Public example Fictional project scenario
A complete blueprint example for an AI SaaS MVP
This fictional scenario follows a two-person team with intermediate web development experience building a web-first generative AI subscription product for US users. The plan supports email and password plus Google sign-in, targets the first paying user within three months, and keeps initial operational work low.
Two components — OpenAI usage and the final Vercel seat count — depend on decisions that are not yet confirmed.
01 / Executive decision summary
The plan in one screen.
What is being built?
A web-first subscription product for US users with account access, a generative AI workflow, product data, transactional messages and a measurable path to payment.
Primary optimization target
Reach the first paying user within three months while keeping operational work low enough for two people to own.
Recommended architecture
Use Next.js for the application layer, Clerk for identity, Supabase for product data and storage, OpenAI API for server-side model calls, Lemon Squeezy for subscriptions and entitlement webhooks, Resend for transactional email, PostHog for event evidence, and Vercel for build and deployment. Keep identity, application data and paid entitlement as separate responsibilities even though the user experiences one product.
Resolved launch cost
At launch this stack has one verified fixed cost: Vercel Pro at $20/month per developer seat. Every other component sits inside a free allowance. AI usage and payment fees are variable and not yet resolved.
Three decisions that shape the build
- Separate identity from application data.Clerk owns users and sessions; Supabase owns product records and storage.
- Keep model calls server-side.OpenAI credentials, usage controls and application context stay outside the browser.
- Use a merchant-of-record payment boundary.Lemon Squeezy reduces the tax operation the two-person team must own before launch.
Three largest risks
- Identity records, authorization rules and user-owned data can drift if their boundary is not defined first.
- AI unit cost cannot be assessed until model choice and request volume are confirmed.
- Payment webhooks can create incorrect access if entitlement updates are not idempotent and auditable.
Define one paid end-to-end user journey, its acceptance criteria and its required data events before implementation begins.
02 / Project brief and assumptions
The conditions behind the recommendation.
If any item marked “Needs confirmation” changes materially, the stack or its cost interpretation may also change.
The delivery team has two people.
ProvidedBoth team members have intermediate web development experience.
ProvidedThe first product is web-first.
ProvidedUsers need email and password sign-in plus Google sign-in.
ProvidedThe business model uses subscription payments.
ProvidedThe product includes a generative AI feature.
ProvidedThe target is the first paying user within three months.
ProvidedThe launch architecture should keep operational work low.
ProvidedInfrastructure decisions should serve users in the US market.
ProvidedManaged product services are preferred over operating equivalent infrastructure.
InferredThe first release should optimize one paid user journey before adding breadth.
InferredExpected monthly active users at launch.
Needs confirmationExpected AI model choice, requests, input volume and output volume.
Needs confirmationThe number of developer seats required for commercial deployment.
Needs confirmationExplicitly excluded requirements
Not part of this recommendation
- Native mobile applications
- Multilingual product support
- Enterprise SSO
- HIPAA or SOC 2 compliance scope
- Custom model training
- Multi-region active-active failover
03 / Architecture map
Eight responsibilities, one delivery path.
The arrows show the blueprint’s decision sequence. Runtime integrations remain explicit in the text below the diagram.
Accessible architecture description
Next.js holds the interface, application routes and server-side coordination. Clerk authenticates the user and provides a stable identity to protected application routes. Supabase stores application records and files after authorization is checked. OpenAI API receives model requests only from server-side application code.
Lemon Squeezy runs subscription checkout and sends payment-state webhooks that the application converts into entitlements. Resend sends account and payment-triggered messages. PostHog records the defined activation and payment events. Vercel builds, previews and deploys the application layer.
04 / Recommended stack
Each tool has one accountable role.
Costs below use the verified records reviewed on 10 September 2026. A free allowance is not treated as a guarantee that the project will remain free.
Next.js
- Recommended for
- A web-first product that needs interface routes and server-side application logic in one project.
- Why it fits
- The two-person team can keep the first paid user journey in one codebase instead of coordinating separate application and API projects during the three-month launch window.
- Watch for
- Routing, caching and rendering behavior can change across framework upgrades; server and client boundaries still need explicit ownership.
- Cost in this scenario
- No framework fee; MIT licensed. Deployment is costed separately under Vercel.
Clerk
- Recommended for
- Email and password sign-in plus Google sign-in without building account interfaces and session handling from scratch.
