Automation and integrations decision
Commentions - Automated Comments with Mentions
A competent developer can implement a useful subset (automated comment discovery, LLM generation, and posting) in a few weeks, but the full product's trust, safety tuning, and polished UX/support are meaningful differentiators that are costly to reproduce.
Visit website↗$49/mo
$588/yr
Read off the official pricing page.
$100one-off44 h to build
$60/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 2 seats.
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 Commentions - Automated Comments with Mentions alternatives, with the arithmetic →
What a replacement has to do
- Discover new videos for target keywords, read/transcribe video content, generate a context-aware human-sounding comment with an LLM, and post it to YouTube with safety quotas and randomized delays (optionally queue for human review).
What it still won’t have
- 170+ verified reviews and established brand trust
- Founder/dedicated onboarding and priority support
- Built-in safety heuristics and historic zero-ban track record
- Polished UX, money-back guarantee, and paid plan billing/handling
What remains hard
- Brand trust
Excellent Rated 4.9 / 5 based on 170+ verified reviews
- Brand trust
170+ founders · 0 bans · 30+ languages
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 YouTube auto-commenting service using Node.js (Express) + PostgreSQL + React dashboard, deployed on a single Heroku/DigitalOcean droplet. Core features in scope: 1) OAuth linking for YouTube accounts and secure token storage; 2) a scheduled worker to poll the YouTube Data API for new videos matching user-provided keywords; 3) transcript retrieval (captions) or a fallback speech-to-text job (use Whisper-as-a-service) to extract video context; 4) comment generation using an LLM (OpenAI or compatible) with configurable tone templates and quota enforcement; 5) posting engine that applies randomized delays and per-account safety quotas and records posting results; 6) a lightweight dashboard to view queued comments, approve/reject, and show posting logs. Out of scope: multi-tenant billing, large-scale rate-limiting infrastructure, built-in analytics beyond basic logs. Require error handling for API failures, token refresh, retries, and unit tests for core modules (auth, polling, generation, posting).
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 3 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score67
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 productCommentions homepage (product + pricing)
- official pricingCommentions pricing
- open sourcedarkzOGx/youtube-automation-agent
Integrity checks
What held up, and what did not.
