AI assistants and search decision

Microsoft 365 Copilot Business

A narrow Copilot-like assistant (document search + chat + draft generation) is feasible for a single developer in about a week using existing LLM APIs, but reproducing Microsoft 365 Copilot's deep app integrations, enterprise features, and managed service experience is not realistic.

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You pay

$21/mo

$252/yr

Not verified against a pricing page.

You’d pay instead

$50one-off30 h to build

$200/mo8 h/mo upkeep

On cash alone, building overtakes the subscription at 10 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 Microsoft 365 Copilot Business alternatives, with the arithmetic →

What a replacement has to do

  • A chat-style assistant that answers user questions using company documents and composes drafts (e.g., email or doc snippets) from LLM outputs with retrieved context.

What it still won’t have

  • Deep native integration with Microsoft 365 apps (Word, Outlook, Teams) and real-time context
  • Microsoft enterprise support, SLAs, and managed compliance
  • Proprietary model tuning and any closed-source enhancements Microsoft provides
  • Built-in org-level deployment, licensing, and billing handled by Microsoft

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 10 seats.

Paid seatsseats

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 business AI copilot as a web app using: Node.js + Express backend, React frontend, PostgreSQL for user/workspace metadata, an object store (S3-compatible) for documents, and a vector DB (weaviate or pgvector). Core features in scope: document ingestion (PDF/DOCX/email), embedding pipeline and similarity search, LLM orchestration with prompt templates, a chat UI showing source snippets and attribution, and basic auth + single-tenant workspace isolation. Out of scope: deep integrations with Microsoft 365 apps, custom model training, enterprise compliance certifications, and multi-region deployment. Include error handling for failed ingestion and API calls, unit tests for ingestion/search/LLM orchestration, and deployment instructions (Docker + cloud instructions).
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.

! 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! 1 moat recorded