- Why it fits
- Authentication is required at launch, and a dedicated identity layer reduces the amount of security-sensitive interface work assigned to a two-person team.
- Watch for
- The Hobby plan has no MFA and no passkeys, and session lifetime is fixed at 7 days. Application authorization still has to be designed separately.
- Cost in this scenario
- $0 while under 50,000 MRU per application. Hobby includes unlimited applications and 3 dashboard seats.
Supabase
- Recommended for
- Product records, user-owned content and file storage behind the authenticated application.
- Why it fits
- A managed data layer supports the low-operations constraint while preserving a clear boundary: Clerk owns identity and Supabase owns application data and storage.
- Watch for
- Database design and row-level security still require backend expertise. Several backend concerns also become coupled to one provider.
- Cost in this scenario
- $0 while within 50,000 MAU, 500 MB database, 5 GB egress, 5 GB cached egress, 1 GB file storage, and 2 active projects. Free projects are paused after 1 week of inactivity.
OpenAI API
- Recommended for
- Server-side model calls for the product feature that produces generative output.
- Why it fits
- The scenario requires a generative AI feature but does not justify operating model infrastructure before the first paid user. Calls stay server-side so credentials and usage controls do not reach the browser.
- Watch for
- Cost varies with model choice, context size, output length and tools. Model behavior and available capabilities can change between releases.
- Cost in this scenario
- Usage-based with no fixed monthly fee. Actual project cost is not verified because model choice and token volume need confirmation.
Lemon Squeezy
- Recommended for
- Subscription checkout for a US-facing digital product with a small team and no internal tax operation.
- Why it fits
- The three-month revenue target requires a production payment path, while the two-person team benefits from placing checkout and sales-tax handling behind a merchant-of-record boundary.
- Watch for
- Merchant-of-record convenience comes with platform and conditional processing fees. Payout timing and account review remain platform dependencies.
- Cost in this scenario
- No monthly fee. 5% + $0.50 per transaction, plus +0.5% for subscription payments. Additional geographic, payment and payout fees can apply.
Resend
- Recommended for
- Verification, password recovery, receipt and account-state messages triggered by the application.
- Why it fits
- Email and password sign-in plus subscription events create transactional messages from the first release; an API-based delivery layer keeps that work inside the existing application flow.
- Watch for
- Daily limits can constrain testing and early production traffic on the free tier. Deliverability still depends on domain configuration and sending practices.
- Cost in this scenario
- $0 while within 3,000 emails per month, capped at 100 per day, 3 domains, and 30 days of data retention.
PostHog
- Recommended for
- Measuring whether users complete the activation and payment journey before the team expands scope.
- Why it fits
- The launch target is a paying user rather than feature volume. A small event plan gives the team evidence about the one journey it is optimizing within three months.
- Watch for
- Identified events cost roughly 4x anonymous events. Combining several product functions can also increase implementation scope before the event taxonomy is stable.
- Cost in this scenario
- $0 while within 1M analytics events, 5,000 session recordings, 1M feature flag requests, and 1,500 survey responses per month.
Vercel
- Recommended for
- Managed previews and production delivery for the web-first application.
- Why it fits
- The team can ship and review application changes without adding a separate server operation track during the first three months.
- Watch for
- Usage is measured across several infrastructure dimensions, and the framework integration increases platform dependence.
- Cost in this scenario
- $20/month per developer seat on Pro, including $20 in usage credits per seat. Hobby is for personal, non-commercial use only; final seat count needs confirmation.
05 / One decision, fully shown
Supabase vs Firebase for this project
This is a scenario decision, not a general product ranking. The result follows from the stated data shape, team size, delivery window and identity boundary.
For this two-person, web-first MVP, Supabase is the better fit because the core product will likely connect accounts, workspaces, subscriptions, generated outputs and usage records—data with durable relationships. Its relational model and policy layer fit that shape. Firebase remains credible for offline-first or client-synchronized products, but those are not requirements here.
