How to Build a SaaS Quickly in 2026: AI Agents, Boilerplates, and Vibe Coding

Building a SaaS in 2026 is fundamentally different from even two years ago. AI coding agents have shifted the bottleneck from "can I write this code?" to "can I describe what I want clearly enough?" Combined with a solid SaaS boilerplate, you can go from idea to production in days, not months.

The fastest path to building a SaaS in 2026: Use a SaaS starter kit like Makerkit to handle authentication, billing, and multi-tenancy. Then use AI coding agents (Claude Code, Cursor, or Codex) to build your custom features. You provide direction and review; the AI writes the code. A functional MVP is achievable in one to two weeks of focused work.

What is Vibe Coding?

Vibe coding is a development style where you describe what you want in natural language and let AI agents write most of the implementation. You guide the direction, verify each step works, and make corrections when needed. It's not "no-code" because you still need to understand the output and catch mistakes. Think of it as pair programming where your partner types really fast but occasionally needs course corrections.

Why 2026 Is Different: The Rise of AI Coding Agents

Two years ago, AI coding meant autocomplete suggestions. Today's AI agents operate more like junior developers who never sleep. Claude Code, Cursor, and Codex can:

• Read and understand your entire codebase
• Make multi-file changes across your project
• Run tests, analyze failures, and fix bugs in a loop
• Push commits and open pull requests

Recent benchmarks show Claude and GPT-5.2-Codex consistently achieving 70-80% accuracy on real-world coding tasks. That translates to code that works on the first pass more often than not.

But AI agents still need guardrails. They excel at implementing features you've clearly defined. They struggle with ambiguous requirements and can confidently produce code with subtle security flaws. The solution: pair them with a battle-tested foundation that handles the security-critical parts.

1. Start with a SaaS Boilerplate

A SaaS boilerplate handles the undifferentiated heavy lifting that every SaaS needs:

• Authentication: Sign up, sign in, password reset, social auth, email verification
• Billing: Stripe/Paddle/Lemon Squeezy integration, subscription management, usage-based pricing
• Multi-tenancy: Team workspaces, permissions, role-based access control
• Infrastructure: Database setup, API routes, deployment configuration

Without a boilerplate, you're spending your first two months on features that don't differentiate your product. With one, you're building your core value proposition on day one.

Why This Matters More with AI Agents

AI agents can scaffold CRUD features quickly, but they make mistakes on security-sensitive code. Authentication, authorization, and billing are exactly where those mistakes hurt. A boilerplate like Makerkit provides tested, secure implementations for these critical paths.

After building 5+ production apps with AI agents over the past year, I've seen them:

• Generate RLS policies that accidentally expose data to other tenants
• Forget to validate webhook signatures on payment events
• Create permission checks that authenticated users can bypass with modified requests

When these patterns are baked into your boilerplate, the AI works within safe constraints. It can focus on your business logic while the foundation handles security.

2. Validate Before You Build

Even with AI acceleration, building the wrong thing wastes time. Validation is faster than ever:

1. Build a landing page using your boilerplate's marketing components (Makerkit includes these out of the box)
2. Set up email capture with ConvertKit or similar
3. Drive traffic via Twitter, Reddit (subreddit targeting), or small ad spend
4. Talk to signups before writing custom code

This entire process can happen in a weekend. With Makerkit's landing page components and a few hours of content work, you can have a professional-looking validation page live.

The goal: confirm people will pay before you invest the effort, even AI-assisted effort, of building.

3. Choose Your AI Coding Stack

The leading AI coding agents each have different strengths. Here's what works in January 2026.

Claude Code

Best for: CLI-native developers, complex refactors, large codebases

Claude Code runs in your terminal and operates like a staff engineer who understands your entire repo. It handles large codebases successfully and remembers your preferences across sessions, including style guidelines and common commands.

At $20/month with Claude Pro, it's accessible for indie developers. We've optimized Makerkit's rules specifically for Claude Code, making it aware of our patterns and conventions.

Use Claude Code when: You prefer terminal over IDE, you're doing large refactors across 10+ files, or you need context memory across sessions.

See our step-by-step Claude Code tutorial for building a complete SaaS feature.

Cursor

Best for: VS Code users, visual feedback, parallel agent execution

Cursor replaces VS Code with an AI-native editor. The Agent/Composer mode plans and applies multi-file changes, and it can run multiple agents in parallel. If you prefer staying in an IDE rather than the terminal, Cursor is the smoother experience.

We maintain Cursor and Claude Code rules for Makerkit that help AI agents understand our project structure.

Use Cursor when: You want visual diff previews, you're comfortable in VS Code, or you need to run parallel agent tasks.

See our Cursor development guide for best practices.

Codex (OpenAI)

Best for: ChatGPT users, long-horizon tasks, web search integration

OpenAI's Codex has evolved significantly since its 2025 launch. The CLI runs locally from your terminal and can work independently for hours on complex refactors and migrations. GPT-5.2-Codex powers both the CLI and IDE extensions for VS Code, Cursor, and Windsurf.

Key features that set Codex apart: web search integration for up-to-date information, cloud task execution with local diff application, and the open agent skills specification for extensibility.

