Building an AI SaaS MVP in 2026: My Stack and Timeline

Last quarter I helped a founder kill a six-month build that was still nowhere near paying customers. The stack was fine. The problem was they had architected for a Series B while trying to validate whether anyone wanted the product at all. I've been shipping AI SaaS MVPs under BizFlowAI on a four-week clock, and the shortcuts I take now are deliberate. Here is the stack, the timeline, and the tradeoffs I actually make.

The stack I default to in 2026

For an AI SaaS MVP the goal is one thing: get a real user paying or churning within 30 days. Every stack choice below is optimized for that, not for scale to 10 million users.

Here is what I reach for by default:

The reason this stack works is not that each piece is best-in-class. It is that they compose without me writing glue code. Supabase RLS talks to Next.js middleware. Stripe webhooks land in a route handler and update Postgres in one query. Lambda picks up long jobs so my Vercel functions never time out.

The single most important choice: put the database and the vector store in the same Postgres instance. I have watched teams stand up Pinecone, then Weaviate, then rip both out. pgvector with an HNSW index handles millions of rows. Until you can prove you need a dedicated vector DB, you do not.

The 4-week timeline that actually ships

I plan every MVP in four weeks of five working days. Not because it is a magic number, but because a founder can hold context and momentum for that long. Past week five, everyone loses the thread.

Week 1: the boring foundation

Days 1 to 5 are the parts that never make the demo but decide whether the thing survives contact with users.

• Day 1: Repo, Next.js app, Vercel deploy, Supabase project, custom domain. Ship a page that says the product name.
• Day 2: Auth with magic link and Google. Protected routes. A dashboard shell with an empty state that describes what the user will see once they use the product.
• Day 3: Database schema, RLS policies, seed data. Stripe Checkout wired to a test product. Webhook handler that writes subscription status to Postgres.
• Day 4: The one core object of the product. In a RAG SaaS this is a "workspace" with "documents". In an agent SaaS it is a "run" with "steps". Model it in Postgres, build the CRUD.
• Day 5: A single working end-to-end flow with a hardcoded LLM call. User logs in, creates an object, gets a mediocre AI response, sees it saved.

If week 1 does not end with a signed-in user seeing an AI response tied to their account, the project is already behind. I have never had a week 1 miss lead to an on-time week 4.

Week 2: the AI core

This is where the product becomes itself. Two hard rules:

1. Pick one LLM task that matters and make it excellent before adding others.
2. Every prompt lives in a versioned file with a matching eval set, even if the eval set is just five hand-labeled examples.

If the product uses RAG, week 2 is when I build the ingestion pipeline. My default in 2026:

// ingest.ts (simplified)
const chunks = await semanticChunk(document, {
  targetTokens: 500,
  overlap: 60,
});

const embeddings = await openai.embeddings.create({
  model: "text-embedding-3-small",
  input: chunks.map(c => c.text),
});

await supabase.from("chunks").insert(
  chunks.map((c, i) => ({
    workspace_id,
    document_id,
    content: c.text,
    embedding: embeddings.data[i].embedding,
    metadata: c.metadata,
  }))
);

Retrieval is hybrid from day one: BM25 via Postgres full text search, plus vector, fused with reciprocal rank fusion. I wrote about this in more depth in my RAG architecture post, but the short version is that pure vector search fails on acronyms, product names, and error codes, which is exactly what users search for.

Week 3: the surface that sells

Week 3 is UI, onboarding, and the specific moments where users decide to pay or leave. I spend more time on the empty state and the first result screen than on any other pages combined. If the first thing a user creates does not feel useful in 30 seconds, they are gone.

Two things that always go into week 3:

• Streaming responses with the Vercel AI SDK. Not because it is faster, but because a user waiting for a blank screen for eight seconds churns. A user watching tokens stream in for eight seconds does not.
• A shareable artifact. Every AI product needs one output the user wants to send to a colleague. A public link, a PDF, an embed. This is the only free distribution channel that consistently works for AI SaaS.

Week 4: pay, measure, launch

Days 16 to 20 are usage metering, rate limits, and the last painful bugs.

