AI assistants and search decision

Claude

Build a narrow Claude-like assistant (chat UI + API integration + context retrieval) yourself — it's realistic for a single developer using Anthropic’s API and open-source tooling; but you’ll lose the proprietary models, vetted integrations, and managed enterprise safety/scale that drive Claude’s durable value.

Visit website
Subscription$20/month
Initial build30 hours
Monthly upkeep8 hours + $20
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.

What a replacement has to do

  • Call Anthropic Claude API for chat/completions, manage context (conversation state + embeddings), render UI, store/retrieve short-term cache and user history, and add prompt templates and safety checks.

What it still won’t have

  • Managed model updates and new model access cadence
  • Enterprise-grade safety tuning and monitoring
  • Built-in connectors (vetted integrations) and hosted Workbench
  • BAA/HIPAA-ready managed configuration unless negotiated with Anthropic

What remains hard

  • Proprietary models
  • Integration maintenance
  • Infrastructure at scale
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 2 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 Claude-like assistant using the Anthropic Claude API and a lightweight web UI (stack: Next.js + React frontend, Node.js/Express backend, PostgreSQL for history, and Redis for short-term cache). Core features in scope: API auth and request wrapper, conversation state management with truncation, prompt templates (system/user/instruction), embeddings-based doc retrieval (use Anthropic embeddings or local OpenAI/HuggingFace embeddings), a simple web chat UI, and basic safety/retry/error handling. Out of scope: training models, large-scale multi-tenant infra, enterprise SOC/BAA compliance, and managed connectors. Require error handling, unit tests for API wrapper and conversation truncation, and integration test for end-to-end chat flow.
How we checked6 sources · 2/3 runs agreed · evidence score 22

How the score was reached

  • Pay verdict base20
  • An open-source build was found+5
  • 6 cited sources+3
  • Hard moats found in the evidence-6
  • Evidence score22

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 · 6

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! Price not confirmed on the page — this pricing page renders its price in the browser! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 3 moats recorded