an AI meal planner for US home cooks
Estimated monthly cost at the usage scale below — billed by actual usage
9.33 req/s
Peak Concurrency
3.66 GB
Database Storage
0 GB
Object Storage
0 GB
Outbound data transfer
What is this app
AI Meal Planner is a meal-planning app for people in the US who cook at home. Users provide body stats, activity level, a goal, and food preferences or restrictions, and the app generates a three-day plan with nine meals. Calories and macros are populated through real USDA food nutrition lookups, while the app also creates a gram-based shopping list that reuses core ingredients across the plan.
As an anonymous visitor, I can browse an introduction to the home meal-planning service
As a home cook, I can enter my body stats, activity level, goal, food preferences, and restrictions
As a home cook, I can generate a three-day meal plan with breakfast, lunch, and dinner
As a home cook, I can review the generated meals with USDA-based calorie and macro values
As a home cook, I can view a consolidated shopping list in grams with reused core ingredients
50000
home_cooks
Registered people in the US who cook at home and use personal body, activity, goal, and dietary inputs to generate meal plans and shopping lists.
0
anonymous_visitors
Unregistered visitors who can view the public introduction but do not create profiles or generate plans.
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 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
10 req/s
single-tenant kit × 1
(plan/kit 155 req/s)
Covered by plan/kit (margin ~1450%)
120
Database storage add-on
3.93 GB
10GB × 1/year, amortized monthly
(plan/kit 2.20 GB + add-on +1.73 GB)
Add-on fills +1.73 GB gap beyond plan/kit
8.33
Object storage
add-on
0 B
not purchased
(plan/kit 52.00 GB)
No requirement
0
Outbound data transfer add-on
0 B/mo
not purchased
(plan/kit 52.00 GB/mo)
No requirement
0
AI points add-on
108.6M points
6,000,000 AI Points × 18/month
(plan/kit 109.0M points + add-on +107.6M points)
Add-on fills +107.6M points gap beyond plan/kit
180
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
3.93 GB-month
RDS gp3 storage → 0.45
0.00 GB-month
S3 Standard → 0
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $330.7 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL
(INFRA + OPS)
Traditional outsourcing / build in-house
about $120,000 - $180,000
≈ $331
Based on the AWS estimate above
≈ $2,331
Infra $331 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $15,000 - $24,000
≈ $331
Based on the AWS estimate above
≈ $1,831
Infra $331 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $7,000 - $10,000
≈ $331
Based on the AWS estimate above
≈ $1,831
Infra $331 + 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
$167
All-in: egress / storage / auto-scaling; excludes AI usage
$167
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 does not provide an explicit user count or a directly implied population scale. This is the standard planning assumption for an unspecified consumer application and can be adjusted when the expected US customer base is known.
We assume this is a standard linear-growth consumer SaaS product because usage should build progressively as more home cooks adopt the planner rather than launch with a full pre-existing dataset or an immediate fixed ceiling. If the app launches with a large imported customer base or experiences a short, intense burst of usage, this parameter can be adjusted accordingly.
Weekly Plan Generation Peak
Meal Plan Review
Routine Plan Setup
4
times/month
9.33 req/s
Peak load
We assume weekend meal preparation creates a recognizable weekly planning period with more simultaneous generation activity than ordinary weekdays. The scenario contains one purpose because profile setup and plan generation are parts of the same authenticated planning intent, while review and shopping are modeled separately. We assume a 900-second window for the concentrated planning burst and four monthly occurrences because this behavior is typically weekly rather than daily.
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 learning about the meal-planning service.
Profile entry and new meal-plan generation flow.
Review flow for examining the generated meals and nutrition values.
Flow for using the plan's consolidated shopping list.
page
data_table
ai_agent
action_flow
Home Page
Public entry page explaining the meal-planning service to visitors.
Meal Planner Setup Page
Form for entering the personal inputs used to generate a meal plan.
Meal Plan Results Page
Displays the generated three-day plan and the nutrition values for each meal.
Shopping List Page
Shows the consolidated shopping list created from the selected meal 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 most?
Are the nutrition values generated by AI?
What does one generated meal plan contain?
Does every plan require a fresh USDA lookup?
Copy to discuss with AI
Challenge & re-estimate