• Meter every LLM call against a Stripe subscription tier. I use Stripe's usage-based billing with a nightly reconciliation job on Lambda.
• Add a hard cap per workspace so a single user cannot burn $2,000 of tokens overnight. I have seen this happen. Set it low, raise it on request.
• Wire Langfuse or Logfire so every trace has a user id, workspace id, and cost. If you cannot answer "what did this customer cost us last month" from one SQL query, you will fly blind on pricing.
• Cut a launch list of 20 to 50 people who have already said they want it. Send them a personal link, not a Product Hunt post.

The tradeoffs I make on purpose

There are five decisions where founders lose weeks. Here is where I land and why.

Serverless vs a real server

I default to serverless Lambda and Vercel functions for the MVP. The tradeoff is real: cold starts, 15-minute Lambda limits, and a mental model that some engineers hate. The payoff is I pay $30 a month until users show up, and I never wake up at 3 am for a server. When a specific workload outgrows Lambda, usually a long-running agent or a heavy embedding job, I move that one workload to a Fargate task or a small EC2 with a queue in front. I do not migrate the whole app.

One LLM provider vs many

I pick one provider as primary and one as fallback. In 2026 that is Anthropic first, OpenAI second for me, but the specifics matter less than the pattern. Behind a thin router:

async function complete(task: TaskKind, messages: Msg[]) {
  const model = ROUTER[task]; // "sonnet" | "haiku" | "gpt-4.1"
  try {
    return await primary(model, messages);
  } catch (e) {
    if (isRateLimit(e) || isProviderDown(e)) {
      return await fallback(FALLBACK_MAP[model], messages);
    }
    throw e;
  }
}

The router earns its keep the first time a provider has a bad afternoon. I do not build a full abstraction layer over five providers on day one. That is procrastination dressed as architecture.

RAG vs long context vs fine-tuning

For an MVP, RAG almost always wins. Long context is tempting when the corpus is small, but it burns tokens on every call and gets slower as the user grows their data. Fine-tuning is rarely the answer at MVP stage: you do not have the data yet, and the models keep getting better underneath you. Ship RAG. Measure retrieval quality. Only consider fine-tuning once you have real usage data and a clear failure mode that better retrieval cannot fix.

Multi-tenant from day one

Even for the smallest MVP I use workspace_id on every table and enforce it with Postgres RLS. Retrofitting multi-tenancy after you have paying customers is a three-week rewrite. Doing it upfront costs maybe two hours.

Build vs buy for the plumbing

I buy auth, payments, email, analytics, and error tracking. I build the product and the AI pipeline. Every founder I know who tried to save $30 a month on Clerk lost a week to auth bugs. The plumbing is not the moat.

The gotchas that cost me weeks

Real things that have burned me. Learn from them.

1. Vercel function timeouts on RAG queries. Vercel serverless functions default to 10 seconds on the hobby plan and 60 on pro. A cold Postgres query plus a slow LLM call blows through that. Move heavy AI calls to Lambda with a longer timeout, or use Vercel's fluid compute with streaming responses.
2. Postgres connection exhaustion. Serverless functions each open a connection. Use Supabase's connection pooler (transaction mode) or you will see mystery outages the day traffic doubles.
3. Token cost blowups from retries. A naive retry on a failed 8k token completion doubles your cost silently. Log cost per call, alert on anomalies, cap retries.
4. Prompt drift. Someone edits a prompt in the UI editor on Tuesday, quality drops on Wednesday, no one knows why. Keep prompts in the repo, version them, review them like code.
5. Embedding model changes. If you upgrade your embedding model, you must re-embed the entire corpus. Store the model name on every row so you know what needs migrating.

What I would do if I were starting Monday

If I were a solo founder shipping an AI SaaS starting this Monday, my week zero would be one day of user calls, not one day of code. Five 20-minute calls with people in the target segment. If I could not find five people who wanted the thing badly enough to talk about it, I would change the idea, not the stack.

Then I would open a Next.js repo, wire Supabase and Stripe on day one, and hold to the four-week plan above. I would spend $20 on Claude API credits, $25 on Vercel, $25 on Supabase, and I would not add a sixth tool for at least a month.

The stack does not make the product. The four weeks of focused decisions do. Almost every failed AI SaaS I have looked at spent months on the wrong three things: a custom auth system, a self-hosted vector DB, and an agent framework that ended up wrapping three lines of code.

If you are mid-build and want a second pair of eyes on the architecture or the timeline, get in touch at lazar-milicevic.com/#contact. Or if you are earlier and just want to read how I think about the pieces, the RAG in production and agent memory posts are the ones I would start with.