Why this decision matters in this scenario
The data layer is chosen before authentication claims, entitlement state, AI request records and analytics events are connected. A poor fit here would surface in nearly every later step. The three-month launch target also means the team needs a model it can inspect and change without maintaining parallel copies of account, subscription and usage facts.
| Criterion | Supabase | Firebase | Decision for this project |
|---|---|---|---|
| Data model | Relational tables make accounts, workspaces, subscriptions, generated outputs and usage records explicit entities with enforceable relationships. | A document model keeps related data in collections and documents; cross-entity access patterns usually need to be designed into the document shape. | SupabaseThe expected product records have durable relationships, so a relational model reduces duplicated ownership and entitlement data. |
| Query flexibility | Relational queries can combine product, account, entitlement and usage records without copying the same facts into several documents. | Document queries work well when access patterns are known early, but broader reporting can require denormalized records or additional read paths. | SupabaseThe first release still needs room to inspect activation, payment state and AI usage together as the product model changes. |
| Identity boundary | The selected Clerk session token can be accepted through Supabase third-party authentication, then its claims can be used in row-level access policies. | An external identity layer can be connected, but that authorization path was not validated for this blueprint and would need its own implementation decision. | SupabaseThe Clerk-to-data authorization path is documented for the selected stack and keeps identity separate from application records. |
| Portability and lock-in | The core data model remains relational and can be exported in a form that preserves tables and relationships. | Application code and access rules depend more directly on document APIs, collection structure and provider-specific security rules. | SupabaseA two-person team cannot eliminate migration work, but it can avoid making the first data model inseparable from document-specific access patterns. |
| Local development | A local environment can reproduce the database and policy layer used by the application. | Its emulator suite supports local work across several connected services and is useful for prototyping and integration tests. | FirebaseFirebase has the clearer advantage when broad local service emulation is a primary requirement; it is not a stated requirement for this web-first MVP. |
| Team learning path | The team must understand relational schema design, migrations and row-level policies even though the service is managed. | The team must learn document modeling, security rules and how to avoid reads or duplicated data caused by a poor collection design. | SupabaseThe scenario expects changing relationships around users, subscriptions and outputs; making those relationships visible is easier to review during a short launch window. |
| Migration difficulty | A future move still requires application and policy changes, but the relational schema provides a direct inventory of tables, constraints and relationships. | A move can require reshaping denormalized documents, replacing security rules and rewriting provider-specific data access. | SupabaseThe relational inventory creates a more inspectable exit path if the MVP becomes a longer-lived product. |
Firebase pricing has not been independently verified for this blueprint, so this comparison covers architecture and fit rather than cost.
A different product shape can reverse the decision.
- The primary experience is offline-first or mobile-first, and synchronized client state during intermittent connectivity is a defining requirement.
- The application has stable, document-shaped access patterns with few cross-entity reporting or entitlement queries.
- The team already has production experience with Firebase data modeling, security rules and emulator-based testing, but limited relational database experience.
Change the choice when the assumptions change.
- A native mobile or offline-first product becomes part of the committed launch scope.
- Real usage proves that the core records behave as independent documents rather than related business entities.
- The team’s operating expertise changes enough to make one data model materially safer to own.
- Migration, residency or control requirements become explicit and are tested against both architectures.
06 / Alternatives considered
What was evaluated and left out.
A rejected option is not a rejected product. It means the option created a weaker fit against this project’s current assumptions.
These alternatives are listed as they were evaluated for this scenario. Their pricing and product details have not been independently verified for this blueprint.
Selected · Clerk
Supabase Auth
- Why it was considered
- It could place identity beside the selected data and storage layer and remove one provider boundary.
- Why it was not selected for this scenario
- Clerk was selected because the launch requires both email and password plus Google sign-in, and the two-person team values prebuilt account flows more than minimizing provider count; the separate data-authorization setup is accepted explicitly.
Auth0
- Why it was considered
- It was considered as a dedicated identity layer with room for more complex organizational access later.
- Why it was not selected for this scenario
- Enterprise SSO is explicitly excluded, so that additional identity scope is not required for the three-month MVP.
Selected · Supabase
Firebase
- Why it was considered
- Its managed document model and client synchronization are credible for products shaped around document reads and realtime client state.
- Why it was not selected for this scenario
- This product is expected to join accounts, workspaces, entitlements, generated outputs and usage records, making a relational model the clearer fit for the stated launch scope.
Appwrite
- Why it was considered
- It was considered as a combined application backend with identity, data and storage responsibilities.
- Why it was not selected for this scenario
- The scenario prioritizes low operational work and does not require a self-managed backend path, while the selected stack already assigns those responsibilities explicitly.
