an AI meal planner for US home cooks
Estimated monthly cost at the usage scale below — billed by actual usage
2.33 req/s
Peak Concurrency
1.52 GB
Database Storage
0 GB
Object Storage
0 GB
Outbound data transfer
What is this app
AI Meal Planner is a US-focused app for people who cook their own meals at home. Users enter body statistics, activity level, a nutrition goal, and food preferences or restrictions, and the app generates a three-day plan with breakfast, lunch, and dinner. Each meal's calorie and macro values are retrieved from the USDA food nutrition database, and the app creates a consolidated shopping list in grams by reusing core ingredients across the plan.
As an anonymous visitor, I can learn what the AI Meal Planner does before deciding whether to use it
As a home cook, I can enter my body statistics, activity level, goal, food preferences, and restrictions
As a home cook, I can generate a three-day plan containing breakfast, lunch, and dinner for each day
As a home cook, I can review the calories and macros for every planned meal using USDA nutrition data
As a home cook, I can view a consolidated shopping list with ingredient quantities in grams
0
anonymous_visitors
People who view the public product explanation without creating or using a registered meal plan.
50000
home_cooks
Registered US users who enter personal nutrition inputs, generate three-day meal plans, review nutrition details, and use consolidated shopping lists.
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
Multiple workflows and external USDA integration require at least 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
3 req/s
not purchased
(plan/kit 5 req/s)
Covered by plan/kit (margin ~67%)
0
Database storage add-on
4.76 GB
10GB × 1/year, amortized monthly
(plan/kit 200.00 MB + add-on +4.56 GB)
Add-on fills +4.56 GB gap beyond plan/kit
8.33
Object storage
add-on
0 B
not purchased
(plan/kit 2.00 GB)
No requirement
0
Outbound data transfer add-on
0 B/mo
not purchased
(plan/kit 2.00 GB/mo)
No requirement
0
AI points add-on
279.7M points
6,000,000 AI Points × 47/month
(plan/kit 283.0M points + add-on +278.7M points)
Add-on fills +278.7M points gap beyond plan/kit
470
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.
2 × t4g.large × 730h
EC2 t4g.large → 98.11
2 × db.m6g.large (Multi-AZ) × 730h
RDS PostgreSQL db.m6g.large → 232.14
1.63 GB-month
RDS gp3 storage → 0.19
0.00 GB-month
S3 Standard → 0
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $330.44 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL
(INFRA + OPS)
Traditional outsourcing / build in-house
about $110,000 - $170,000
≈ $330
Based on the AWS estimate above
≈ $2,330
Infra $330 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $14,000 - $22,000
≈ $330
Based on the AWS estimate above
≈ $1,830
Infra $330 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $6,000 - $10,000
≈ $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
$47
All-in: egress / storage / auto-scaling; excludes AI usage
$47
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 description does not provide an explicit user count or a concrete geographic population size. This is the standard planning assumption for an internet-facing consumer app and can be adjusted when the expected US launch audience or customer base is known.
We assume this is a standard linear-growth consumer SaaS product because users and generated plans are expected to accumulate progressively after launch rather than arriving as a full pre-existing dataset. If the app launches with a large imported customer and plan history or experiences a rapid early burst followed by a stable ceiling, this parameter can be adjusted.
Evening Meal Planning Peak
Evening Meal Planning Peak
Evening Meal Planning Peak
30
times/month
2.33 req/s
Peak load
We assume evening meal planning is the principal concentrated scenario because the product is used to prepare a future three-day eating schedule. Generation and review are represented as separate purposes: generation creates business records and consumes AI and USDA processing, while review reads the resulting records. The four-hour duration reflects a voluntary planning period rather than a strong external signal, and the scenario occurs daily because planning demand is distributed across the month. The participant counts are flattened arrivals across the period and remain within the registered home-cook pool.
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.
Anonymous browsing flow for understanding the meal-planning product.
Home cook setup flow for entering the personal inputs used by meal generation.
Generates a three-day plan and reviews its nine meals and nutrition values.
Reviews the generated meals and the consolidated ingredient list.
page
data_table
ai_agent
action_flow
App Overview Page
Introduces the AI Meal Planner and explains its core value to visitors before they create a plan.
Meal Planner Setup Page
Collects and stores the personal inputs used to generate a meal plan.
Plan Generation Page
Starts the plan-generation process and presents progress before directing the user to the generated plan.
Meal Plan Detail Page
Displays each day's meals and the USDA-backed calorie and macro details.
Shopping List Page
Shows the combined ingredients and gram quantities needed for the selected three-day plan.
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 this app's resource usage?
How are calories and macros kept accurate?
What is included in the planned MVP?
How does the shopping list avoid half-used ingredients?
Copy to discuss with AI
Challenge & re-estimate