an AI meal planner for home cooks
Estimated monthly cost at the usage scale below — billed by actual usage
250 req/s
Peak Concurrency
9.6 GB
Database Storage
0 GB
Object Storage
0 GB
Outbound data transfer
What is this app
AI Meal Planner helps people in the US who cook their own meals at home create personalized three-day meal plans. Users provide body statistics, activity level, a goal, food preferences, and restrictions, and the app generates breakfast, lunch, and dinner for each day. Calorie and macro values are looked up from the USDA food nutrition database, while the app also consolidates reused ingredients into a shopping list measured in grams.
As a home cook, I want to enter my body statistics, activity level, goal, food preferences, and restrictions so that the planner can personalize my meals.
As a home cook, I want to generate a three-day plan with breakfast, lunch, and dinner so that I know what to cook.
As a home cook, I want to review verified calories and macros for each meal and see a consolidated shopping list in grams so that I can shop efficiently.
As an anonymous visitor, I want to browse the meal-planning introduction and try the core planner so that I can evaluate it before registering.
50000
home_cooks
Registered people in the US who cook at home and use their body statistics, activity level, goals, food preferences, and restrictions to generate meal plans.
0
anonymous_visitors
Unregistered visitors who browse the public introduction and may try the meal-planning experience without a saved account profile.
Requirements to minimum viable plan to cost
Momen doesn't give a vague quote: it first sizes the project's actual demand for each capability and resource, then costs each item out.
The numbers in the "Project demand" column below — their scale assumptions and derivations — are detailed in Scale & sizing.
Minimum viable plan
BASIC
USDA integration and public deployment require BASIC.
See the capability-by-capability assessment ▾
PROJECT DEMAND
ALLOWANCE PROVIDED (COMPOSITION)
Basic
Basic is the minimum viable plan
Basic
(Plan tier: BASIC)
See minimum viable plan above
39
single-tenant kit
250 req/s
single-tenant kit × 2
(plan/kit 305 req/s)
Covered by plan/kit (margin ~22%)
240
Database storage add-on
10.30 GB
10GB × 1/year, amortized monthly
(plan/kit 4.20 GB + add-on +6.10 GB)
Add-on fills +6.10 GB gap beyond plan/kit
8.33
Object storage
add-on
0 B
not purchased
(plan/kit 102.00 GB)
No requirement
0
Outbound data transfer add-on
0 B/mo
not purchased
(plan/kit 102.00 GB/mo)
No requirement
0
AI points add-on
516.5M points
6,000,000 AI Points × 86/month
(plan/kit 517.0M points + add-on +515.5M points)
Add-on fills +515.5M points gap beyond plan/kit
860
plan + add-ons · usage-based · no development cost
Your options for this project
In the table below, the "monthly infrastructure" for the self-built / AI routes is derived from the AWS list prices shown below, sized against this project's actual usage.
5 × t4g.large × 730h
EC2 t4g.large → 245.28
2 × db.m6g.large (Multi-AZ) × 730h
RDS PostgreSQL db.m6g.large → 232.14
10.30 GB-month
RDS gp3 storage → 1.18
0.00 GB-month
S3 Standard → 0
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $478.6 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL
(INFRA + OPS)
Traditional outsourcing / build in-house
about $120,000 - $180,000
≈ $479
Based on the AWS estimate above
≈ $2,479
Infra $479 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $18,000 - $26,000
≈ $479
Based on the AWS estimate above
≈ $1,979
Infra $479 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $7,000 - $11,000
≈ $479
Based on the AWS estimate above
≈ $1,979
Infra $479 + ops ~$1,500
hours
3 / 5
Off-the-shelf SaaS / vertical solution
$0
N/A
Priced per seat, not by cloud infra
≈ $16,000+
Per-seat pricing, tens of thousands of users
days
1 / 5
Momen
Recommended
$0
$287
All-in: egress / storage / auto-scaling; excludes AI usage
$287
hours
5 / 5
Servers, databases, traffic and storage are inherent infrastructure costs for this project — you pay them whether you outsource, use Cursor or Lovable, or build it yourself (self-built runs ≈ $441/mo at AWS list prices, often more), plus the extra dev and ops staff. Momen bundles all of it into $368/mo all-in and removes the need for an ops team.
The frontend can be generated with AI tools (Cursor, Lovable, etc.); the hard part is the backend — auth, database, scaling, data security and ops. Momen delivers a production-grade backend as a BaaS: keep the vibe-coding speed on the frontend, while the backend runs on proven infrastructure — reliable, with no self-hosting or ops.
What scale this estimate assumes, and how the numbers are derived
Cost depends heavily on usage volume. First see the key assumptions and business scenarios this estimate uses, then the full calculation derived from each scenario for every resource — all adjustable to your real situation.
We assume 50000 registered accounts because the project description gives no explicit user count or concrete business scale. This is the standard planning assumption for an otherwise unspecified consumer SaaS product, and it can be adjusted when launch or customer data is available.
We assume 225 equivalent running days because this is a standard consumer SaaS product expected to grow progressively rather than launch with a full pre-existing dataset or experience a short recent burst. If the app launches with a large imported user and plan history or has a strongly seasonal adoption pattern, this parameter can be adjusted.
Evening Plan Generation Peak
Daily Plan Review
Daily Plan Review
30
times/month
249.6 req/s
Peak load
We assume this is a standalone scenario because home cooks commonly plan meals after work and anonymous visitors often try the product during the same evening period, creating the strongest concentration of AI generation requests. The two purposes are separated by account status and flow: registered users load and may save profiles, while anonymous visitors use the public introduction and skip profile actions. We use a 300-second duration because evening demand is concentrated but not synchronized by an external signal, and we use 30 monthly occurrences because this pattern is expected on most days of a stable month.
Main impact: Peak Concurrency
The scenarios above set the assumptions for each resource; below is the full calculation based on those assumptions. Click to expand each item.
How this app works
What exactly does this budget support? Broken down by business scenario, showing the pages, data tables, automation flows and AI
assistant behind each one.
Registered user creates and reviews a personalized three-day meal plan.
Anonymous visitor tries the core meal-planning experience and reviews the resulting shopping list.
page
data_table
ai_agent
action_flow
Meal Planner Input Page
Form for entering the body statistics, activity level, goal, food preferences, and restrictions used to create a meal plan.
Generated Meal Plan Page
Displays the generated three-day plan, nine meals, ingredient quantities, and verified nutrition values.
Shopping List Page
Shows the consolidated ingredient list and total gram quantities needed for the three-day plan.
Meal Planning Landing Page
Public entry page introducing the meal planner and directing visitors to enter their planning information.
What you might want to know about this project
The Q&A below is generated by AI based on this project's type, features and scale.
What will drive resource usage for this meal-planning app?
How are calorie and macro values calculated?
How does the shopping list avoid half-used ingredients?
Can people use the planner without registering?
Copy to discuss with AI
Challenge & re-estimate