The SaaS Founder Stack: What We Actually Run in 2026

Next.js with TypeScript and Tailwind is the most hireable frontend stack that exists, and that property matters more than any technical argument anyone can make about alternatives.

The reasoning is about year two rather than week one. When you bring engineering in-house, or hand a codebase to somebody else, you want a stack the next developer already knows. Choosing something more interesting means choosing a smaller hiring pool and a longer onboarding period, and both of those costs arrive precisely when the company can least afford them.

This is the general principle behind most of the choices below. The question is rarely which tool is best. It is which tool leaves you with the fewest problems eighteen months from now, and those are different questions with different answers.

Cost is zero. You pay for hosting rather than for the framework.

Two categories where building your own is the most frequent early error we see, and both fail for the same reason. They appear simple and are not.

Authentication means Clerk or Auth.js. Sessions, password resets, social login, multi-factor, organisations, and the security implications of getting any of it slightly wrong. Clerk handles it with a component you drop in. Auth.js gives you more control and less magic. Building it yourself is a two-week estimate that becomes six weeks and produces something worse than the thing you would have installed in an afternoon.

Payments means Stripe, and not only checkout. Subscriptions, trials, proration when somebody upgrades mid-cycle, failed card recovery, invoicing, and tax. Stripe Billing covers all of it. The part teams skip is webhook reconciliation, which keeps your database and Stripe agreeing about what happened, and skipping it produces billing bugs that cost real money and are discovered by customers rather than by you.

Both have free tiers that last well past launch, which means the case for building your own is not even a cost argument. It is an instinct, and it is an expensive one.

Postgres for anything relational, which in practice is nearly everything. It is the most capable open database available and it handles jobs that regularly send teams looking for a second system. JSON documents, full-text search, and vector search through pgvector, which means an AI feature frequently does not require a separate vector database at all. That is one fewer service, one fewer bill, and one fewer thing to keep in sync.

Supabase if you want Postgres with an API layer, authentication, storage, and row-level security without standing up infrastructure. It is Postgres underneath, so there is no proprietary query language to learn and no migration to fear later, which is the property that makes it a safe default rather than a lock-in.

We use it on smaller builds and reach for raw Postgres on AWS when scale or compliance demands it. The free tier is generous. Note that inactive projects pause, which surprises people who leave a project alone for a fortnight and return to a slow first request.

This is the section founders skip and it is the one that separates a product from a demonstration.

Sentry for errors, because you want to know a user hit an exception before they email you, and you want the stack trace when they do.

PostHog for product analytics, meaning where people abandon onboarding, which feature nobody opens, and whether the change you shipped last week helped. It replaces three or four separate tools and has a genuinely usable free tier.

Grafana when you depend on external services. If your product calls model providers or third-party APIs, you want a dashboard showing degradation before a support ticket does. We learned this building Decipher Engine, which calls around twenty external providers, where the practical reality is that one of them is having a bad day most weeks.

The argument against doing this early is that there is nothing to measure yet, and the argument is wrong. Early users are where the sharpest signal exists, because there are few enough of them that individual behaviour is legible. Adding observability after something breaks means spending a day reconstructing what happened from logs that were not designed to answer the question.

Claude, GPT, and Gemini behind a single abstraction, so that switching is a configuration change rather than a rewrite. This matters more than it sounds, because model pricing and capability shift every few months and being locked to a decision made in week one is a genuine cost.

OpenRouter if you want many models behind one API. Replicate for image models. Langfuse or LangSmith for tracing and evaluations, because a generative feature without an evaluation suite is one you can change and hope about but cannot improve deliberately.

The lever most teams miss is routing. Send simple steps to a cheaper model and reserve the frontier model for actual reasoning. It routinely halves the bill, and it is the first thing we examine on any AI build that has become expensive.

We built our own authentication once, on an early product, because it looked straightforward. It is straightforward until password reset, then session invalidation, then somebody asks for Google login, then multi-factor. Two weeks became six and the result was worse than a component we could have installed in an afternoon. We do not do it any more, and the lesson generalises. The features that look simple in authentication are the ones you have thought about, and the expensive ones are the ones you have not.

We also skipped analytics until launch, reasoning that there was nothing to measure before there were users. The consequence was launching, acquiring users, and having no idea which part of onboarding was losing them. The instrumentation you want is the instrumentation that was already running when the interesting thing happened, which means it has to be in place before you know what the interesting thing will be.