The Seed-Stage CTO's Tech Stack: Boring Technology That Scales
Cutting-edge technology kills early-stage startups. Here's the boring stack that lets you ship fast, hire easily, and scale when you're ready—with specific recommendations.
February 24, 2025 11 min read
The Innovation Token Budget
Dan McKinley coined the term "innovation tokens" a decade ago at Stripe. The concept is simple: every time you introduce unfamiliar technology, you spend an innovation token. You only get three.
Most seed-stage startups spend all their tokens in the first week.
They choose a trendy database because it's what the cool kids use. They pick a cutting-edge framework because it promises performance they don't need yet. They implement a microservices architecture because that's what Netflix does.
By the time they start building features, they've exhausted their innovation budget on infrastructure that adds cognitive overhead without adding customer value.
McKinley learned this the hard way. He admits to introducing Scala and MongoDB at Etsy before eventually helping remove them. The fancy technology created more problems than it solved.
The lesson: save your innovation tokens for the parts of your product that actually differentiate you. Everything else should be boring.
Why Boring Beats Cutting-Edge
"Boring" doesn't mean bad. It means well-understood.
Failure modes are documented. When Postgres throws an error, Stack Overflow has the answer. When your boutique database fails, you're reading source code at 2 AM.
Talent is available. Every developer knows JavaScript and SQL. Hiring for a rare language or framework limits your candidate pool and increases salaries.
Stop planning and start building. We turn your idea into a production-ready product in 6-8 weeks.
Solutions exist. Common problems in boring stacks have common solutions. Novel problems in cutting-edge stacks require novel solutions that you have to invent.
Upgrades are tested. Mature technology gets security patches and performance improvements. You benefit from someone else's engineering investment.
The argument for cutting-edge technology typically centers on performance or developer experience. But at seed stage, performance is irrelevant—you don't have enough users to stress any reasonable architecture. And developer experience in an unfamiliar stack is worse, not better, because the team is constantly learning instead of shipping.
Next.js has become the default for good reason. It handles routing, server rendering, and API routes out of the box. You can start with a simple single-page app and add complexity as needed.
TypeScript adds type safety without significant overhead. The initial learning curve pays off quickly in reduced bugs and easier refactoring.
This combination is boring in the best sense: well-documented, widely used, and continuously maintained. Most React developers can be productive immediately.
Why not: Vue, Svelte, or other frameworks. They're fine technology, but the React ecosystem is larger, hiring is easier, and component libraries are more mature.
Backend: Node.js or Next.js API Routes
For most seed-stage products, you don't need a separate backend service. Next.js API routes handle basic CRUD operations, authentication, and third-party integrations.
If you need more backend complexity, Node.js with Express or Fastify keeps you in the JavaScript ecosystem. One language across the stack means any developer can work on any part of the codebase.
Why not: Go, Rust, or Python. They're excellent languages with specific strengths. But at seed stage, the cognitive overhead of switching languages doesn't pay off. The performance advantages of Go and Rust are irrelevant when you have dozens of users, not millions.
Database: Postgres
Postgres is the most boring choice possible, and that's exactly why you should use it.
It's been around for 35+ years. It handles relational data brilliantly. It handles JSON documents when you need schema flexibility. It has full-text search built in. It scales vertically to remarkable sizes before you need to think about sharding.
Most importantly: every developer knows SQL. Every ORM works with Postgres. Every managed database service offers Postgres.
Why not: MongoDB, DynamoDB, or other NoSQL databases. They have legitimate use cases—but those use cases rarely apply at seed stage. Postgres handles 99% of early-stage data patterns with better tooling and more predictable behavior.
Hosting: Vercel, Railway, or Render
The managed hosting war has produced excellent options. Vercel deploys Next.js apps instantly. Railway and Render handle full-stack applications with databases included.
All three offer generous free tiers. All three scale from $0/month to thousands without architecture changes. All three have good documentation and responsive support.
Why not: AWS, GCP, or self-managed servers. The cognitive overhead of cloud platforms is substantial. At seed stage, you don't need the flexibility—and you definitely don't need the complexity. Move to cloud providers when your hosting bills justify the engineering investment.
