an online junk removal quoting app for Bay Area customers
What this app does
BayHaul Junk Removal is an online request and quoting service for customers in the Bay Area. Customers describe their junk, upload photos, receive an AI-generated price range and detected-item list, then choose a pickup date, a fixed one-hour time slot, and an address. BayHaul staff review requests in a dashboard, see the calculated warehouse-to-customer driving distance and time, and accept or decline each request; declined requests can be edited and resubmitted for recalculation.
As a customer, I can describe my junk and upload photos to request an AI-generated price range and detected-item list
As a customer, I can review my generated quote and choose a pickup date, one-hour time slot, and address
As a customer, I can edit and resubmit a declined request for a new quote and driving estimate
As a staff member, I can review incoming requests with their quote, detected items, address, and driving distance and time
As a staff member, I can accept or decline a request and optionally record a note
50000
customers
Bay Area customers who submit junk details and photos, review quotes, schedule pickups, and resubmit declined requests.
1000
staff
BayHaul team members who review requests, inspect quote evidence and route estimates, and accept or decline jobs.
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 branded public service
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 1
31 req/s
single-tenant kit × 1
(plan/kit 155 req/s)
Covered by plan/kit (margin ~400%)
¥ 120
Database storage add-on
x 0
1.89 GB
not purchased
(plan/kit 2.20 GB)
Covered by plan/kit (margin ~16%)
¥ 0
Object storage
add-on
x 1
124.31 GB
100GB × 1/year, amortized monthly
(plan/kit 52.00 GB + add-on +72.31 GB)
Add-on fills +72.31 GB gap beyond plan/kit
¥ 2.92
Outbound data transfer add-on
x 0
22.58 GB/mo
not purchased
(plan/kit 52.00 GB/mo)
Covered by plan/kit (margin ~130%)
¥ 0
AI points add-on
x 103
616.2M points
6,000,000 AI Points × 103/month
(plan/kit 619.0M points + add-on +615.2M points)
Add-on fills +615.2M points gap beyond plan/kit
¥ 1030
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 $1191.92/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
1.89 GB-month
RDS gp3 storage → 0.22
124.31 GB-month
S3 Standard → 2.86
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $333.33 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL (INFRA + OPS, EXCL. AI USAGE)
Traditional outsourcing / build in-house
about $90,000 - $140,000
≈ $333
Based on the AWS estimate above
≈ $2,333
Infra $333 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $12,000 - $20,000
≈ $333
Based on the AWS estimate above
≈ $1,833
Infra $333 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $6,000 - $10,000
≈ $333
Based on the AWS estimate above
≈ $1,833
Infra $333 + 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
$162
All-in: egress / storage / auto-scaling; excludes AI usage
$162
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 ≈ $333.33/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 $1030/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 customer or staff scale. This is the standard planning assumption for an unspecified product scale and can be adjusted when BayHaul provides its expected account population.
We assume this is a standard linear-growth service SaaS because customer requests and staff operations should build gradually as the service gains adoption. If the company launches with a full pre-existing dataset or experiences an early burst followed by a plateau, this parameter can be adjusted accordingly.
New Request Submission Peak
Quote Review and Scheduling Routine
Quote Review and Scheduling Routine
Declined Request Recovery
New Request Submission Peak
30
times/month
30.67 req/s
Peak load
We assume new quote requests form a short recurring activity window because customers commonly submit requests after deciding to arrange removal, while the business does not impose a synchronized external event. The 120-second duration represents a concentrated but not externally synchronized submission period, and the monthly frequency reflects repeated daily customer intake. This scene contains one purpose only: submitting a new request, because the submission flow's preliminary form action and its asynchronous quote-generation completion belong to the same customer objective.
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
Customer submits junk information for an AI quote, reviews the result, and selects pickup timing.
Junk Request Page
customers
junk_quote_analyzer
create_quote_request_flow
Customer reviews a declined request, edits its inputs, and resubmits it for refreshed analysis and routing.
Junk Request Page
customers
junk_quote_analyzer
create_quote_request_flow
Staff review the request queue, inspect one request's evidence and route estimate, and accept or decline it.
Junk Request Page
customers
page
data_table
ai_agent
action_flow
Junk Request Page
Customer form for describing junk, uploading photos, and entering a pickup address.
Quote Review and Scheduling Page
Customer page showing the generated price range and detected items and collecting the pickup date and fixed one-hour time slot.
Declined Request Edit Page
Customer page for reviewing a declined request, editing its junk description, photos, or address, and resubmitting it.
Staff Request Dashboard
Staff dashboard for reviewing requests, quote details, detected items, route estimates, and decision notes.
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 drives resource usage for this junk removal app?
Which features create persistent business data?
Why is photo storage and delivery important?
What happens when staff decline a request?
Copy to discuss with AI
Challenge & re-estimate
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The latest news, cases, and resources