Use Codex when: You're already in the ChatGPT ecosystem, need web search during coding, or want to offload long-running tasks to the cloud.

Avoid Codex when: You need offline capability, you're working with proprietary code you don't want leaving your machine, or you prefer fine-grained control over context.

Install via npm i -g @openai/codex or brew install --cask codex. Included with ChatGPT Plus at $20/month.

Windsurf (Now Part of Cognition)

Windsurf underwent a dramatic ownership change in July 2025. After a failed $3 billion OpenAI acquisition (blocked by Microsoft's GitHub Copilot conflict), Google hired away the founders in a $2.4 billion deal. Cognition, the company behind Devin, then acquired Windsurf's remaining 210-person team, IP, and $82M ARR business within 72 hours.

If you're already using Windsurf, it still works. Cognition has continued development and added GPT-5.2 support. But developer adoption has dropped significantly since the acquisition chaos, and the product roadmap is now tied to Cognition's Devin strategy rather than standalone editor development.

Our recommendation: For new projects, start with Claude Code, Cursor, or Codex instead. These have clearer product direction and larger active communities. If you have existing Windsurf workflows, they'll continue working, but consider migrating during your next major project.

See our Windsurf tutorial for the pre-acquisition setup (still functional).

Quick Decision Guide

All of these work well with Makerkit. Pick based on your existing workflow preferences.

4. Build Your MVP with AI Agents

Here's the workflow that works:

Step 1: Define Your Data Model

Start by telling your AI agent the business context. Add a clear description to your project's CLAUDE.md, AGENTS.md, or .cursorrules:

## Application Scope
We are building a SaaS that allows teams to [core function].
Users can [primary actions].
Billing is [per-seat/usage-based/flat-rate].

Then ask the agent to design your database schema. Be specific about constraints:

Design a database schema for [feature]. Requirements:
- Only include new tables needed for app functionality
- Team accounts own tables, not individual users
- Use the existing has_role_on_account function for permissions
- Include RLS policies for all tables
- Add appropriate indexes for common queries

After the agent generates the schema, verify it works:

pnpm supabase db push
pnpm supabase test db

Review the generated schema carefully. AI agents are good at structure but can miss security edge cases. Run the schema through a security review (more on this below).

Step 2: Build Features Incrementally

Don't ask for everything at once. Break features into small, testable chunks:

1. "Create the page layout for [feature]"
2. "Add the data fetching for [feature]"
3. "Build the form to create [resource]"
4. "Add the server action to handle submission"

Each step should produce working code you can verify before moving on. This "discover, action, improve" loop prevents the AI from going down wrong paths for too long.

Step 3: Use AI Agent Reviewers

Here's a 2026 addition: use AI agents to review AI-generated code. After building a feature, ask:

• "Review this code for security vulnerabilities, especially in the RLS policies"
• "Check this server action for proper input validation"
• "Are there any edge cases I'm missing in this implementation?"

Claude Code, Cursor, and Codex can all act as reviewers. The key insight: an AI reviewing code uses different reasoning than the AI that wrote it, catching mistakes the original pass missed. Codex is particularly strong at finding bugs in existing codebases.

For critical paths (auth, billing, data access), consider a second review pass or human verification.

5. Launch and Iterate

With a boilerplate foundation and AI-assisted development, you can reach a launchable MVP in one to two weeks of focused work.

Launch channels that work in 2026:

• Product Hunt: Still relevant, especially for dev tools and B2B
• Hacker News: Show HN posts for genuine projects
• Reddit: Targeted subreddits for your niche
• Twitter/X: Build in public updates
• Email your waitlist: Those validation signups are now your first users

After launch, the same AI workflow accelerates iteration. User feedback becomes prompts for your coding agent: "Users are asking for [feature], add it to the [page] with [constraints]."

6. Common Pitfalls When Building SaaS with AI Agents

1. Over-relying on AI for Security

AI agents write plausible-looking security code that can have subtle flaws. Always verify:

• RLS policies actually restrict access correctly (test with different user contexts)
• Webhook signatures are validated before processing
• API routes check permissions before returning data
• Sensitive operations use proper authorization

2. Skipping the Boilerplate

"I'll just have Claude Code build auth from scratch" sounds fast. It isn't. You'll spend more time debugging edge cases (password reset flows, email verification, session handling) than you would configuring a tested solution.

3. Not Providing Context

AI agents perform dramatically better with context. Give them:

• Existing code files as examples of your patterns
• Your project's conventions (naming, file structure)
• The business logic behind what you're building

Makerkit's rule files provide this context automatically for common tasks.

4. Vibe Coding Without Verification

Vibe coding works, but only if you verify each step actually functions. Don't accept code you haven't run. After every AI-generated change:

• Run the app and test the feature manually
• Check the browser console for errors
• Verify database operations completed correctly

Next Steps

Ready to build your SaaS quickly?

1. Get a SaaS Starter Kit license
2. Choose your AI coding agent (Claude Code, Cursor, or Codex)
3. Set up your project with the Makerkit docs based on your preferred stack
4. Follow our agent-specific tutorials: Claude Code, Cursor

The combination of a battle-tested boilerplate and AI coding agents is the fastest path from idea to production in 2026.