Selected · Lemon Squeezy
Paddle
- Why it was considered
- Its merchant-of-record structure matches the need to reduce the team’s cross-border tax operation.
- Why it was not selected for this scenario
- Its product and payout details were not independently verified for this blueprint, while the selected option has a dated pricing and payout source record for this scenario.
Stripe
- Why it was considered
- It was considered for direct control over checkout, subscriptions and payment infrastructure.
- Why it was not selected for this scenario
- Direct payment infrastructure leaves more merchant and tax operating responsibility with the founder, which conflicts with the low-operations constraint before the first paid user.
Selected · PostHog
Mixpanel
- Why it was considered
- It was considered as a dedicated event-analysis product for activation and conversion questions.
- Why it was not selected for this scenario
- The verified PostHog record covers the event, replay and feature-control scope selected for one launch workflow; equivalent coverage was not verified for this alternative.
Amplitude
- Why it was considered
- It was considered for structured product analysis as the team learns where users leave the paid journey.
- Why it was not selected for this scenario
- The two-person team needs one small observation boundary at launch, and the selected option already covers the planned evidence without adding another evaluation track.
07 / Integration sequence
Build the connections in dependency order.
Each step closes one testable boundary before the next service depends on it. “Done” means an observable condition, not that code merely exists.
- 01
Repository and deployment pipeline
- Prerequisite
- A source repository exists and both team members can contribute to it.
- What you are doing
- Connect the repository to managed preview and production environments. Separate preview configuration from production secrets and require a successful build before release.
- Services connected
- Vercel
- Done when
- A change produces a preview deployment, the production branch produces a production deployment, and neither environment exposes a secret to the browser.
- Blocking risk
- Every later integration becomes harder to test safely if preview and production configuration are mixed.
- 02
Application shell and routing
- Prerequisite
- The deployment pipeline builds a minimal application successfully.
- What you are doing
- Create the public, account, application and server-route boundaries. Define which routes render public content and which will require an authenticated session.
- Services connected
- Next.js
- Done when
- All planned routes resolve, error states are visible, and protected placeholders cannot be reached through public navigation.
- Blocking risk
- Unclear route ownership spreads authentication and server-only logic across the interface in later steps.
- 03
Database schema and access policies
- Prerequisite
- The core user journey and its required records are written down.
- What you are doing
- Model accounts, workspaces, generated outputs, usage records and entitlements. Enable row-level security and begin with deny-by-default policies before connecting real identity claims.
- Services connected
- Supabase
- Done when
- Migrations can recreate the schema, row-level security is enabled on user-owned tables, and anonymous requests cannot read or write protected records.
- Blocking risk
- Adding policies after application code is written can expose records or force a late rewrite of every data query.
- 04
Authentication and protected routes
- Prerequisite
- Public and protected route boundaries exist.
- What you are doing
- Configure email and password plus Google sign-in, session handling, sign-out and protected-route checks. Keep interface state separate from server authorization.
- Done when
- A new user can sign in by both required methods, sign out, return with a valid session and receive a denied response when a protected server route has no session.
- Blocking risk
- A client-only redirect can look protected while the underlying server route remains callable without authorization.
- 05
Clerk ↔ Supabase boundary
- Prerequisite
- Clerk sessions work, the Supabase schema exists and row-level security is enabled.
- What you are doing
- Configure Clerk as a Supabase third-party authentication provider, pass the Clerk session token to the Supabase client, and write policies against the required claims through auth.jwt(). This is a separate integration step: installing both services does not make Supabase trust Clerk automatically. Do not use the deprecated custom JWT template path.
- Done when
- One signed-in user can create and read only their own records, a second user cannot read them, and the same denial holds for direct data requests.
- Blocking risk
- A missing role claim, an untrusted issuer or a policy built around the wrong user identifier blocks valid users—or exposes another user’s data.
- 06
Core user workflow, end to end
- Prerequisite
- Authenticated data access works for two isolated test users.
- What you are doing
- Build one complete journey from account entry to saved input, generated result placeholder and return visit. Use stable record states before connecting variable AI output.
- Done when
- A user can start, save, leave and resume the core workflow without manual data repair, while another user remains isolated.
- Blocking risk
- Connecting AI or billing before the core record lifecycle is stable makes failures difficult to attribute.
- 07
AI integration, server-side
- Prerequisite
- The core workflow can persist an input and a pending result without calling a model.