Authentication: Clerk, Auth0, or Supabase Auth
Don't build authentication. It's a solved problem with excellent solutions.
Clerk is the current favorite for developer experience. Auth0 is the enterprise-grade option. Supabase Auth integrates tightly if you're using Supabase for your database.
All three handle social login, email verification, session management, and security best practices. The monthly cost is trivial compared to the engineering time you'd spend building and maintaining this yourself.
Why not: Rolling your own. You'll get it wrong. Password hashing, session management, CSRF protection, rate limiting, and secure cookie handling have subtle requirements that authentication services have already solved.
Supabase gives you Postgres with real-time subscriptions and authentication built in. If your mental model is "database with extras," Supabase fits well.
Convex takes a more opinionated approach with TypeScript-first development and automatic reactivity. If your app is highly interactive, Convex's approach can eliminate significant boilerplate.
Both reduce the infrastructure you need to manage. Both have clear upgrade paths when you outgrow them.
What About AI?
Every 2025 tech stack article mentions AI. Here's the boring approach:
Use APIs, not models. OpenAI, Anthropic, and Google offer API access to frontier models. Unless AI is your core product differentiation, don't run your own models.
Add AI features incrementally. AI-powered search, content generation, or analysis can enhance a product without requiring architectural changes. Treat AI as a feature, not a foundation.
Budget for costs. AI API calls cost money. At seed stage, the amounts are usually trivial. But build rate limiting and cost monitoring from the start.
The boring approach to AI: use it when it adds user value, access it through APIs, and don't make it the centerpiece of your architecture until you've validated that users care.
The Ruby on Rails Case
Rails deserves a special mention as the original "boring" web framework.
The Rails approach—convention over configuration, sensible defaults, rapid prototyping—remains compelling for certain products. If you're building a content-heavy application, an e-commerce site, or a traditional web app with server-rendered pages, Rails is a legitimate choice.
The tradeoff: the React/Next.js ecosystem has grown larger. More developers know JavaScript than Ruby. More component libraries exist for React than for Rails frontends.
For interactive, JavaScript-heavy applications, Next.js is probably the better choice. For server-rendered CRUD apps, Rails remains excellent.
It has legitimate advantages: JavaScript everywhere, easy full-stack hiring, and mature tooling. If your team knows MERN, it's a reasonable choice.
The concern is MongoDB. NoSQL databases solve specific problems—high write throughput, schema flexibility at scale, document-oriented data. But they also create problems: no referential integrity, limited query flexibility, and eventual consistency surprises.
Most seed-stage products have relational data. Users have posts. Posts have comments. Comments have authors. Postgres handles this naturally with joins and foreign keys. MongoDB requires application-level joins that grow complex quickly.
If your data is genuinely document-oriented—logs, events, content blobs—MongoDB fits well. If your data has relationships, prefer Postgres.
The Kubernetes Trap
Kubernetes is amazing technology. It's also wildly inappropriate for seed-stage startups.
The pitch sounds compelling: container orchestration, automatic scaling, infrastructure as code. But the reality is weeks of configuration for capabilities you don't need.
A seed-stage startup does not need automatic scaling. You don't have enough users. A single server handles thousands of concurrent users for most applications.
A seed-stage startup does not need container orchestration. You have one service. Maybe two. There's nothing to orchestrate.
A seed-stage startup does not need Kubernetes' complexity. The learning curve is steep. The failure modes are subtle. The debugging is challenging.
When you have a dedicated infrastructure team and traffic that actually requires horizontal scaling, Kubernetes makes sense. Before then, it's an innovation token spent on overhead.
Microservices solve problems that emerge at massive scale: independent deployment of services, organizational boundaries between teams, different scaling needs for different components.
At seed stage, you have:
One team, not twenty
One deployment, not one hundred
One scaling requirement (handle a few hundred users)
The monolith is boring. It's also correct.
A well-structured monolith—with clear boundaries between domains, sensible directory organization, and consistent patterns—can scale to millions of users. You can extract services later when you have the team size and traffic patterns that justify the complexity.
