a meal-planning app for home cooks
Estimated monthly cost at the usage scale below — billed by actual usage
0.73 req/s
Peak Concurrency
1.09 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 who cook at home in the United States. Users provide body statistics, activity level, a goal such as losing weight, building muscle, or maintaining weight, along with food preferences and restrictions. The app generates a three-day plan with nine meals, validates calorie and macronutrient values through the USDA food nutrition database, and creates a gram-based shopping list that reuses core ingredients across the plan.
As a registered home cook, I want to enter my body statistics, activity level, goal, food preferences, and restrictions so that the app can personalize my meal plan
As a registered home cook, I want to generate a three-day meal plan with USDA-backed calorie and macro values so that I can follow meals aligned with my goal
As a registered home cook, I want to view a consolidated shopping list in grams so that I can buy the ingredients needed for the plan without unnecessary leftovers
As an anonymous visitor, I want to view an explanation of the meal-planning product so that I can decide whether to register
40000
registered_home_cooks
People who create an account, enter personal nutrition inputs, generate meal plans, and review shopping lists.
0
anonymous_visitors
Public visitors who can view the product introduction but do not access personalized meal-planning data.
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 lookup, meal generation, and shopping-list workflows require multiple integrations and workflows, while the external consumer app needs ongoing publishing, branding, and watermark removal; therefore BASIC is the minimum plan.
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
1 req/s
not purchased
(plan/kit 5 req/s)
Covered by plan/kit (margin ~400%)
0
Database storage add-on
3.34 GB
10GB × 1/year, amortized monthly
(plan/kit 200.00 MB + add-on +3.14 GB)
Add-on fills +3.14 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
248.4M points
6,000,000 AI Points × 42/month
(plan/kit 253.0M points + add-on +247.4M points)
Add-on fills +247.4M points gap beyond plan/kit
420
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
1.17 GB-month
RDS gp3 storage → 0.13
0.00 GB-month
S3 Standard → 0
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $330.39 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL
(INFRA + OPS)
Traditional outsourcing / build in-house
about $110,000 - $180,000
≈ $330
Based on the AWS estimate above
≈ $2,330
Infra $330 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $14,000 - $24,000
≈ $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
$467
All-in: egress / storage / auto-scaling included
$467
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 the product reaches a moderate US consumer audience with a substantial registered pool and a smaller public browsing pool because home meal planning is broadly applicable but the product is not described as a mass-market social network. If your actual marketing reach or retention differs, this population anchor can be adjusted accordingly.
We assume this is a standard linear-growth consumer SaaS product because usage should build progressively as more home cooks discover and register for the planner, rather than launching with a full pre-existing dataset or reaching an immediate fixed ceiling. If the product launches with an established user base or experiences a strong short-lived launch burst, this equivalent operating period can be adjusted.
Evening Planning and Shopping Review
Evening Planning and Shopping Review
Evening Planning and Shopping Review
30
times/month
0.73 req/s
Peak load
We assume the main authenticated workload occurs during a broad evening period when home cooks plan the next few days and prepare grocery lists. This is weakly assembled usage without an external signal, so the duration represents a typical four-hour window. It occurs daily because meal planning and shopping preparation happen throughout the month. The purposes are separated into generation and shopping-list review because generation is a write-heavy AI and database operation, while shopping-list review is a read-only activity with a different demand profile.
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.
Public browsing flow for visitors who have not registered or signed in.
Primary registered-cook flow from personal setup through plan generation and review.
Registered-cook flow for reviewing the generated meals and corresponding shopping list.
page
data_table
ai_agent
action_flow
Landing Page
Public entry page explaining the meal-planning service and directing cooks to begin setup.
Profile Setup Page
Form for collecting and reviewing the personal inputs used to generate meal plans.
Meal Plan Page
Generation and review page for a cook's current three-day meal plan.
Shopping List Page
Consolidated shopping list showing reusable ingredients and quantities in grams.
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 is included in the meal planner MVP?
What will most affect resource usage?
Why is plan generation more resource-intensive than browsing?
How are calories and macros determined?
Copy to discuss with AI
Challenge & re-estimate