- What you are doing
- Add the model call behind a server route, validate input, cap accepted payloads, record request state and handle timeout or failure without losing the user’s work.
- Services connected
- OpenAI APINext.jsSupabase
- Done when
- Credentials never reach the browser, one accepted request produces a stored result, and a failed or repeated request ends in a recoverable recorded state.
- Blocking risk
- Unbounded input, output or retries can create unresolved usage cost and duplicate results before the team has a baseline.
- 08
Subscription checkout and entitlement webhooks
- Prerequisite
- A signed-in account and a stable entitlement record exist.
- What you are doing
- Create test checkout, attach the internal user reference, verify every webhook signature, persist a processed-event key, and update entitlement state with idempotent writes. Replaying the same event must not grant access twice or regress newer subscription state.
- Services connected
- Lemon SqueezyNext.jsSupabase
- Done when
- Test purchase, renewal, cancellation and failed-payment events produce the expected entitlement, and replaying each captured event leaves the final state unchanged.
- Blocking risk
- Unsigned or non-idempotent webhook handling can grant incorrect access, duplicate side effects or overwrite a newer subscription state.
- 09
Transactional email
- Prerequisite
- Authentication and payment events have stable names and account recipients.
- What you are doing
- Send only required account and subscription messages, separate transactional templates from application logic, and record delivery failures without blocking the underlying account update.
- Done when
- Each required event sends one message in testing, duplicate business events do not send duplicates, and a delivery failure can be inspected and retried.
- Blocking risk
- Tying account state changes to successful delivery can leave users paid or verified in one system but blocked in another. Resend's free tier is capped at 100 emails per day; if launch-day sign-ups exceed that cap, verification messages stop and a user can pay but still be unable to access the product.
- 10
Analytics events and production readiness
- Prerequisite
- The full sign-in, core workflow and payment path works in test environments.
- What you are doing
- Define a small event contract for entry, activation, AI success or failure, checkout start and paid entitlement. Remove sensitive payloads, verify production environment values and run the full journey once before release.
- Done when
- One test journey creates the expected ordered events without sensitive content, production health checks pass, and the team can identify the exact step where a failed journey stopped.
- Blocking risk
- Late or inconsistent instrumentation makes the three-month goal impossible to diagnose and can send private user content into event data.
08 / Build checklist
Release conditions, not activity.
These checks are intentionally read-only. Each item describes evidence the team should be able to observe before treating a phase as complete.
Foundation
5 checks- Production and preview environments use separate secrets.
- A new environment can be created from documented configuration without copying credentials from another environment.
- Every protected server route rejects a request with no valid session.
- Database migrations recreate the required schema and access policies from an empty project.
- Both team members can deploy, inspect an error and restore the last known working release.
Core product
6 checks- A user can create an account with email and password and can also sign in with Google.
- A second user cannot read or change the first user's records.
- A user can start the core workflow, leave, return and continue from the saved state.
- An accepted AI request produces one stored result linked to the correct user and input.
- A failed AI request ends in a recoverable state, not a lost input.
- Repeating the same submitted request does not create an unexplained duplicate result.
Monetization
6 checks- A test customer can complete subscription checkout from an authenticated account.
- Each payment webhook is rejected when its signature is invalid.
- Replaying a captured webhook leaves entitlement state unchanged.
- Renewal, cancellation and failed-payment events produce the expected access state.
- A successful payment can be traced from checkout reference to account and entitlement record.
- A payment-state update succeeds even when its transactional email cannot be delivered.
Observability
4 checks- One complete user journey produces the expected ordered activation, AI and checkout events.
- Analytics events contain no prompt text, generated output, credential or payment payload.
- The team can distinguish an AI timeout from an application error in production records.
- A failed journey can be located at a specific step without reproducing it from user memory.
Launch readiness
5 checks- Account, AI, payment and email failure states show a recoverable user action.
- The production domain passes sign-in, checkout and webhook return-path tests.
- A launch-day email-volume check confirms whether the 100-per-day free-tier cap is sufficient.
- The team has a named owner and response action for authentication, payment and AI incidents.
- A production smoke test completes the paid workflow without using a privileged staff account.
Post-launch
4 checks- The team reviews activation, AI failure and paid-conversion events on a fixed weekly cadence.
- Actual AI input, output and tool-call usage is recorded well enough to calculate unit cost.