Starting with microservices means starting with distributed systems complexity: network failures, eventual consistency, service discovery, and deployment coordination. None of this adds user value. All of it slows you down.
Every unusual technology in your stack reduces your candidate pool. Choosing Elixir over Node.js might delight your current team, but it eliminates 90% of candidates when you need to hire.
Boring technology is also equitable technology. Experienced developers know it. New graduates know it. Career changers can learn it. Your hiring pool is maximized when your stack is mainstream.
The exception: if you're hiring for a specific skill set—machine learning, blockchain, graphics programming—your stack should match the domain. But for general product development, boring means hiring advantage.
Stage-Appropriate Evolution
Your stack should evolve with your stage. Here's what that looks like:
Pre-Seed: Maximum simplicity
Next.js monolith with API routes
Supabase or Postgres on Railway
Vercel hosting
Clerk for auth
Total infrastructure cost: Under $50/month. Total infrastructure complexity: Nearly zero.
Seed: Add only what you need
Same stack, with additions as required:
Redis for caching or queues (if you actually need it)
Stripe for payments (don't build billing)
Resend or Postmark for email (don't run mail servers)
Still simple. Still boring. Still fast to iterate.
Series A: Start separating concerns
Now you have a team. Now you have traffic. Now you can afford some complexity.
Dedicated backend service if API routes become a bottleneck
Database read replicas if query load increases
CDN for static assets
Basic CI/CD pipeline
Still mostly boring. Still avoiding premature optimization.
Series B+: Invest in infrastructure
You have customers. You have revenue. You have engineers to spare.
Consider microservices where team boundaries justify them
Evaluate Kubernetes if you have dedicated DevOps
Build internal tooling that makes developers faster
Invest in observability and monitoring
Now the cutting-edge stuff might make sense. You've earned the right to spend innovation tokens on infrastructure.
When to Break the Rules
The boring stack has exceptions. Here's when cutting-edge makes sense:
Your differentiation is technical. If you're building a real-time collaborative editor, the boring WebSocket library might not cut it. If you're processing video at scale, managed cloud functions won't work. When your core product requires performance or capabilities that boring tech can't provide, spend the innovation token.
Your team has deep expertise. If your founding team has years of Rust experience and can ship faster in Rust than in JavaScript, that changes the calculation. Known technology trumps objectively simpler technology.
The boring option is actively bad. Sometimes the mainstream solution has a fatal flaw for your use case. If you've validated that the obvious choice doesn't work—not that it might not work, but that it actually doesn't—then reach for alternatives.
These exceptions are rare. Most startups that think they're exceptional are actually just optimizing prematurely.
The Speed Argument
Every discussion of tech stack is ultimately a discussion of speed.
Boring technology lets you ship faster because you spend less time learning and debugging infrastructure. You spend more time building features that users pay for.
The AI-accelerated development tools of 2025 make this even more pronounced. Cursor, Copilot, and similar tools work best with common patterns in popular languages. Ask for help with React and get excellent suggestions. Ask for help with a niche framework and get generic answers.
Boring technology is also AI-augmented technology. The training data favors it. The tooling supports it. The documentation exists.
In 2025, betting on boring means betting on the stack that both humans and AI understand best.
The CTO's Actual Job
At seed stage, the CTO's job isn't to build perfect infrastructure. It's to ship product fast enough to find product-market fit before money runs out.
Every hour spent configuring Kubernetes is an hour not spent talking to customers. Every day debugging a distributed system is a day not iterating on the product.
Your technical decisions should optimize for learning speed, not technical elegance. When validating your SaaS idea, fast iteration beats perfect architecture every time.
The time for technical sophistication comes later—after you've found something that works, after you've raised money to support infrastructure investment, after you have users who actually stress your systems.
Until then: choose boring technology. Ship fast. Learn from users. Save your innovation tokens for what actually matters.
A practical comparison of Cursor and Codeium (Windsurf) AI coding assistants for startup teams, with recommendations based on budget and IDE preferences.
