AI wrappers
AI-wrapper SaaS stack
AI-wrapper SaaS needs five slots the solo-founder stack does not need: model gateway, observability, rate limiting, vector store, and prompt management. The right pick at each slot avoids the two most common AI-wrapper failure modes: cost explosion under traffic and inability to debug bad outputs in production.
Who: Founders shipping an AI-powered SaaS (chat, agent, generation tool) where the model call is the core value proposition.
Cost ceiling at launch scale: $50-$200/month plus model usage. Model usage is the dominant cost — budget for $200-$2,000/month per 1,000 active users depending on the workload.
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The 5 slots
Model gateway
Vercel AI Gateway
Unified API across model providers with built-in failover, cost tracking, and zero-data-retention configuration. Replaces direct provider SDKs that lock you in.
Cost: usage-based on top of provider cost
Frontend + streaming
Next.js + Vercel AI SDK
Server-Sent Events streaming, structured output, tool calling, and React hooks for chat UIs. Tightly coupled to the rest of the Vercel stack.
Cost: free (open source)
Database + auth
Supabase
Same reason as the solo-founder stack — but with the added benefit of pgvector for in-Postgres embeddings if vector volume is small.
Cost: $0-$25/month
Payments
Usage-based billing via Meters is the right model for AI products with per-token cost variance.
Cost: 2.9% + 30¢ per charge
Observability
Helicone or Langfuse
Log every prompt, response, latency, cost, and outcome. Mandatory for AI products — you cannot debug 'the model said something weird' without traces.
Cost: $0-$50/month at launch
When NOT to use this stack
If the AI is one step in a larger workflow (not the core value), the solo-founder stack plus one model call is enough. Do not over-stack.
Common mistakes
- Direct-binding to one model provider's SDK. When pricing changes or the model degrades, you have a migration to do under pressure. Use the gateway from day one.
- Skipping observability because 'it works in dev'. The first production-only edge case will land in week two and you will have no trace to debug from.
- Pricing per-seat when the cost is per-token. Mismatched pricing axis on AI products is the most common reason early customers churn — they hit a heavy-user pattern that loses you money.
When to swap a slot
- Swap pgvector for a dedicated vector store (Pinecone, Turbopuffer) once embedding count exceeds 10M or query latency matters.
- Add a rate limiter (Upstash Redis) once you have a freemium tier — without it, one user can torch your model budget in an hour.
- Add prompt management (PromptLayer, Helicone) once you have more than 5 prompts in production.
Tuned for these niches
Related categories
Related Brunson terms
Frequently asked
- Should I build on top of OpenAI or Anthropic directly?
- Neither directly. Route through a gateway from day one. The cost of switching providers later is much higher than the cost of the gateway today. Provider stability changes; the gateway abstraction does not.
- Do I need a vector store?
- Only if you are doing retrieval-augmented generation at scale. For most pre-revenue AI wrappers, pgvector inside Supabase is enough until the embedding count crosses 10M. Premature vector-store choice is a cost sink.
Other stack recommendations
Stack is right, but page is broken?
The free 90-second Launch Diagnostic looks at your live product page and labels the failure mode — Wrong Person, Weak Offer, or Weak Belief. The stack is necessary; the funnel is sufficient.