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The question is not which model. It is where it runs, who can reach it, and how much it costs you to change your mind later. [Draft copy]
The problem
The second use case costs as much as the first.
Adoption happened per team. Each picked a provider, wired its SDK directly into the application, and solved retrieval, prompt management and access control on its own. The model is now a hard dependency of the code rather than a decision you can revisit.
So nothing compounds. There is no shared retrieval layer, no shared evaluation, no common view of who is calling what. Changing provider means changing applications, which means it will not happen — and that is a commercial position as much as a technical one.
What enterprise-grade looks like here
A platform layer, so the model stays a decision rather than a dependency.
Enterprise-grade means your applications talk to a platform, and the platform talks to whichever model is appropriate. Routing, retries, fallback and access control live in one place. Retrieval is a service rather than something each team rebuilds. Evaluation is shared, so quality is comparable across use cases instead of asserted per team.
Cloud-native or self-hosted is an evidence question, not a preference. Managed services such as Amazon Bedrock and its equivalents are the right answer when the data residency terms, the commercial terms and your obligation all permit it. Self-hosting on your own GPU hardware is the right answer when they do not — data that cannot leave the perimeter, or hardware you have already bought and should be using. We have built both and will tell you which one your situation actually calls for.
Routing is where the money is. Frontier models earn their price on architecture and hard reasoning; a great deal of production work — classification, extraction, test generation, first-pass review — runs on far smaller and cheaper models at the same quality. Paying premium rates for every call is the single most common source of waste we see.
This framing repeats on every capability page. It is the differentiator, and it reads consistently across all of them (§9 T3).
What we do
Five moves, in this order.
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Decide hosted or self-hosted on the evidence
Data residency, latency, cost at your real volume, and what your obligation permits. Written down with the rejected option and the reason, so the decision can be revisited when the inputs change.
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Build the platform layer
One provider-agnostic interface for your applications, with routing, retries, fallback and per-team access control behind it.
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Make retrieval and evaluation shared services
One place to index and retrieve, one place to evaluate. This is what makes the second use case cost a fraction of the first.
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Route by task, not by habit
Large models where they earn it, small ones everywhere else, with the policy expressed in configuration rather than in each developer’s judgement.
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Hand over the platform
Runbooks, capacity model and upgrade path. Your platform team runs it; we are not in the critical path.
[Draft copy]
What you end up owning
Documents and configuration, not a dependency.
| Deliverable | Owned by |
|---|---|
| Provider-agnostic platform layer | Platform team |
| Hosting decision with the alternatives recorded | Platform team and risk |
| Shared retrieval service | Platform team |
| Shared evaluation harness | Engineering |
| Model routing policy and per-team access control | Platform team |
[Draft copy]
Tell us what you are being asked to prove.
Tell us where your AI runs today and what you are not allowed to send it.
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