The AI SaaS MVP checklist

Define the one job the AI does

Pick a single, repetitive, painful task your user does today. Not three. One. “Generates a draft of a sales follow-up email from a CRM note” is a job. “AI assistant for sales teams” is a category.

The reason this matters more for AI products than for traditional SaaS: model quality is task-specific. Claude Sonnet 4 will be brilliant at one narrow workflow and mediocre at the adjacent one, and your evals, prompts, and guardrails all collapse if the surface area is fuzzy. Founders who keep scope tight to one job ship in 4–8 weeks. Founders who insist on a “platform” usually ship nothing.

Choose your model and your fallback

For most AI SaaS MVPs we recommend Claude Sonnet 4 as the default workhorse. It handles tool use, long context, and structured outputs reliably, and the per-token cost is sane for a paid product. Use Haiku for cheap classification or routing steps. Reserve Opus for the rare high-stakes reasoning call.

Pick a fallback model from day one. Not because Anthropic will go down-they rarely do-but because you’ll want to A/B prompts against a second provider eventually, and retrofitting that into a tightly-coupled codebase is painful. An abstraction layer (LiteLLM, your own thin wrapper, or the Vercel AI SDK) costs you one afternoon now and saves a week later.

Decide where the agent’s state lives

AI products are stateful in ways traditional CRUD apps aren’t. You have:

• Conversation history — needs to be retrievable, often searchable.
• Tool call results — must be cached so you don’t re-run expensive operations.
• User memory — preferences, prior context, learned facts.
• Embeddings — for RAG, semantic search, or similarity.

 

Postgres + pgvector handles all four for an MVP. Don’t reach for Pinecone, Weaviate, or a separate vector DB until you have a real reason. One database, one backup story, one set of migrations. Supabase or Neon gets you there in an hour.

Set hard cost ceilings before you ship

This is the line item that kills more AI MVPs than any other. A single user looping an agent on a 200K-token context can spend $40 in an afternoon. Multiply by a free trial cohort and you have a real problem.

Three controls, non-negotiable:

1. Per-request budget. Token cap on every model call. If a response would exceed it, you truncate or stop.
2. Per-user daily budget. Tracked in your DB, checked before each call. Free tier users get $0.50/day. Paid users get whatever your unit economics support.
3. Org-wide circuit breaker. If total spend in the last hour exceeds X, alerts fire and new requests queue or degrade to Haiku.

 

Anthropic’s usage API plus a small middleware layer gives you all three. Build it before your first external user, not after the bill arrives.

Write the prompts as code, not config

Prompts are the most important code in your repo. Treat them that way. Version them in git, review them in PRs, and write tests against them. Storing prompts in a database where a non-engineer can edit them feels nice until someone ships a regression at 11pm and you have no diff to inspect.

A clean structure for an MVP: a prompts/ directory with one file per agent or step, plus a thin loader that injects runtime variables. Anthropic’s prompt caching can then key off the static portion, which cuts costs 50–90% on repeated calls.

Build evals before features

You cannot ship an AI product without evals. You can ship a CRUD product without tests-badly, but you can. AI is different because outputs are non-deterministic and “it works on my machine” means nothing when the next user phrases their input slightly differently.

Start small. Twenty hand-curated examples covering:

1. The five most common happy paths.
2. Five edge cases you expect.
3. Five adversarial or messy inputs (typos, missing fields, off-topic).
4. Five regression cases from real user bugs as they appear.

 

Run them on every prompt change. Score with a mix of exact-match (where applicable), LLM-as-judge (Claude scoring Claude on a rubric), and human spot-checks. Tools like Braintrust, Langfuse, or a homegrown script all work-the discipline matters more than the platform.

Pick your auth and billing stack on day one

This is the most boring section of any AI SaaS MVP checklist, and the one founders most often punt on. Don’t.

 

Layer	Recommended for MVP	Why
Auth	Clerk or Supabase Auth	Social login, magic links, org/team support out of the box
Billing	Stripe + Stripe Customer Portal	Metered billing for token usage is a first-class feature
Usage metering	Stripe Meters or Orb	Lets you bill per AI call, per token, or hybrid seat + usage
Email	Resend or Postmark	Transactional and product email without SendGrid pain

 

Hybrid pricing-a small seat fee plus metered AI usage-is now the dominant model for AI SaaS, and Stripe Meters makes it trivial. Set up the meter on day one even if you charge a flat $29 at launch. You’ll thank yourself when you need to migrate pricing in month three.

