000 credits
};

// LLM costs in credits per 1K tokens
const LLM_CREDIT_COST = {
  'gpt-4o': { input: 0.5, output: 1.5 },         // per 1K tokens
  'claude-3-5-sonnet': { input: 0.3, output: 1.5 },
};

Subscription with Soft Limits

// Plans include monthly token budget, warn at 80%, block at 100%
const PLANS = {
  starter: { tokensPerMonth: 500_000, price: 29 },
  pro: { tokensPerMonth: 5_000_000, price: 99 },
};

AI SaaS Launch Checklist

Before launching an AI product:

• Rate limiting — Prevent users from consuming all credits in one burst
• Error handling — LLM APIs are flaky; implement retry with exponential backoff
• Streaming — Users expect character-by-character output, not wait-then-dump
• Cost controls — Set monthly spend limits on your LLM provider account
• Content moderation — Screen inputs for ToS violations (OpenAI Moderation API)
• Fallback models — If primary model fails, fallback to alternative
• Usage dashboard — Show users their credit balance and usage history

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Compare AI SaaS boilerplates and standard starters on StarterPick.