EVALUATION TOOLKIT

Copilot Replacement Readiness Checklist

Hosted Copilot is easy to start with and hard to govern at scale. If your data can’t leave your perimeter — or your per-seat bill has outgrown the value — this checklist helps you decide whether you’re ready to move to a private, on-prem AI platform, and how to get there.

Score your readiness See the Copilot alternative

You’re likely a fit if…

Data can’t leave your tenant
Per-seat cost outruns the value
You need more than autocomplete
You want model choice

WHEN IT MAKES SENSE

Signals it’s time to move off hosted Copilot

Data can’t leave your tenant

Your regulators, customers, or contracts require that source code, documents, or customer data never leave your environment — something a hosted Copilot cannot guarantee.

Per-seat cost outruns the value

You are paying per-seat for Copilot across a large org, but usage is uneven and the spend is hard to justify against measured productivity.

You need more than autocomplete

The real goal is governed multi-agent workflows over internal systems — not just inline suggestions in one IDE or office suite.

You want model choice

You need to choose, swap, and govern models by task and sensitivity, rather than being tied to a single provider’s roadmap and pricing.

READINESS CHECKLIST

Are you ready to replace it?

Tick what’s true today. Your score is saved in your browser — nothing is uploaded.

0of 12 ready

Scope & value

We have identified which Copilot use cases actually drive value (and which don’t).
We know which workflows need private data that must stay in our perimeter.
We have a baseline of current Copilot spend and adoption to compare against.

Infrastructure & models

We have (or can provision) on-prem or private-cloud GPU capacity.
We have selected candidate open-weight or licensed models to run privately.
We can serve models with our preferred stack (vLLM, Ollama, or similar).

Security & governance

SSO, RBAC, and SIEM logging requirements are documented.
Compliance obligations (EU AI Act, GDPR, DORA, HIPAA) are mapped to controls.
A model-governance and approval process is defined.

Rollout & change

We have a pilot team and a first set of high-value workflows to migrate.
Success metrics for the pilot are agreed (adoption, cycle time, cost).
A change-management and enablement plan is in place for wider rollout.

MIGRATION PATH

A four-phase path to private AI

Phase 1

Assess & baseline

Inventory Copilot use, quantify spend and adoption, and identify the private-data workflows a hosted tool can’t serve.

Phase 2

Pilot privately

Stand up VDF AI on-prem with a small team and 2–3 high-value workflows. Wire SSO, RBAC, and logging from day one.

Phase 3

Prove & compare

Measure the pilot against your Copilot baseline: adoption, cycle time, output quality, and total cost.

Phase 4

Roll out & govern

Expand to more teams and workflows under one governance model, retiring Copilot seats where private AI now covers the need.

Plan your Copilot replacement.

We’ll baseline your current Copilot use and design a private pilot around your highest-value workflows. Compare the economics with the flat vs token pricing guide, and see the controls in the Trust Center.