a plant placement app for home gardeners
PROJECT OVERVIEW
What this app does
This app helps people identify suitable locations for plants within a photographed setting. Users can filter the desired planting scenario—such as colourful, tall and vision-blocking, lush, small, or exotic—and receive plant placement recommendations that match those preferences.
As an anonymous visitor, I can browse planting scenarios and filter the plant catalogue by visual and structural preferences
As a plant planner, I can create an account so that my placement plans can be saved
As a plant planner, I can upload a setting image and select a planting scenario to receive recommended plant locations
As a plant planner, I can review my saved setting projects and their placement recommendations
As a catalogue administrator, I can create and update plants and their filtering attributes
As a catalogue administrator, I can create and update the planting scenario presets offered to users
The following user counts are projected totals after one year of growth
10
catalog_admins
Internal users who maintain the plant catalogue and the available planting scenario presets.
2990
plant_planners
Registered gardeners and design users who upload settings, request placement recommendations, and revisit saved plans.
1000
anonymous_visitors
Public visitors who browse planting scenarios and plant examples without an account.
COST BREAKDOWN
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
The public, ongoing, professionally branded plant-planning application requires at least the BASIC plan.
See the capability-by-capability assessment ▾
PROJECT DEMAND
ALLOWANCE PROVIDED (COMPOSITION)
AI points add-on
x 7
41.5M points
6,000,000 AI Points × 7/month
(plan/kit 43.0M points + add-on +40.5M points)
Add-on fills +40.5M points gap beyond plan/kit
$ 70
Outbound data transfer add-on
x 4
133.41 GB/mo
500GB × 4/year, amortized monthly
(plan/kit 2.00 GB/mo + add-on +131.41 GB/mo)
Add-on fills +131.41 GB/mo gap beyond plan/kit
$ 11.67
Object storage
add-on
x 1
82.47 GB
100GB × 1/year, amortized monthly
(plan/kit 2.00 GB + add-on +80.47 GB)
Add-on fills +80.47 GB gap beyond plan/kit
$ 2.92
Database storage add-on
x 1
241.01 MB
10GB × 1/year, amortized monthly
(plan/kit 200.00 MB + add-on +41.01 MB)
Add-on fills +41.01 MB gap beyond plan/kit
$ 8.33
single-tenant kit
x 0
1 req/s
not purchased
(plan/kit 5 req/s)
Covered by plan/kit (margin ~400%)
$ 0
Basic
x 1
Basic is the minimum viable plan
Basic
(Plan tier: BASIC)
See minimum viable plan above
$ 39
plan + add-ons · usage-based · no development cost
WHY MOMEN
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 $131.92/mo all-in above.
Current usage sizing
AWS list pricing
0.00 GB (first 100GB free)
Internet egress → 0
82.47 GB-month
S3 Standard → 1.9
0.24 GB-month
RDS gp3 storage → 0.03
2 × db.m6g.large (Multi-AZ) × 730h
RDS PostgreSQL db.m6g.large → 232.14
2 × t4g.large × 730h
EC2 t4g.large → 98.11
Total ≈ $332.18 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL (INFRA + OPS, EXCL. AI USAGE)
Traditional outsourcing / build in-house
about $90,000 - $130,000
≈ $332
Based on the AWS estimate above
≈ $2,332
Infra $332 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $18,000 - $25,000
≈ $332
Based on the AWS estimate above
≈ $1,832
Infra $332 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $8,000 - $11,000
≈ $332
Based on the AWS estimate above
≈ $1,832
Infra $332 + 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
$62
All-in: egress / storage / auto-scaling; excludes AI usage
$62
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 ≈ $332.18/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 $70/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.
SCALE & SIZING
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 this product fits the AI generation and recommendation tool category because its principal registered-user value comes from submitting a setting and receiving an AI-produced placement plan. With no explicit user count or request throughput supplied, the typical mature registered-account anchor for an independent tool of this kind is 3,000 accounts. This includes ordinary planners and a very small catalogue administration team but excludes anonymous visitors. If your launch has a known audience, existing customer list, or measured request volume, this parameter can be adjusted accordingly.
We assume this is a new consumer-facing AI design application with gradual adoption because standard independent software products usually build their registered base and catalogue activity progressively during the first year. The equivalent accumulation period is therefore set to the standard linear-growth value rather than treating the application as fully loaded from launch. If your launch includes a full pre-existing user and project dataset, this parameter can be adjusted accordingly.
Daily Planner Activity
Daily Planner Activity
Daily Planner Activity
Daily Public Catalogue Browsing
Daily Planner Activity
30
times/month
0.06 req/s
Peak load
We assume registered planning activity is concentrated into a four-hour after-work and leisure window because home-gardening design tools are commonly used during evenings without a precise external starting signal. Configuration, AI plan generation, history browsing, and opening a saved result are separated because they have different intent, write behavior, AI use, and image delivery. The scenario occurs daily, giving 30 monthly occurrences. If the product primarily serves professional landscapers during working hours, the window can be adjusted accordingly.
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.
DEVELOPMENT SCOPE
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 catalogue administrator reviews, creates, and refines planting scenario presets.
Plant Catalogue Page
users
A catalogue administrator finds an existing plant and corrects its presentation or filtering attributes.
Plant Catalogue Page
users
A catalogue administrator reviews the existing catalogue and adds a new plant.
Plant Catalogue Page
users
A planner revisits saved setting projects and opens one previous placement plan.
Plant Catalogue Page
users
A registered planner selects a scenario, uploads a setting, generates a placement plan, and reviews the recommendations.
Plant Catalogue Page
users
plant_placement_adviser
generate_placement_plan
A prospective planner creates an account before saving a placement project.
Plant Catalogue Page
users
A visitor explores planting styles and filters the available plant catalogue without creating a plan.
Plant Catalogue Page
users
page
data_table
ai_agent
action_flow
Catalogue Administration Page
Internal page for maintaining plant records, representative images, filtering attributes, and scenario presets.
Saved Plans Page
History page listing a planner's saved setting projects and their processing states.
Placement Results Page
Result page showing the submitted setting and the ranked plant placement recommendations generated for it.
Plant Placement Planner Page
Workspace where registered planners select a scenario, upload a setting image, and request a placement plan.
Account Registration Page
Registration form used to create an account before saving placement plans.
Plant Catalogue Page
Public page for exploring planting scenarios and filtering representative plants by desired appearance and screening qualities.
FAQ
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
Does the app require third-party APIs?
Can the plant catalogue and scenario filters be maintained after launch?
Does the estimate include multiple images for each setting or plant?
What activities will drive most of this app's resource usage?
Copy to discuss with AI
Challenge & re-estimate
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