a Bay Area junk-removal booking app
What this app does
BayHaul Junk Removal is an online request and scheduling 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 each request in a dashboard, see the calculated warehouse-to-customer driving distance and time, and accept or decline the job with an optional note; customers can edit and resubmit declined requests.
As an anonymous visitor, I can view BayHaul's online junk-removal request entry point.
As a customer, I can describe my junk and upload photos for an AI-generated price range and detected-item list.
As a customer, I can review my estimated price and detected items before choosing a pickup time and address.
As a customer, I can choose a pickup date, one of the nine fixed one-hour time slots, and an address for my request.
As a customer, I can view the request status, driving distance and time, and any staff note.
As a customer, I can edit and resubmit a declined request for another review.
As a staff member, I can view incoming junk-removal requests and their item, pricing, scheduling, and route details.
As a staff member, I can accept or decline a request and optionally add a note.
1000
anonymous_visitors
People who visit BayHaul's public online request entry point without a registered account.
4500
customers
Registered Bay Area customers who submit junk details, receive estimates, schedule pickups, and resubmit declined requests.
10
staff
BayHaul's internal team members who review, accept, or decline customer requests.
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
External branded service with multiple integrations
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 1
239.82 MB
10GB × 1/year, amortized monthly
(plan/kit 200.00 MB + add-on +39.82 MB)
Add-on fills +39.82 MB gap beyond plan/kit
$ 8.33
Object storage
add-on
x 1
10.04 GB
100GB × 1/year, amortized monthly
(plan/kit 2.00 GB + add-on +8.04 GB)
Add-on fills +8.04 GB gap beyond plan/kit
$ 2.92
Outbound data transfer add-on
x 1
8.66 GB/mo
500GB × 1/year, amortized monthly
(plan/kit 2.00 GB/mo + add-on +6.66 GB/mo)
Add-on fills +6.66 GB/mo gap beyond plan/kit
$ 2.92
AI points add-on
x 7
42.2M points
6,000,000 AI Points × 7/month
(plan/kit 43.0M points + add-on +41.2M points)
Add-on fills +41.2M points gap beyond plan/kit
$ 70
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 $123.17/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
0.24 GB-month
RDS gp3 storage → 0.03
10.04 GB-month
S3 Standard → 0.23
0.00 GB (first 100GB free)
Internet egress → 0
Total ≈ $330.51 / mo
Pure cloud resources
DEVELOPMENT COST
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL (INFRA + OPS, EXCL. AI USAGE)
Traditional outsourcing / build in-house
about $90,000 - $150,000
≈ $331
Based on the AWS estimate above
≈ $2,331
Infra $331 + ops ~$2,000
4-6 months
5 / 5
Vibe Coding
about $12,000 - $20,000
≈ $331
Based on the AWS estimate above
≈ $1,831
Infra $331 + ops ~$1,500
hours
5 / 5
AI full-stack generation
about $7,000 - $11,000
≈ $331
Based on the AWS estimate above
≈ $1,831
Infra $331 + 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
$53
All-in: egress / storage / auto-scaling; excludes AI usage
$53
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 ≈ $330.51/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.
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 registered customer pool is grounded in the stated throughput of 20 orders per day across the effective operating period, with 10 additional internal staff accounts. This represents primarily distinct one-off service customers rather than a large repeat-subscription base; if customers commonly place repeated requests, the customer pool should be reduced accordingly.
We assume 225 equivalent operating days because a local junk-removal booking service is a standard linear-growth SaaS operation that builds its customer and request history progressively. If your actual business launches with a full pre-existing dataset or experiences a rapid early burst followed by a plateau, this parameter can be adjusted accordingly.
Public Entry Browsing
Customer Request Processing
Public Entry Browsing
Customer Request Processing
Customer Request Processing
30
times/month
0.04 req/s
Peak load
We assume anonymous visitors arrive throughout a broad daytime and evening availability period because the public entry page is accessible continuously and is not driven by a synchronized external signal. If the service advertises the page only during a narrower campaign window, the duration can be shortened accordingly. We use a long routine window and daily recurrence because public entry browsing can occur on every operating day, and this scene contains one purpose only: loading the public request-entry page.
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
Anonymous visitor opens the public BayHaul entry page before starting a request.
BayHaul Landing Page
Customer submits junk information, reviews the AI estimate, and schedules a pickup.
BayHaul Landing Page
users
junk_quote_analyzer
analyze_junk_request
Customer reviews a declined request, edits the scheduling or address information, and resubmits it for route recalculation.
BayHaul Landing Page
users
analyze_junk_request
Staff opens the dashboard, reviews a request and its photos, then accepts or declines it with an optional note.
BayHaul Landing Page
users
analyze_junk_request
page
data_table
ai_agent
action_flow
BayHaul Landing Page
Public entry page that introduces the online junk-removal request path and directs visitors to begin.
Junk Quote Page
Customer page for entering a junk description, uploading photos, and reviewing the AI-generated estimate and detected items.
Pickup Scheduling Page
Customer page for selecting a pickup date, one of nine fixed one-hour slots, and a service address, including resubmission of declined requests.
Staff Request Dashboard
Staff dashboard for reviewing incoming requests, route information, customer details, and making an accept or decline decision with an optional note.
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 the resource usage for this BayHaul system?
Are the nine pickup slots modeled as separate records?
Why are request photos and detected items separate tables?
What happens when a request is declined?
Copy to discuss with AI
Challenge & re-estimate
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