Should we standardize on a single AI vendor?

Standardize the platform layer for operability, but keep the model layer swappable behind an internal interface. Single-vendor simplicity is worth real money; single-vendor lock-in at the model layer rarely is.

How do we compare vendors fairly?

One evaluation set you own, run identically against every candidate, scored per case by the same reviewers against a threshold written before testing. Anything else compares sales effort.

Are open models a serious option?

For some workloads, yes — particularly where data residency, cost at volume or control over behaviour dominates. They shift cost from licence to engineering and operations, so the comparison must include the people to run them.

How long should a vendor evaluation take?

Weeks. A longer process usually means the acceptance threshold was never written, so no result can end it.

意思決定インテリジェンス

AI ベンダー評価：デモでは見えない五つの評価軸

評価すべきはデモが示さない五点です。可逆性（他社移行に要する時間と再作業、プロンプトや評価セットの可搬性）、評価の透明性（自社テストセットで個別結果を確認できるか）、データ条件（保持・学習利用・削除）、実運用コストの変動、そして障害時の責任範囲です。

Decision intelligenceWritten by NirjiX AI AdvisoryPublished January 2026Last reviewed February 20268 min read

Direct answer

結論

以下の詳細分析は英語原文のまま掲載しています。英語版のフルガイドを読む

Evaluation criteria that separate vendors

Criterion	What to ask for	Red flag
Reversibility	Export of prompts, evaluation sets, tuned artefacts and embeddings in a usable format	Portability described as possible but never demonstrated
Evaluation transparency	Run your own test set; see per-case outputs and failures	Only aggregate scores, or benchmark results you cannot reproduce
Data terms	Written retention, training-use, processing-location and deletion terms	Terms that can change unilaterally with notice
Roadmap dependency	Which committed capabilities are shipped today versus promised	The business case depends on a feature in preview
Operational fit	Latency and rate limits under your peak, plus incident history	No named support path and no status history

Running a bake-off that produces a decision

01
Write the acceptance threshold first
Define what performance is good enough for this use case before you see any vendor's output.
02
Build one evaluation set you own
Real cases, including the hard and the ambiguous ones. It becomes a durable asset across future vendor changes.
03
Test the same set with every candidate
Same prompts, same data, same reviewers. Score per case, not by impression.
04
Price the second decision
Model the cost of moving away in eighteen months. That figure is the real difference between finalists.

Contract terms worth negotiating before signature

Export rights for your data, prompts, evaluation sets and tuned artefacts, in a documented format.
Notice and price-protection terms for pricing model changes.
Commitment that your data is not used for model training without explicit opt-in.
Defined support response times and a named escalation path.
The right to run independent evaluation and publish internal results.

NirjiX view

The NirjiX view

The right question is not which vendor is best today. Model capability moves fast enough that the ranking will change; what will not change quickly is how expensive you have made it to switch.

Buy the layers that commoditize, own the layers that encode your judgement — evaluation sets, prompts and process design are the assets that survive a provider change.

Frequently asked executive questions

Should we standardize on a single AI vendor?: Standardize the platform layer for operability, but keep the model layer swappable behind an internal interface. Single-vendor simplicity is worth real money; single-vendor lock-in at the model layer rarely is.
How do we compare vendors fairly?: One evaluation set you own, run identically against every candidate, scored per case by the same reviewers against a threshold written before testing. Anything else compares sales effort.
Are open models a serious option?: For some workloads, yes — particularly where data residency, cost at volume or control over behaviour dominates. They shift cost from licence to engineering and operations, so the comparison must include the people to run them.
How long should a vendor evaluation take?: Weeks. A longer process usually means the acceptance threshold was never written, so no result can end it.

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Related intelligence

このテーマの中心ガイド

企業はAI機能を自社構築すべきか、プラットフォームを購入すべきか。

本ページは意思決定の一側面を扱います。AIの内製と購入の比較に関するNirjiXの総合ガイドで全体像をご確認ください。

AIの内製と購入の比較 →

Sources and methodology

This page reflects NirjiX advisory practice rather than a survey or a vendor benchmark. The structure of the assessment — the dimensions, the maturity language and the sequencing logic — is the same framework used inside the NirjiX AI readiness assessment and the AI plan builder.

Where we describe patterns ("most organizations discover…"), we are describing what we observe across client engagements, not a measured statistic. We deliberately avoid quoting market numbers we cannot verify, because an AI investment case built on borrowed statistics collapses the first time a CFO tests it.

Any figure that ends up in your own plan should come from your own data: your cost base, your cycle times, your error rates, your volumes. The assessment and plan builder are designed to force that discipline.

Turn the judgement into a plan you can fund

The AI readiness assessment scores where the organization actually stands; the AI plan turns that into a sequenced, costed set of moves.

Run the AI assessment Build the AI plan

Outputs are preliminary and intended for advisor validation before funding decisions.

AI ベンダー評価：デモでは見えない五つの評価軸

結論

Evaluation criteria that separate vendors

Running a bake-off that produces a decision

Write the acceptance threshold first

Build one evaluation set you own

Test the same set with every candidate

Price the second decision