AI assistants and search decision
Gronk
A single technical user can build a useful self-hosted chat+image assistant using existing open-source projects, but reproducing the App Store polish, native distribution, and subscription plumbing at commercial quality is larger and outside the small-scope replacement.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Gronk alternatives, with the arithmetic →
What a replacement has to do
- User sends text or image → backend forwards to chosen LLM/image API → receive response/image → show in chat UI and save to history.
What it still won’t have
- App Store distribution, reviews, and existing user base
- Polished native iOS UI and platform integrations (in-app purchases/subscriptions)
- Developer-provided curated assistant presets and ongoing app-store updates
- Brand/trust and cross-device App Store presence (ratings)
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 2 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal self-hosted AI chat webapp: backend Node.js (Express) + Postgres, frontend React, store uploads on S3-compatible storage. Core features: user sign-up/auth (email+password), chat UI with streaming responses, select model (provider key), proxy requests to OpenAI-compatible text and image APIs, save conversation history, upload images for image descriptions, basic rate-limiting and logging. Out of scope: App Store in-app purchases, native iOS client, advanced assistant marketplace, large-scale multi-tenant billing. Include error handling, unit tests for API routes, and basic integration tests for chat flows.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- Evidence score63
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 4
Every page the run actually retrieved.
- official productAI Chat Assistant - ChatOne App - App Store
- open sourceNextChat (prior art)
- open sourceLibreChat (prior art)
- open sourceGPT4All (prior art)
Integrity checks
What held up, and what did not.