Plan for tool use and retrieval, not just chat

Most useful AI products are agents, not chatbots. They call tools. They fetch data. They take actions. Your MVP architecture needs:

1. A registry of tool definitions (JSON schemas Claude can call).
2. A safe executor that runs tools with timeouts and error handling.
3. Logging of every tool call with inputs, outputs, and latency.
4. A retrieval layer if you’re grounding answers in user data.

 

For RAG specifically: chunk by semantic boundary (paragraphs, sections), not fixed token windows. Store the original document reference alongside each chunk so you can cite sources. Use hybrid search (BM25 + vector) from the start-pure vector search performs worse than founders expect on real product queries.

Instrument everything, then look at it daily

You need three dashboards before launch:

1. Product analytics — PostHog or Mixpanel. Activation, retention, feature usage.
2. LLM observability — Langfuse, Helicone, or LangSmith. Every prompt, response, latency, and cost per call.
3. Error tracking — Sentry. Front-end and back-end.

 

The middle one is the one founders skip and regret. When a user says “the AI gave me a weird answer yesterday,” you need to pull up that exact trace in under thirty seconds. Without LLM observability you’re guessing.

Decide your latency budget

A streaming response that starts in 800ms feels fast. A non-streaming response that takes 12 seconds feels broken, even if the content is better. Pick a target before you build:

• Conversational UX: first token under 1.5s, streaming throughout.
• Background agents: can take minutes; show progress.
• Batch jobs: overnight is fine; email when done.

 

Match your model and architecture to the budget. Sonnet streams well. Heavy retrieval steps don’t-do them in parallel with a “thinking…” placeholder. If you need sub-second responses for classification or routing, use Haiku in front of Sonnet.

Write the safety and abuse story before you have abusers

Even a niche B2B AI tool needs basic safeguards. The minimum:

• Rate limiting — per IP, per user, per org.
• Input validation — reject inputs over a token threshold, block known jailbreak patterns.
• Output filtering — for the specific risks of your domain (PII leakage, hallucinated citations, inappropriate content).
• An audit log — of every AI action taken on behalf of a user, especially for agents that send emails, modify records, or spend money.

 

For B2B buyers, a one-page security and data handling doc-what you log, what you send to Anthropic, what’s retained, what’s encrypted-closes deals. Anthropic’s zero-retention option for enterprise customers is worth mentioning if you’re targeting regulated industries.

Choose your launch surface and feedback loop

An MVP exists to learn, not to scale. Pick one launch channel and instrument it:

• Closed beta with 10–30 design partners. Best for B2B. Weekly 30-minute calls beat any analytics dashboard.
• Product Hunt + waitlist. Best for prosumer tools. Expect a spike, then silence-plan for it.
• Niche community launch. A specific subreddit, Slack, or Discord where your exact user lives. Often the highest signal per visitor.

 

Whichever you pick, build an in-product feedback widget that captures the conversation context with every report. “This response was wrong” with the full trace attached is worth a hundred generic NPS surveys.

The 1-page checklist, condensed

 

#	Decision	Default for an AI MVP
1	The one job	One input, one output, one user
2	Model + fallback	Claude Sonnet 4, abstracted
3	State storage	Postgres + pgvector
4	Cost ceilings	Per-request, per-user, per-org
5	Prompts as code	Git-versioned, cached
6	Evals	20 examples, run on every change
7	Auth + billing	Clerk + Stripe Meters
8	Tools + retrieval	Tool registry, hybrid search
9	Observability	PostHog + Langfuse + Sentry
10	Latency budget	Streaming, <1.5s first token
11	Safety	Rate limit, audit log, security doc
12	Launch surface	One channel, instrumented feedback

If you can answer each row in a single sentence specific to your product, you’re ready to build. If three or more are vague, that’s the next week of work-not more wireframes.

What this checklist deliberately leaves out

No mobile app. No multi-tenancy beyond Stripe orgs. No fine-tuning. No custom model hosting. No SOC 2. These are real concerns at different stages, but every one of them adds weeks and forces architectural decisions you don’t have enough information to make yet. An MVP exists to find out whether the core loop works. Everything else is a Phase 2 problem, and Phase 2 is a privilege you earn by shipping Phase 1.

The founders we see ship fastest treat this list as a forcing function. They sit with it for two days, fight about scope, then start building. Six weeks later they have paying users and real data about what to build next. That’s the actual goal of any AI SaaS MVP checklist-not completeness, but conviction.