case studymicro appsLLMs

Case Study: How a 7-Day Dining App Was Built with LLMs — Architecture, Costs, and Lessons Learned

ddeploy

2026-02-01

11 min read

A reconstructed 7-day LLM micro app build — timeline, architecture, costs, and how to harden it for production in 2026.

Hook — Why this case study matters to teams that must ship faster (and cheaper)

Decision fatigue, fragmented tooling, and rising hosting bills are familiar pain points for engineering teams and DevOps owners in 2026. Rebecca Yu’s 7-day dining micro app — built with LLMs and minimal infra — is a useful, realistic blueprint for rapid prototyping. This case study recreates the timeline, architecture, cost analysis, and the exact pitfalls she hit, and then shows how to harden a micro app for production without blowing the budget.

Executive summary (most important info first)

In 7 days Rebecca built Where2Eat, a web micro app that recommends restaurants to small friend groups by combining prompts to Claude and ChatGPT with a simple data layer (places API + user prefs). The prototype cost ~USD 50–250 in third-party API charges for personal use and used a serverless front-end (edge functions) + managed DB. Key takeaways:

Rapid prototyping with LLMs is cheaper and faster than building bespoke recommendation logic.
Main cost driver is LLM inference and external places APIs; hosting and DB costs are minor at prototype scale.
Pitfalls include prompt drift, unpredictable latency, lack of caching, and weak auth controls.
Production hardening focuses on caching, rate-limiting, model selection, observability, and safer data handling.

Timeline — Day-by-day reconstruction of the 7-day build

The timeline below recreates a practical rapid-prototype sprint for a micro app using LLMs. Each day focuses on a small, deliverable milestone.

Day 0 — Plan & scope (2 hours)

Define MVP: group-based restaurant recommendations, simple preference inputs, shareable link.
Pick LLMs: Claude for longer contextual prompts + ChatGPT for fallback; choose a places API (Google Places or Yelp).
Infra choices: Next.js (edge), serverless functions, Supabase (auth + DB), Vercel hosting.

Day 1 — UI shell & routing (4–6 hours)

Scaffold Next.js app: pages for /, /room/[id].
Minimal UI for creating a room and adding friend preferences.

npx create-next-app@latest where2eat --tailwind

Day 2 — Integrate places API & DB

Connect to Google Places or Yelp for venue metadata.
Store rooms and user prefs in Supabase (or SQLite for simplest local testing).

# example Supabase SQL table
create table rooms (
  id uuid primary key default gen_random_uuid(),
  name text,
  created_at timestamptz default now()
);

create table members (
  id uuid primary key default gen_random_uuid(),
  room_id uuid references rooms(id),
  prefs jsonb
);

Day 3 — LLM prompt design & basic serverless API

Create a serverless POST /api/recommend that accepts room_id and returns ranked places.
Design prompt template to inject member prefs + venue candidates.

# simplified serverless handler (Node/Edge)
export default async function handler(req, res) {
  const { room_id } = await req.json();
  const prefs = await db.query('select prefs from members where room_id=$1', [room_id]);
  const places = await fetchPlacesNearby();

  const prompt = `Group prefs:${JSON.stringify(prefs)}\nPlaces:${JSON.stringify(places)}\nChoose top 3 with reasons`;

  const reply = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: { 'Authorization': 'Bearer ' + process.env.OPENAI_KEY },
    body: JSON.stringify({ model: 'gpt-4o-mini', messages:[{role:'user', content:prompt}] })
  });

  const text = await reply.json();
  res.json({ result: text });
}

Add copy, small animations, and short URLs for rooms.
Test with friends and iterate prompts based on feedback.

Day 5 — Add caching & prompt tweaks

Cache LLM responses per room for 10–60 minutes to reduce cost and latency (in-memory or Redis).
Introduce deterministic ranking rules for ties to stabilize outputs.

Day 6 — QA and heat testing

Run small load tests (50–100 simulated requests) to spot latency spikes.
Catch prompt failure modes (hallucinations, overconfident assertions) and add guardrails.

Day 7 — Launch to friends

Deploy to Vercel, share link; track usage and costs for the week.

Recreated architecture — components and why they were chosen

The prototype stack balances speed of development and minimal ops. Below is the recreated architecture and the rationale behind each component.

Front-end: Next.js (Edge)

Next.js gives rapid UI iteration, server-side rendering for first load, and edge functions for low-latency API responses. In 2026, edge runtimes are common for LLM-driven micro apps to reduce network hops to model inference endpoints.

