Using
Momen · full-stack no-code platform
to build
a test-plan workspace for QA teams
PROJECT OVERVIEW
What this app does
This product is a focused workspace for QA teams to create and maintain test plans. It stores plan objectives, scope, environment details, review status, and the individual test cases associated with each plan so that authors can prepare plans and reviewers can inspect them.
As a QA author, I can create and maintain a test plan with its scope, objectives, environment, and test cases
As a QA reviewer, I can open a test plan and update its review status
The following user counts are projected totals after one year of growth
10
qa_reviewers
QA leads or designated reviewers who inspect plans and record their review status.
40
qa_authors
QA practitioners who create and maintain test plans and their individual test cases.
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
Ongoing test-plan management requires at least the BASIC plan.
See the capability-by-capability assessment ▾
ALLOWANCE PROVIDED (COMPOSITION)
not purchased
(plan/kit 1.0M points)
Outbound data transfer add-on
x 0
not purchased
(plan/kit 2.00 GB/mo)
not purchased
(plan/kit 2.00 GB)
Database storage add-on
x 0
not purchased
(plan/kit 200.00 MB)
Covered by plan/kit (margin ~643%)
not purchased
(plan/kit 5 req/s)
Covered by plan/kit (margin ~400%)
Basic is the minimum viable plan
See minimum viable plan above
Monthly total
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 $39/mo all-in above.
0.00 GB (first 100GB free)
Internet egress → 0
0.00 GB-month
S3 Standard → 0
0.03 GB-month
RDS gp3 storage → 0
2 × db.m6g.large (Multi-AZ) × 730h
RDS PostgreSQL db.m6g.large → 232.14
2 × t4g.large × 730h
EC2 t4g.large → 98.11
Total ≈ $330.26 / mo
Pure cloud resources
MONTHLY CLOUD INFRA ($/MO)
MONTHLY TOTAL (INFRA + OPS, EXCL. AI USAGE)
Traditional outsourcing / build in-house
≈ $330
Based on the AWS estimate above
≈ $2,330
Infra $330 + ops ~$2,000
≈ $330
Based on the AWS estimate above
≈ $1,830
Infra $330 + ops ~$1,500
≈ $330
Based on the AWS estimate above
≈ $1,830
Infra $330 + ops ~$1,500
Off-the-shelf SaaS / vertical solution
N/A
Priced per seat, not by cloud infra
≈ $16,000+
Per-seat pricing, tens of thousands of users
$39
All-in: egress / storage / auto-scaling; excludes AI usage
Infrastructure cost is unavoidable
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 ≈ $330.26/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 $0/mo all-in and removes the need for an ops team.
Vibe-coding speed + a production-grade backend
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
50
Total users
We assume 50 registered accounts based on the internal-tool category anchor because the input gives neither an explicit user figure nor a throughput signal. These accounts represent QA authors and reviewers within one organization rather than anonymous public visitors. If the product is intended as an outward B2B SaaS product, the registered scale can be adjusted.
225
Data retention period
We assume this is a standard linear-growth internal productivity tool because no existing dataset, launch event, or recent burst of usage is described. If the product launches with a full pre-existing test library, this parameter can be adjusted toward a full-year assumption.
We assume the workspace is used as an internal productivity tool during ordinary weekday work rather than in a synchronized, externally triggered rush. Authoring and review are distinct purposes, so they are represented separately with their own group sizes and core actions. An eight-hour workday is a reasonable period over which these activities are distributed, and weekdays-only operation is represented as 22 occurrences per month. If the team has a fixed release-day surge or a different work schedule, the scenario timing and frequency can be adjusted.
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.
Peak Concurrency
0.01 req/s
Outbound data transfer
0 GB
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
review_test_plan
A reviewer finds a plan, opens its details and cases, and records the review status.
author_create_test_plan
An author opens the plan workspace, reviews existing plans, creates or edits a plan, and records its test cases.
Test Plan Review Page
Review page where QA reviewers inspect test plans and record review status.
Test Plan Workspace
Workspace where QA authors find, create, edit, and populate test plans.
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
What will affect ongoing resource usage?
How was the registered user scale estimated?
What functionality is included in this test-plan product?
Made and hosted in the United States. 🇺🇸
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