AI assistants and search decision

CoDude

A competent developer can build a usable self-hosted agent prototype (chat, RAG, LLM integration) in ~40 hours using open-source projects, but duplicating a polished, fully integrated commercial product (support, broad connectors, polishing and scale) is more work.

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You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$100one-off40 h to build

$50/mo6 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 CoDude alternatives, with the arithmetic →

What a replacement has to do

  • User submits a task → agent orchestrates LLM calls and tools → returns structured result and context to the user

What it still won’t have

  • Proprietary or bundled models trained by the vendor
  • Polished enterprise integrations and UI polish
  • Customer support, SLA, and hosted convenience
  • Any closed-source features the vendor provides

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

CoDude 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

Keep paying
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

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

Not run yet
Build a minimal self-hosted AI-assistant web app using: Next.js (React) frontend, Node.js/Express backend, Postgres for user/storage, Pinecone (or an open-source vector DB) for embeddings, and OpenAI-compatible LLM API. Core features: login (email or OAuth), chat UI with conversation history, document upload + embedding ingestion pipeline (PDF/TXT), vector-based retrieval-augmented responses, an agent orchestration layer that runs simple tool calls (web search, calculator), admin health endpoints, Dockerfile and deploy scripts for a single VPS. Out of scope: multi-tenant billing, advanced observability, enterprise SSO. Include input validation, retries for external calls, error logging, and automated tests for ingestion, LLM integration, and auth flows.
How we checked3 sources · 2/3 runs agreed · evidence score 60

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 3 cited sources+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 · 3

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded