AI assistants and search decision
Agent MMA
A technical user can build a functional replacement for the core prediction and display workflow, but reproducing the full product (continuous live-odds integrations, polished UX, curated data and audience/brand) requires more time and operational effort than a small DIY project.
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-off80 h to build
$100/mo6 h/mo upkeep
No published price to break even against.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Ingest fight schedule and odds, compute model predictions for matchups, store and surface track record and fight breakdowns, update predictions in real time on a web UI, fetch and display fighter stats/news.
What it still won’t have
- polished consumer UX, mobile polish and daily editorial content
- proprietary curated datasets and historical modeling refinements
- brand, audience and SEO that drive traffic
- any commercial agreements for live odds/feeds and aggregated partnerships
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Agent MMA 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 AgentMMA-style web app using Next.js (React) for the frontend, FastAPI (Python) for the backend, Postgres for storage, Redis for job queue, and Docker for deployment. Core features in scope: (1) a scheduled importer that fetches upcoming fight cards and odds from a configurable provider or web scraper and stores normalized fighter and event records in Postgres; (2) a prediction service that calls an LLM or ML model (configurable to use OpenAI API keys or a local model) to produce per-fight probability outputs with a stored prompt and timestamp; (3) background jobs to refresh odds and re-run predictions on schedule; (4) a public UI to list events, open a matchup page showing fighter stats, the AI breakdown, probability numbers, and a simple track-record page listing past predictions vs outcomes; (5) logging, error handling, and retry logic for external fetches; (6) unit tests for importers, prediction pipeline, and API endpoints, plus basic integration tests for the UI. Out of scope: training large proprietary models, mobile native apps, paid account billing, and editorial/news CMS. Require clear error messages, retries for transient failures, metrics for job success/failure, and automated tests covering critical flows.
How we checked
How the score was reached
- Partly verdict base52
- 3/3 assessment runs agreed+4
- Evidence score56
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 · 1
Every page the run actually retrieved.
- official productAgentMMA — AI Fight Intelligence for MMA & UFC Fans
Integrity checks
What held up, and what did not.
