an AI meal planner for home cooks
What this app does
AI Meal Planner is a US-focused meal-planning app for people who cook at home. Users provide body statistics, activity level, goal, food preferences, and restrictions, then receive a 3-day plan covering breakfast, lunch, and dinner, with calorie and macronutrient values validated through the USDA food nutrition database. The app also creates a gram-based shopping list that reuses ingredients across the plan to reduce partially used food.
As a home cook, I can enter and update my body statistics, activity level, meal goal, food preferences, and dietary restrictions
As a home cook, I can generate a 3-day meal plan with breakfast, lunch, and dinner
As a home cook, I can view the meal calories and macronutrients based on USDA nutrition data
As a home cook, I can view a gram-based shopping list that reuses ingredients across my meal plan
500
home_cooks
Registered US users who cook at home and provide personal planning information to receive meal plans and shopping lists.
How your requirements become a plan and a price
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
External-facing app needs durable publishing and branding
See the capability-by-capability assessment ▾
PROJECT DEMAND
ALLOWANCE PROVIDED (COMPOSITION)
Basic
x 1
Basic is the minimum viable plan
Basic
(Plan tier: BASIC)
See minimum viable plan above
$ 39
single-tenant kit
x 0
1 req/s
not purchased
(plan/kit 5 req/s)
Covered by plan/kit (margin ~400%)
$ 0
Database storage add-on
x 0
21.37 MB
not purchased
(plan/kit 200.00 MB)
Covered by plan/kit (margin ~836%)
$ 0
Object storage
add-on
x 0
0 B
not purchased
(plan/kit 2.00 GB)
No requirement
$ 0
Outbound data transfer add-on
x 0
0 B/mo
not purchased
(plan/kit 2.00 GB/mo)
No requirement
$ 0
AI points add-on
x 0
0 points
not purchased
(plan/kit 1.0M points)
No requirement
$ 0
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. Figures cover infrastructure and operations only — AI usage is billed separately and excluded here, so they don't match the $39/mo all-in above.
Current usage sizing
AWS list pricing
2 × t4g.large × 730h
EC2 t4g.large → 98.11
2 × db.m6g.large (Multi-AZ) × 730h
RDS PostgreSQL db.m6g.large → 232.14
0.02 GB-month
RDS gp3 storage → 0
0.00 GB-month
S3 Standard → 0
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $330.25 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL (INFRA + OPS, EXCL. AI USAGE)
Traditional outsourcing / build in-house
about $55,000 - $85,000
≈ $330
Based on the AWS estimate above
≈ $2,330
Infra $330 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $8,000 - $15,000
≈ $330
Based on the AWS estimate above
≈ $1,830
Infra $330 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $3,500 - $7,000
≈ $330
Based on the AWS estimate above
≈ $1,830
Infra $330 + 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
$39
All-in: egress / storage / auto-scaling; excludes AI usage
$39
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 ≈ $330.25/mo at AWS list prices, often more), on top of the dev and ops staff you'd need to hire. Momen bundles all of it into $0/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 a small registered customer pool of 500 because the description states that the app has 50 active users per day, indicating an early-stage consumer product with several hundred registered home cooks rather than a mass-market platform. If the 50 daily active users represent only a pilot segment or if repeat usage is substantially higher or lower, this parameter can be adjusted accordingly.
We assume a standard linear-growth pattern because a consumer meal-planning app typically builds its registered base gradually rather than launching with a full pre-existing dataset or experiencing an early burst followed by a plateau. If actual adoption is concentrated in a recent launch campaign or the app imports an established customer database, this parameter can be adjusted accordingly.
Meal Plan Generation Peak
Meal Plan Generation Peak
30
times/month
0.67 req/s
Peak load
We assume meal-plan generation forms a standalone short scenario because it is the product's main active request and invokes AI composition, USDA lookups, and several coordinated database writes. The request is user initiated rather than externally synchronized, so a five-minute window represents a realistic concentrated period when many home cooks submit planning requests and wait for results. The scene occurs on ordinary active days, so a daily monthly frequency is appropriate. This scene contains one purpose because generating and reviewing the resulting plan are parts of the same meal-planning journey rather than unrelated parallel behaviors.
Main impact: Peak Concurrency
The scenarios above set the assumptions for each resource; below is the full calculation derived from them. 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
A home cook opens the profile setup page, reviews any saved information, and saves body and dietary inputs.
Profile Setup Page
users
A home cook submits the meal-planning request, waits for the generated plan, and reviews the meals, nutrition values, and shopping list.
users
meal_plan_composer
generate_meal_plan_flow
page
data_table
ai_agent
action_flow
Profile Setup Page
Allows a home cook to enter and review body statistics, activity level, goal, food preferences, and restrictions.
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 determines the system's resource usage?
Are calorie and macro values generated by AI?
Does the app store images?
What happens when ingredients repeat across meals?
Copy to discuss with AI
Challenge & re-estimate
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