- Usage against Supabase, Clerk, PostHog, Resend and Vercel limits is visible before a cap is reached.
- At least one person other than the original implementer can diagnose the identity-to-entitlement flow.
09 / Verified Cost Plan
Known fixed cost, visible unknowns.
The plan separates resolved fixed amounts from usage and transaction drivers. A free allowance is a current launch condition, not a long-term cost forecast.
(one developer seat)
have a resolved numerical cost
This is not a complete monthly total.
Verified fixed costs
Usage-sensitive costs
- OpenAI API
- Driven by input tokens, output tokens, selected model and tool calls.
- Supabase
- Driven by active users, database size, egress, cached egress and storage.
- PostHog
- Driven by product events and the other enabled product-data workloads.
Transaction costs
Lemon Squeezy charges 5% + $0.50 per transaction, plus +0.5% for subscription payments. Additional geographic, payment and payout fees can apply.
Excluded or unresolved
- Actual OpenAI project cost: model and volume are not confirmed.
- Final Vercel developer seat count.
- Tax treatment and foreign-exchange conversion.
Use measured production inputs; do not substitute a headline token price for a project total.
Monthly AI cost = input tokens × model input rate
+ output tokens × model output rate
+ tool call chargesGrowth-stage review thresholds
Review the paid plan before the database reaches 500 MB or the application reaches 50,000 MAU.
Review the paid plan before sending 3,000 emails per month or reaching the cap of 100 per day.
Review paid usage before product analytics reaches 1M events per month.
Review the paid plan before the application reaches 50,000 MRU.
10 / Risks and decision triggers
Decisions stay valid only while their assumptions do.
Each mitigation is paired with a trigger so the team knows when to reopen the decision instead of carrying it indefinitely.
Identity and data become coupled incorrectly
- Why it matters
- Clerk owns identity while Supabase policies enforce access. If the shared user identifier or claim contract drifts, valid users can be blocked or another user's records can be exposed.
- Mitigation
- Document the claim contract, deny by default and test two-user isolation against direct data requests.
- Revisit when
- The identity claim shape, authorization model or account hierarchy changes.
AI cost grows faster than revenue
- Why it matters
- Input length, output length, model choice, tool calls and retries all affect cost while the product's real usage pattern is still unknown.
- Mitigation
- Record usage by workflow, cap accepted input and retries, and calculate cost per completed user outcome before expanding the AI surface.
- Revisit when
- A model, prompt, context limit or product workflow changes—or cost per completed outcome exceeds its approved threshold.
OpenAI promotional pricing changes
- Why it matters
- The promotional price for GPT-5.6 Sol runs through at least 21 November 2026. After that, input could rise 25% and output 50%.
- Mitigation
- Do not base the business case on the promotional rate; recalculate unit economics against the regular published rate before that date.
- Revisit when
- 21 November 2026, or earlier if the selected model or published price changes.
Payment and entitlement state diverge
- Why it matters
- Webhook retries, out-of-order events or a failed write can leave a paid customer without access or retain access after cancellation.
- Mitigation
- Verify signatures, store processed event identifiers, make updates idempotent and keep an auditable subscription-state history.
- Revisit when
- A new subscription state, refund path, grace period or manual entitlement action is introduced.
Transactional email is delayed or capped
- Why it matters
- Authentication and payment access depend on messages reaching the user, while the Resend free tier is capped at 100 emails per day.
- Mitigation
- Authenticate the sending domain, monitor failures, keep account state independent of delivery and upgrade before planned volume reaches the daily cap.
- Revisit when
- Daily sending approaches 100, complaint or bounce behavior changes, or a launch campaign is scheduled.
Vercel usage grows on more than one dimension
- Why it matters
- Transfer, requests, function activity and developer seats can grow differently, so one headline allowance does not describe the full scaling boundary.
- Mitigation
- Track the dimensions used by the production workload and investigate the first sustained increase instead of waiting for a cap.
- Revisit when
- A server-heavy feature, large asset path, additional developer seat or material traffic source is added.
One founder becomes the integration bottleneck
- Why it matters
- A two-person team cannot respond reliably if only one person understands the identity, data, payment and entitlement path.
- Mitigation
- Pair on the boundary tests, record recovery steps and require the second person to diagnose a staged failure before launch.
- Revisit when
- One person becomes the sole reviewer or operator for two consecutive releases.
