AI Solutions · Managed AI Infrastructure
Managed AI Infrastructure
The bill arrives monthly as one number nobody can attribute. Meanwhile nobody can say whether the system is behaving or quietly drifting. [Draft copy]
The problem
It is production infrastructure, and it is being run like a subscription.
Token spend is a single line item. It cannot be attributed to a team, an application or a use case, so it cannot be challenged or forecast. There are no budgets and no alerts, which means the first signal that something is wrong is the invoice — and a retry loop left running over a weekend can cost more than the project saved in a quarter.
The observability gap is worse than the cost gap. Without traces of what went in, which tools were called and what came back, there is no way to tell a system that is working from one that is quietly degrading, and no way to answer a customer asking why it did what it did.
What enterprise-grade looks like here
Metered like a utility, watched like a production system.
Enterprise-grade means token consumption is accounted for per team, per application and per use case, with budgets, alerts and hard caps that act before the month ends. It means every run leaves a trace — inputs, tool calls, outputs, latency and cost — retained long enough to answer a question asked later.
And it means guardrails at the boundary rather than good intentions in a document: input and output filtering, detection of prompt-injection and data-exfiltration patterns, and rate limits per identity rather than per application, so one compromised credential cannot spend the annual budget in an afternoon.
Abuse monitoring is the part organisations reach for last and need first. Volume and pattern anomalies, off-purpose use, and credentials behaving unlike their owner — caught by the platform, with a defined response, rather than discovered in a reconciliation three weeks later.
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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Meter the tokens and attribute them
Per team, per application, per use case. Then budgets, alerts and hard caps, so cost is a control rather than a report.
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Trace every run
Inputs, tool calls, outputs, latency and cost, with a stated retention period. This is what makes "why did it do that" an answerable question.
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Put guardrails at the boundary
Input and output filtering, injection and exfiltration detection, and rate limits bound to an identity. Enforced in the platform, not requested of developers.
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Monitor for abuse, and define the response
Anomaly detection on volume and pattern, tied to a runbook that says who is called and what is suspended. Detection without a response is a dashboard nobody opens.
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Run it, or hand it to you
We will operate it while your team builds the capability, and we would rather hand it over than keep it.
[Draft copy]
What you end up owning
Documents and configuration, not a dependency.
| Deliverable | Owned by |
|---|---|
| Token accounting and showback by team and use case | Finance and platform jointly |
| Budgets, alerts and hard caps | Platform team |
| Trace store with a stated retention policy | Operations |
| Guardrail policy set at the boundary | Security and platform |
| Abuse detection rules and the response runbook | Security and operations |
[Draft copy]
Tell us what you are being asked to prove.
Tell us what your AI spend was last month, and whether you can attribute it.
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