AI assistants and search decision
koala.fyi
A solo developer can build and run a minimal AI search service (index, embeddings, vector DB, search API, small UI) faster than maintaining a hosted product; prior-art projects like xerj show the core is reproducible.
Visit website↗Built by Jared Rhizor, who ships 4 products in this index
Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$50one-off26 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 koala.fyi alternatives, with the arithmetic →
What a replacement has to do
- Index documents -> embed vectors -> store vectors -> run nearest-neighbor search -> serve results via HTTP
What it still won’t have
- hosted product polish and UI/UX iteration
- commercial support and SLAs
- any proprietary optimizations or hosted analytics the vendor might add
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
koala.fyi 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
—
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 AI document search service: use Python FastAPI for the API, Postgres + pgvector for storage, Docker Compose for local deployment, React for a tiny frontend, and OpenAI-compatible embeddings (configurable). In scope: document ingestion (file upload and plain-text extraction), embedding computation and batched vector upserts, metadata storage in Postgres, a /search endpoint implementing nearest-neighbor + simple scoring, and a single-page UI to enter queries and show results. Out of scope: multi-tenant billing, advanced agent orchestration, long-term analytics, and enterprise SLAs. Include error handling, input validation, Dockerized deployment, and unit tests for the API.
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 productofficial-product
- open sourceswirlai/swirl-search
- open sourcearc53/DocsGPT
Integrity checks
What held up, and what did not.