11 / What not to build yet
The current cost is greater than the current return.
These choices are not prohibited. They are deferred until observed product evidence makes their additional ownership worthwhile.
- 01
A separate microservice layer
It adds deployment and failure boundaries before one application workflow has justified them.
- 02
More than one analytics product
Two event systems create duplicate instrumentation and conflicting answers before the first event contract is stable.
- 03
Vector infrastructure without evidence
No stated requirement proves that retrieval or similarity search is part of the paid workflow.
- 04
Custom authentication
Owning credentials and session security adds risk without improving the stated email, password and Google sign-in requirement.
- 05
A complex event pipeline
The team first needs evidence that the core workflow and its small event contract are correct.
- 06
Multilingual or multi-region delivery
Neither requirement is in the launch brief, and both multiply testing and operational work.
12 / 30/60/90-day build path
Three periods, three observable exits.
Time does not close a phase. The exit criterion does.
Prove one complete workflow
- Goal
- Make one authenticated AI workflow work from entry to a saved, recoverable result.
- Deliverable
- Public application shell, both required sign-in methods, isolated user records and one server-side AI request path.
- Dependency
- The core user journey, its record states and the Clerk-to-Supabase authorization contract are agreed first.
- Exit criterion
- Two isolated test users can each sign in, submit, receive or recover from a result, leave and resume without accessing the other user's records.
Add billing, reliability and measurement
- Goal
- Turn the working product path into a measurable subscription path with inspectable failures.
- Deliverable
- Test checkout, idempotent entitlement webhooks, transactional messages and the minimal activation-to-payment event contract.
- Dependency
- The record lifecycle and server-side AI failure states from the first period are stable.
- Exit criterion
- A test purchase grants the expected access once, cancellation removes it as designed, duplicate events do not change the result and the full journey appears in ordered analytics events.
Run production and test scaling assumptions
- Goal
- Operate the real paid path and replace unknown cost and capacity assumptions with observed evidence.
- Deliverable
- Production smoke checks, incident ownership, usage monitoring, unit-cost calculation and a documented response for each critical boundary.
- Dependency
- The complete sign-in, AI, payment, email and analytics path passes in the production domain.
- Exit criterion
- A real user can complete the paid workflow, the team can calculate AI cost for that outcome, and either founder can locate and act on a staged failure.
13 / Source and verification ledger
What was checked, and what was not.
Official pricing pages support the numerical claims. Scenario-specific totals remain unresolved until real usage inputs exist.
| Claim | Source | Checked on | Status |
|---|---|---|---|
| Next.js framework fee and license | Official product site | 10 Sep 2026 | Verified |
| Supabase pricing and free allowance | Official pricing page | 10 Sep 2026 | Verified |
| Clerk pricing and free allowance | Official pricing page | 10 Sep 2026 | Verified |
| OpenAI published model rates | Official pricing page | 10 Sep 2026 | Verified |
| Lemon Squeezy transaction pricing | Official pricing page | 10 Sep 2026 | Verified |
| Vercel Pro seat pricing | Official pricing page | 10 Sep 2026 | Verified |
| PostHog free allowance and usage model | Official pricing page | 10 Sep 2026 | Verified |
| Resend pricing and free sending limits | Official pricing page | 10 Sep 2026 | Verified |
| OpenAI project cost | — | — | Not verifiedModel and volume need confirmation. |
| Firebase comparison | Architecture only | — | Not verifiedPricing not verified. |
Every numerical price on this page traces to a vendor's own pricing page, reviewed on the date shown.
14 / What this example does not know
The unresolved inputs are part of the answer.
This blueprint does not turn missing project evidence into confident claims.
- 01
Actual production traffic, request shape and concurrency.
- 02
The final legal entity, tax treatment and foreign-exchange path.
- 03
Contract-specific or enterprise pricing offered outside public vendor pages.
- 04
Internal security, audit and data-handling requirements beyond the stated exclusions.
- 05
Product, limit and price changes published after 10 September 2026.
- 06
The final model choice and real input, output and tool-call volume.
Your project is not this example
This is one blueprint for one fictional project.
Yours should reflect your users, budget, constraints and way of building.
Build my own blueprint No account required to see your stack snapshot.
What Powers It
This is one blueprint for one fictional project.
Yours should reflect your users, budget, constraints and way of building.
Build your own at whatpowersit.com