Serverless LLM proxy (Edge functions)

A thin proxy keeps API keys server-side and performs prompt assembly. Edge functions reduce cold-start latency and are cost-efficient at small scale.

Data layer: Supabase (Auth + Postgres)

Supabase provides managed Postgres, Auth, and realtime subscriptions; it’s quick to integrate and fits an MVP budget. For single-user micro apps, SQLite or a JSON file may suffice, but Supabase enables easy scale.

Place data: Google Places / Yelp

External place metadata avoids building a local dataset. However it adds per-request cost and rate limits (a key pitfall we’ll analyze later).

Caching: In-memory or Redis

Caching LLM outputs and places queries reduces both latency and API spend. Redis is the recommended production alternative to ephemeral in-memory caches used during prototyping.

Cost analysis — what drove spend in the prototype

I reconstructed a practical cost table based on typical 2025–2026 pricing and Rebecca’s stated approach (Claude + ChatGPT). These are realistic ranges for a 7-day micro app used by a small circle (10–50 users).

Prototype (7 days) — plausible cost breakdown

LLM API calls (Claude + GPT): USD 30–200 — depends on model choice and prompt sizes. Using GPT-4o-mini or Claude Instant reduces costs; GPT-4o-large or Claude 3.0 longform raises costs.
Places API (Google/Yelp): USD 5–50 — small number of place queries during development and user testing.
Hosting (Vercel hobby): USD 0–20 — hobby-tier or free with credit.
Supabase (entry): USD 0–25 — free tier covers prototypes, paid for heavier usage.
Domain: USD 12–20/year.
Monitoring & error tracking: USD 0–25 via free tiers (Sentry) or built-in provider alerts.

Total prototype cost: roughly USD 50–350 for the initial 7-day window. The LLM usage dominates unless you run a large number of inference calls.

Monthly cost if 1,000 monthly active users (projected)

LLM costs: USD 300–2,000 depending on per-request prompt size and model.
Hosting & DB: USD 25–200 (managed tiers for Supabase + Vercel Pro).
Places API: USD 50–300 depending on cache hit rates.
Observability & security: USD 20–200.

For production, expect monthly run-rate of USD 400–2,700 for a small social micro app — again, model selection and caching determine most variance.

Pitfalls encountered and the causes (what went wrong)

Rebecca’s experience and our reconstructed build show the common pitfalls teams hit when shipping LLM-backed micro apps fast.

Uncontrolled LLM spend — naive prompts, wide context windows, and repeated calls (no caching) quickly inflate costs. Run a quick one-page stack audit to identify expensive calls early.
Latency spikes — sync LLM calls in request flow without retries or async work cause poor UX. Consider edge-first runtime patterns to reduce hops.
Hallucinations and inconsistent results — LLMs may invent details (a restaurant’s hours, menu) if not tied to authoritative data; grounding and provenance practices help when you index trusted metadata.
Places API rate limits — live place lookups were rate-limited during friend tests, causing degraded behavior.
Weak auth & link sharing — ephemeral share links without limits can expose rooms publicly.
Observability gap — missing metrics on model latency, token usage, and costs makes root-cause analysis difficult; pair with an observability & cost-control playbook.

Recommended alternatives and production hardening (detailed prescriptive fixes)

Below are practical, command-level, and architectural recommendations to take a 7-day micro app to robust production in a cost-conscious way.

1) Choose the right model for the job

Use lower-cost models for routine ranking (e.g., GPT-4o-mini, Claude Instant) and reserve larger models for nuanced summaries or heavy-context tasks.
Implement model tiering: short list generation with cheap model, final explanation with higher-capacity model.

2) Implement deterministic fallback logic

If the LLM returns an ambiguous answer, fallback to deterministic heuristics: score venues by average rating, distance, and shared tags.
Code snippet: simple scoring fallback

function deterministicRank(places, prefs) {
  return places.map(p => ({
    id: p.id,
    score: (p.rating || 3) * 10 - p.distance_km * 2 + (prefs.tags.includes(p.tag) ? 20 : 0)
  })).sort((a,b) => b.score - a.score);
}

3) Cache aggressively and use TTLs

Cache LLM responses per room for 10–60 minutes depending on expected group stability.
Cache place results for 1–24 hours; only refresh on explicit user action.
Use Redis for distributed cache to survive server restarts and scale horizontally.

