AI assistants and search decision
Kickly - Pronos IA 🎯
A competent developer can build a useful MVP replacement (data ingestion, model, simple UI) and run it cheaply; no durable moats are evident from the provided page so self-hosting is realistic.
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-off46 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 Kickly - Pronos IA 🎯 alternatives, with the arithmetic →
What a replacement has to do
- Ingest match and team data, compute features, run prediction model, present predictions and confidence to user, refresh predictions on schedule
What it still won’t have
- Existing Kickly user base and brand
- Any proprietary training data or tightly-tuned proprietary models Kickly may have
- Commercial integrations (paid odds feeds, analytics partnerships) if present
- Turn-key polished UX, onboarding flows and product polish
- Support SLA and marketing/distribution
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Kickly - Pronos IA 🎯 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 football prediction service using PostgreSQL, Python (FastAPI), scikit-learn or LightGBM for models, and a small React frontend. Core features: (1) ingest public match and team data (use Transfermarkt CSVs or public APIs) and normalize to Postgres; (2) implement a reproducible feature engineering pipeline and train a simple predictive model and expose a /predict API; (3) web UI listing upcoming matches with predicted outcome and confidence; (4) scheduler to refresh data and recompute predictions daily; (5) basic user authentication, logging, and error handling; (6) Dockerfiles, deployment scripts for a single VPS (Docker Compose), and unit tests for ingestion, model, and API. Out of scope: training large neural networks, paid odds-feed integrations, mobile apps, and multi-tenant billing. Include input validation, retry/backoff for external fetches, and CI tests that run model training on a small sample dataset.
How we checked
How the score was reached
- Build verdict base78
- An open-source build was found+5
- 2 cited sources+1
- Evidence score84
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 · 2
Every page the run actually retrieved.
- official productKickly — L'IA au service de vos pronostics football
- open sourcedcaribou/transfermarkt-datasets
Integrity checks
What held up, and what did not.
