AI assistants and search decision
DEJAVU
A compact QA assistant matching the product's stated title can be built and self-hosted by a single capable developer in roughly a week using existing open-source RAG/assistant projects; no durable moats are visible from the supplied page.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off32 h to build
$50/mo3 h/mo upkeep
No published price to break even against.
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 DEJAVU alternatives, with the arithmetic →
What a replacement has to do
- Accept a user's question via a simple web chat UI, send the prompt to a hosted LLM API (optionally with a small retrieval/knowledge-context step), receive the model response, and render it back to the user with basic session history.
What it still won’t have
- Proprietary hosted scaling and hardened infra
- Any closed-source fine-tuned models or bundled API keys
- Polished product UX, analytics, and commercial support
- Any undisclosed integrations or proprietary data the vendor may have
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
DEJAVU does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
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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 Q&A assistant using Next.js (React) for the frontend, a Node.js + Express backend, Postgres for session/history, and Milvus or SQLite+FAISS for a small vector index. Core features in scope: web chat UI, backend endpoint to call an external LLM API (configurable key), prompt templating with top-k vector retrieval, persistent session storage, and basic auth. Out of scope: built-in model training, multi-tenant billing, high-availability clustering. Include error handling, input validation, and unit tests for backend endpoints.
How we checked
How the score was reached
- Build verdict base78
- An open-source build was found+5
- 3 cited sources+3
- Evidence score86
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 · 3
Every page the run actually retrieved.
- official productKriminal AI — The AI That Answers Everything
- open sourceDocsGPT repository
- open sourceFastGPT repository
Integrity checks
What held up, and what did not.