# pseudocode: read-through cache pattern
const cached = await redis.get(cacheKey);
if (cached) return JSON.parse(cached);
const result = await callLLM(...);
await redis.set(cacheKey, JSON.stringify(result), 'EX', 600); // 10 minutes
return result;

4) Rate-limit and meter LLM calls

Rate-limit per-room and per-user to avoid abuse and runaway costs (edge middleware or API gateway).
Instrument token counts and set daily budgets per API key; fail closed with informative UI if the budget is exceeded.

5) Observability — measure model latency, tokens, and costs

Emit metrics: model name, prompt size, tokens consumed, response latency, error rate.
Hook into Prometheus/Grafana, Sentry, or built-in vendor telemetry to trigger alerts on cost spikes or error rates. See Observability & Cost Control for a starting playbook.

6) Data safety & privacy

Avoid sending PII in prompts. When you must include personal preferences, encrypt or strip identifiable fields before sending to third-party models.
Consider running on private model endpoints or self-hosted models (2026 introduces more affordable fine-tuned local inference) to reduce vendor data exposure.

7) Use vector DB + RAG for grounding and lower hallucinations

Index trusted place descriptions and user-generated notes into a vector DB (Pinecone, Weaviate, or open-source alternatives) and retrieve grounded evidence for prompts.
This reduces hallucinations because the model sees concrete context rather than free-form place lists.

8) CI/CD, secrets, and deployment hygiene

Store API keys in managed secrets (Vercel Secrets, AWS Secrets Manager). Rotate keys regularly. See best practices in hardening local JavaScript tooling.
Use staged deployments: preview for friends, canary for 1–5% of users, then full release.

Advanced 2026 strategies and trends to consider

Late 2025 and early 2026 brought several platform and model changes that affect micro app design. Use these to improve latency, cost, and control.

Edge-native LLM runtimes: Several providers now offer lower-latency edge-hosted model inference; great for conversational micros with tight UX needs. See edge-first layouts and runtime patterns.
On-device and small-model inference: Lightweight, distilled models running on user devices reduce inference costs and privacy exposure for small-scale apps; read field reviews of local-first sync appliances to learn about on-device tradeoffs.
Model marketplaces: Choose inference units from competitive marketplaces to lower per-request costs and pick specialized models for recommendation tasks.
RAG standardization: Tooling for retrieval-augmented generation is mature—adopt vector DB + chunking + prompt templating to reduce hallucinations.

Actionable checklist: Steps to move from prototype to production in 30 days

Implement caching for LLM & places API with Redis and set sensible TTLs.
Add rate limiting & per-key budgeting; block calls if budget exceeded.
Switch to model tiering and log token usage to a central store daily.
Replace public share links with expiring tokens and optional password protection.
Integrate Sentry (errors) + Prometheus (metrics) and create two cost/latency alerts: token spend > X/day, median latency > Y ms.
Index authoritative place metadata in a vector DB and use RAG for grounding before invoking LLMs.
Audit prompts for PII and add sanitization layers before sending to external models.

Lessons learned — distilled

Rapid prototyping with LLMs is powerfully effective for micro apps, but the jump from prototype to production requires predictable costs and stronger guardrails.
Design for instability: expect model drift and external API limits; build deterministic fallbacks and caches.
Measure everything: token counts, latencies, and errors are the core signals you’ll use to control spend and maintain UX.
Gradual scale: prioritize rate-limiting, auth, and secrets management early — they are cheap to add and expensive to retrofit after abuse. If you need a quick stack audit, see Strip the Fat.

"Once vibe-coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps," Rebecca said — and her 7-day run shows exactly why: speed and focus beat overengineering at the prototype stage. But production needs an extra layer of engineering discipline.

Final takeaway and next steps

Micro apps built with LLMs are a viable strategy for rapid experimentation in 2026, but to keep them useful, affordable, and safe you must move quickly from prototype hacks to production practices: caching, model tiering, observability, and deterministic fallbacks. The reconstructed 7-day timeline and the hardening checklist above give you a practical playbook to do just that.

Call to action

If you’re evaluating a micro app or an LLM-backed feature, run a quick 7-day spike with the architecture above, then apply the 30-day checklist to harden it. Need a hands-on audit or an actionable migration plan from prototype to production? Reach out to deploy.website for a targeted evaluation and cost-controlled production blueprint tailored to your stack.

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