an AI roofing quote app for US homeowners
What this app does
RoofSnap is an online quoting service for a US residential roofing contractor. Homeowners submit a roof address and four to six roof photos, after which AI estimates roof area, identifies visible damage, and returns a price range with a structured issue list. The system geocodes the address, calculates driving distance from the contractor’s shop, and gives an eight-person contractor staff dashboard for accepting, declining with a note, or requesting additional photos before a crew accepts the work.
As a homeowner, I can submit my roof address and four to six roof photos to request an online quote
As a homeowner, I can view the estimated roof area, price range, travel distance, and detected roof issues for my request
As a homeowner, I can provide additional roof photos and a note when the contractor requests more information
As a contractor staff member, I can review submitted quote requests together with their roof photos, AI findings, and travel distance
As a contractor staff member, I can accept a quote request
As a contractor staff member, I can decline a quote request with a note
As a contractor staff member, I can request more roof photos from a homeowner with a note
As an anonymous visitor, I can view the public quote submission entry point before creating or submitting a request
6000
homeowners
Registered US homeowners who submit roof information and review or supplement their quote requests.
8
contractor_staff
The contractor’s internal team members who review requests and make quote decisions; the description explicitly states there are eight staff members.
1000
anonymous_visitors
Unregistered visitors who can view the public quote entry point before submitting or creating an account.
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
Multiple integrations and public branded quoting
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
48.97 MB
not purchased
(plan/kit 200.00 MB)
Covered by plan/kit (margin ~308%)
$ 0
Object storage
add-on
x 1
3.10 GB
100GB × 1/year, amortized monthly
(plan/kit 2.00 GB + add-on +1.10 GB)
Add-on fills +1.10 GB gap beyond plan/kit
$ 2.92
Outbound data transfer add-on
x 1
3.73 GB/mo
500GB × 1/year, amortized monthly
(plan/kit 2.00 GB/mo + add-on +1.73 GB/mo)
Add-on fills +1.73 GB/mo gap beyond plan/kit
$ 2.92
AI points add-on
x 3
13.3M points
6,000,000 AI Points × 3/month
(plan/kit 19.0M points + add-on +12.3M points)
Add-on fills +12.3M points gap beyond plan/kit
$ 30
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 $74.83/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
0.05 GB-month
RDS gp3 storage → 0.01
3.10 GB-month
S3 Standard → 0.07
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $330.33 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL (INFRA + OPS, EXCL. AI USAGE)
Traditional outsourcing / build in-house
about $85,000 - $140,000
≈ $330
Based on the AWS estimate above
≈ $2,330
Infra $330 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $12,000 - $20,000
≈ $330
Based on the AWS estimate above
≈ $1,830
Infra $330 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $5,000 - $9,000
≈ $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
$45
All-in: egress / storage / auto-scaling; excludes AI usage
$45
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 ≈ $330.33/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 $30/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 approximately 6,000 homeowner accounts, anchored to the stated quote volume of about 25 requests per day over the standard operating horizon, plus the explicitly stated eight contractor staff accounts. This produces a registered-account planning scale of 6,008; if many homeowners submit repeated quotes or most requests remain unregistered, the customer-account assumption can be adjusted.
We assume a standard linear-growth operating pattern because an online residential roofing quote service typically builds its customer and quote history progressively rather than launching with a full pre-existing dataset. If the contractor imports a complete historical quote archive at launch, this parameter can be adjusted upward.
Public Quote Entry Browsing
Routine Quote Review and Follow-up
Public Quote Entry Browsing
Routine Quote Review and Follow-up
Daily Homeowner Quote Submission
30
times/month
0.01 req/s
Peak load
We assume anonymous visitors browse the public quote entry point during a broad daytime operating period because visitors are not pulled in by a synchronized external signal. The duration represents a normal eight-hour service day and the monthly frequency represents daily operation. This scenario has one purpose, public entry browsing, because no quote data is written before a visitor proceeds to the homeowner experience. If the contractor operates only on weekdays, the monthly frequency can be reduced to the actual operating calendar.
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 homeowner opens the quote form, submits address and photos, and later reviews the processed result.
Public Quote Entry Page
users
roof_quote_inspector
submit_quote_flow
A homeowner reviews a request for more information and uploads additional roof photos.
Public Quote Entry Page
users
A contractor staff member filters the dashboard, opens a request, and records the appropriate decision.
Public Quote Entry Page
users
An anonymous visitor opens the public quote entry page and reviews the available starting point for an online quote.
Public Quote Entry Page
page
data_table
ai_agent
action_flow
Public Quote Entry Page
Public entry point that explains the online quote request and directs visitors to begin the submission process.
Homeowner Quote Page
Form where a homeowner enters the roof address, uploads roof photos, submits a quote request, and later reviews the returned estimate.
Staff Quote Dashboard
Staff workspace for filtering quote requests, reviewing photos and AI findings, and accepting, declining, or requesting more photos.
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 primarily drives RoofSnap’s resource usage?
Why are roof photos modeled as separate records?
What happens when staff need more information?
Which integrations are required?
Copy to discuss with AI
Challenge & re-estimate
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