a junk-removal request app for Bay Area customers
Estimated monthly cost at the usage scale below — billed by actual usage
0.83 req/s
Peak Concurrency
2.13 GB
Database Storage
116.61 GB
Object Storage
34.03 GB
Outbound data transfer
What is this app
BayHaul Junk Removal is an online request system for Bay Area customers who need junk hauled away. Customers describe their junk, upload photos, receive an AI-generated price range and structured detected-item list, then choose a pickup date, one of nine fixed one-hour slots, and an address. The system calculates the warehouse-to-customer driving distance and time before BayHaul staff review each request and accept or decline it, with declined requests available for customer editing and resubmission.
As an anonymous visitor, I can view the public junk-removal request entry point so that I can decide whether to start a request
As a customer, I can describe my junk and upload photos to request an online quote
As a customer, I can receive a price range and structured list of detected junk items from my description and photos
As a customer, I can choose a pickup date, one fixed one-hour time slot, and an address and submit the pickup request
As a customer, I can review my request status and edit and resubmit a declined request
As a staff member, I can review request details, photos, detected items, and route estimates and accept or decline a request with an optional note
0
anonymous_visitors
People who visit BayHaul's public request entry point before registering or submitting a request.
45000
customers
People who describe junk, upload photos, receive quotes, schedule pickups, and revise declined requests.
5000
staff_members
BayHaul team members who review requests and accept or decline them with optional notes.
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 integrations require expanded capacity.
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
2.28 GB
10GB × 1/year, amortized monthly
(plan/kit 200.00 MB + add-on +2.08 GB)
Add-on fills +2.08 GB gap beyond plan/kit
8.33
Object storage
add-on
125.21 GB
100GB × 2/year, amortized monthly
(plan/kit 2.00 GB + add-on +123.21 GB)
Add-on fills +123.21 GB gap beyond plan/kit
5.83
Outbound data transfer add-on
36.54 GB/mo
500GB × 1/year, amortized monthly
(plan/kit 2.00 GB/mo + add-on +34.54 GB/mo)
Add-on fills +34.54 GB/mo gap beyond plan/kit
2.92
AI points add-on
405.1M points
6,000,000 AI Points × 68/month
(plan/kit 409.0M points + add-on +404.1M points)
Add-on fills +404.1M points gap beyond plan/kit
680
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
2.28 GB-month
RDS gp3 storage → 0.26
125.21 GB-month
S3 Standard → 2.88
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $333.39 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL
(INFRA + OPS)
Traditional outsourcing / build in-house
about $145,000 - $190,000
≈ $333
Based on the AWS estimate above
≈ $2,333
Infra $333 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $16,000 - $24,000
≈ $333
Based on the AWS estimate above
≈ $1,833
Infra $333 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $7,000 - $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
$56
All-in: egress / storage / auto-scaling; excludes AI usage
$56
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 gives no explicit customer or staff scale, and this is the standard planning assumption for an unspecified online service. If BayHaul has a known service-area population, customer list, or staff account count, this parameter can be adjusted accordingly.
We assume this is a standard linear-growth service-request product because customer requests and staff review activity should build progressively as the service gains adoption. If BayHaul launches with a full pre-existing dataset or experiences an early burst followed by a stable ceiling, this parameter can be adjusted.
Daily Quote Intake
Customer Request Processing
Daily Quote Intake
Customer Request Processing
Daily Quote Intake
30
times/month
0.83 req/s
Peak load
We assume this is a broad daytime customer-acquisition period rather than a synchronized event, because customers discover the service and submit junk details at different times. The public browsing and quote-submission purposes are separated so anonymous entry activity is not blended with the customer write flow. A 7,200-second window represents a typical active service period, and 30 monthly occurrences represent daily operation.
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 visitor views the public request entry point.
Customer submits junk details and receives an AI-generated price range and item list.
Customer schedules a pickup and submits the request for BayHaul review.
Customer reviews submitted requests, quotes, decisions, and attached photos.
Customer edits and resubmits a declined request.
Staff review and decide whether to accept a pickup request.
page
data_table
ai_agent
action_flow
Public Request Start Page
Public entry point where prospective customers begin an online junk-removal request.
Quote Request Page
Customer form for describing junk, uploading photos, and receiving or refreshing an AI-assisted quote.
Pickup Scheduling Page
Page where a customer selects the pickup date, one of nine fixed one-hour slots, and the pickup address.
Customer Request History Page
Customer-facing history and detail view for tracking quotes, pickup requests, decisions, and resubmission access.
Staff Request Dashboard
Internal dashboard for reviewing, accepting, or declining customer pickup requests.
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 resource usage for BayHaul Junk Removal?
What functionality is included in the estimate?
Which assumptions are most sensitive to actual operations?
When are AI and external services used?
Copy to discuss with AI
Challenge & re-estimate