Interview, then compare architectures
compare_architecturesAsks the questions that decide an architecture (static or dynamic, who signs in, how much data, uploads and media, background jobs and live updates, data sensitivity, threat profile, cost of downtime, who runs the servers), then rates every architecture against the answers: static site on a CDN, frontend with Supabase, serverless Next.js on Vercel, Cloudflare Workers, containers on DigitalOcean App Platform, one server, one server behind Cloudflare, Firebase, or on the phone with no server. Returns the top three with a 0-100 fit, what each service does, the monthly cost now and at forecast, strengths and concerns; the rest it considered; and the ones ruled out with the reason. Interview loop: call it with the description first; show the user the questions (each has options, why it matters, and sometimes a suggested answer read from the description); ask the unanswered ones, a few at a time, and call it again with answers. It ranks with whatever is answered, so it is fine to stop early. No model is used for the questions or the ratings. Each architecture has plan_stack_arguments and share_url for the full plan. When the user adds a requirement later, call it again with the same answers and the new description or requirements.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| answers | No | Interview answers by question id, each an option id from compare_architectures, e.g. {"server":"dynamic","logins":"consumers","threat":"public","ops":"managed"}. | |
| founder | No | Optional company details used only to find startup perks. | |
| mau_now | No | ||
| app_type | No | Kind of app, from Product Hunt's categories: productivity, engineering_development, design_creative, finance, marketing_sales, data_analysis, platforms, no_code, product_addons, llms, ai_agents, voice_ai, social_community, health_fitness, family, lifestyle, web3, ecommerce, travel, physical_products, booking_marketplace, content_site, ios_on_device. Sets the usage profile and, when users are not given, typical users at launch and after the forecast period. | |
| platforms | No | Where the app runs. A phone app with no web platform gets the phone questions and options. | |
| categories | No | Categories to plan before the answers adjust them. Default: what the description needs. | |
| profile_id | No | Usage profile: b2b_saas (default), consumer_app, booking_marketplace, content_site, ios_on_device. Defaults to the app type's profile. | |
| description | No | Plain-language product description or PRD text. Read for requirements (unless requirements is given) and for suggested answers. | |
| task_intent | No | What the user is trying to do, in a few words, e.g. "pick a stack for an MVP" or "cost section for an investor deck". No names or contact details. | |
| mau_forecast | No | ||
| requirements | No | Requirements already confirmed; when given, the description is not read for requirements again. | |
| forecast_months | No | ||
| budget_monthly_usd | No | ||
| output_destination | No | Where the answer goes. chat (default): summary for the conversation. document: a section ready to paste into a PRD. code: adds npm packages and environment variable names for each pick. | |
| revenue_per_mau_usd | No | Average monthly revenue per active user (for payment fees). |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | The tool result. Its shape depends on the tool. | |
| stale | Yes | ||
| caveats | Yes | Show to the user word for word. | |
| sources | Yes | ||
| confidence | Yes | ||
| assumptions | Yes | Usage and pricing assumptions behind the numbers. | |
| disclosures | Yes | Always includes the neutrality note: no vendor pays for a place in the answer. Link its url when you mention how Kong Reviews is paid. | |
| generated_at | Yes | ||
| engine_version | Yes |