AI assistants and search decision

Open-Claw.org

A single competent developer can build and operate a useful self-hosted subset (chat gateway, RAG memory, scheduler, basic UI) in about a week and modest monthly ops; the hosted product's managed sandbox, bundled API credits, and turnkey multi-channel support are what you'd give up.

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Subscription$39.9/month ✓ verified
Initial build25 hours
Monthly upkeep6 hours + $100
Evidence1/1 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 Open-Claw.org alternatives, with the arithmetic →

What a replacement has to do

  • Run an LLM-backed chat gateway that receives messages from chat apps, performs retrieval-augmented generation from a personal memory store, executes simple background cron/heartbeat tasks, and sends actions/responses back through the chat gateway.

What it still won’t have

  • Dedicated cloud sandbox (managed, isolated hosting)
  • Built-in LLM API credits included with plans
  • Shared model pool (GPT-5.5 / Claude Opus 4.8 / Gemini 3.5 Flash) ready-to-use
  • Turnkey support for many chat platforms out of the box
  • Priority customer support and managed auto-repair/configure features

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 3 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 self-hosted OpenClaw-style personal AI assistant using: backend in Python (FastAPI) + PostgreSQL (with pgvector) for memory, Redis for job queue, a Docker Compose deployment, and a React web chat UI. Implement: (1) Telegram webhook connector and one additional chat connector (Twilio/WhatsApp), (2) LLM provider adapter layer that supports swapping keys and records usage, (3) embeddings-based retrieval (RAG) against Postgres+pgvector, (4) background scheduler for heartbeats/cron jobs that can send proactive messages, (5) a simple control panel to view logs, start/stop instance, and paste a gateway token. Out of scope: multi-tenant managed hosting, proprietary shared model pool, and web-scale autoscaling. Include authentication for control UI, robust error handling, unit and integration tests for connectors and RAG flow, Dockerfile and docker-compose for local deployment, and a README with deployment and backup steps.
How we checked4 sources · 1/1 runs agreed · evidence score 93

How the score was reached

  • Build verdict base78
  • An open-source build was found+5
  • 4 cited sources+3
  • Price verified on pricing page+3
  • 1/1 assessment runs agreed+4
  • Evidence score93

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 · 4

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page✓ 1 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded