Modelling the real all-in cost, sequencing workloads so the move de-risks itself, and the exit-cost arithmetic that decides your negotiating position
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Per-seat AI pricing scales with headcount while AI consumption follows a power law, so the two curves separate as an organization grows. The gap is what makes migration worth the effort — but the effort is real, and a migration that stalls halfway costs more than either endpoint.
The first task is arithmetic, and it is where most business cases go wrong: the advertised seat price is frequently an add-on figure that requires a base licence underneath.
The second is sequencing. Moving the highest-volume, lowest-risk workload first produces a measured saving that funds and justifies the rest, which is what keeps a migration from stalling at the pilot.
Requests per user per period, not licence counts. Most organizations discover that a minority of licensed users generate the large majority of usage.
Including prerequisite base licences, promotional expiry dates, minimum commitments and renewal notice periods. These determine when a migration can actually land.
Every system the current AI touches. Integration work is usually the largest line in the migration estimate and the most commonly underestimated.
Migrations that belong to nobody stall at the pilot. This needs someone accountable for the end state, not just for the evaluation.
Start with the number you are actually committed to, not the advertised one. AI seats are frequently add-ons requiring a qualifying base licence, which can double or triple the effective figure.
Model the renewal figure, not the introductory one.
Take a representative sample of real requests, measure tokens consumed, and price them against the models you would actually route to — including a cheap local model for the routine tail.
Output is typically priced several times higher.
This number rarely appears in a business case and frequently determines whether the migration is viable. What would have to be rebuilt to run equivalent workloads elsewhere?
If this lives in the vendor's platform, ending the contract ends your access to it.
Move the highest-volume, lowest-risk workload first. It produces the largest measurable saving soonest, which is what funds and justifies the remainder of the migration.
Document classification and internal search are typical first movers.
Parallel running is what turns a migration from a leap into a measurement. It also produces the evidence that makes the next workload an easy decision rather than an argument.
Migrations stall on doubt more often than on technology.
Complete the technical migration before the notice period closes. A migration that finishes a month after auto-renewal buys another year of the cost you were trying to remove.
Under per-seat licensing, engineering that reduces consumption produces no saving. The same work after migration compounds every month, which is a genuine and underrated part of the return.
Once cost stops tracking headcount, rationing access to control licence spend becomes unnecessary — which usually reduces shadow AI as a side effect.
Export historical audit records before the contract ends, and confirm the new platform writes an equivalent trail from day one. Regulators do not accept a gap.
Reconnecting the AI to source systems, identity and downstream workflows is usually larger than the model or platform work. Estimate it explicitly rather than folding it into contingency.
Total platform and infrastructure spend divided by measured request volume
Parallel running with identical held-out tasks
Workload inventory tracked against the sequencing plan
Confirm delivered source, exportable data and no dependency on vendor infrastructure to keep running
Consequence: The incumbent looks far cheaper than it is, and the business case fails on arithmetic rather than on merit.
Prevention: Count prerequisite base licences on the incumbent side, and infrastructure and operations on the new side. Both or neither.
Consequence: The riskiest change happens when the team has the least experience with the new platform, and one bad outcome stops the programme.
Prevention: Sequence by volume and risk. High volume, low risk first — the saving is largest and the downside is smallest.
Consequence: Auto-renewal locks in another full term of the cost the migration was meant to eliminate, regardless of technical completion.
Prevention: Diarize the notice deadline at project kickoff and treat it as the real deadline, ahead of the technical one.
Consequence: Records a regulator later requests are inside a platform you no longer have access to, and cannot be reconstructed.
Prevention: Export and verify all audit history before the contract terminates, and confirm the export is readable outside the vendor's tooling.
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
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