Social media decision

Ocoya

A technically capable developer can build a useful subset (AI captioning + scheduling + basic publishing) in a week and run it cheaply; reproducing Ocoya's full product (proprietary dataset-driven predictions, many integrations, enterprise polish, and scale claims) is much larger and better left to the vendor.

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Subscription$15/month ✓ verified
Initial build30 hours
Monthly upkeep6 hours + $50
Evidence2/3 runs agree

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 Ocoya alternatives, with the arithmetic →

What a replacement has to do

  • Generate social post copy with an LLM, attach/upload media, schedule posts to connected social accounts, and persist assets & schedules.

What it still won’t have

  • AI agent templates and built-in automations/workflows
  • Large set of integrations and design plugin ecosystem (Canva, Unsplash, Giphy)
  • Proprietary analytics trained on Ocoya's dataset and best-times predictions
  • High-throughput scheduler claimed at product scale (eg. 'a thousand posts per minute')
  • Multi-workspace enterprise controls, approvals, and client-facing collaboration polish

What remains hard

  • Proprietary dataTrained on over 20M data-points
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 4 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

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

Not run yet
Build a minimal AI-driven social post scheduler using Next.js (React) frontend, Postgres, Redis, and a Python (FastAPI) backend. Implement: user auth (email), workspace and single-user support, media upload to S3-compatible storage, LLM caption-generation endpoint (pluggable, default OpenAI), per-request credit accounting, OAuth connectors for at least Twitter/X and Facebook/Meta to publish posts, a job scheduler (Redis + RQ/Celery) to execute scheduled publishes, and a calendar view to create/edit/schedule posts. Exclude: multi-tenant enterprise roles, advanced analytics, many third-party design integrations, and AI agent/workflow builder. Provide error handling for failed publishes, webhook retry logic, unit and integration tests for API endpoints and scheduler jobs, and Docker-compose for local dev plus deployment instructions for a single VPS or managed cloud (Postgres, Redis, object storage).
How we checked5 sources · 2/3 runs agreed · evidence score 60

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Price verified on pricing page+3
  • Hard moats found in the evidence-3
  • Evidence score60

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 · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat quoted from the page