Managed AI Operations & FinOps

The project shipped. Now keep it running — and keep it from quietly getting expensive.

A live AI system needs two things a launch doesn't provide: someone watching it in production, and someone watching the bill. We run your workloads to an agreed service level, and we keep your AI spend tracking value instead of drifting upward as usage grows.

Talk about operations →Start with the free assessment

Keep it running

Managed AI operations, to an SLA.

We operate your live workloads — monitoring, alerting, incident response, routine improvement, and reporting — against service-level targets we agree up front. Two tiers, by how much the workload can afford to fail.

Standard SLA

Business-hours support with agreed uptime and response targets. We watch the system, catch problems before your users do, apply routine improvements, and report against the numbers each month.

  • Monitoring, alerting, and incident response
  • Agreed uptime and response-time targets

Monthly retainer · by workload criticality

Regulated / validated

For workloads that answer to an inspector. High-availability targets plus the controls regulated operations require — validated change control, audit-ready records, and a documented trail for every intervention.

  • High-availability targets, 21 CFR Part 11-aware
  • Validated change control and audit-ready records

Monthly retainer · regulated tier prices higher

Continuous security assurance

An agentic system's security posture expires the moment someone swaps a model, edits a prompt, or connects a new tool — which can be any afternoon. We re-test the attack surface on a standing cadence and when your system changes, so a clean assessment stays true instead of quietly going stale.

  • Recurring adversarial testing, plus a re-test on change
  • Evidence kept current for audits and vendor questionnaires

Monthly retainer · usually follows an assessment

FinOps

Keep it efficient. Most AI bills are bigger than they need to be.

AI spend drifts up quietly — the wrong model doing simple work, prompts sent uncached, requests that could be batched, workloads never right-sized after launch. We find the waste and take it out, without touching quality.

One-time optimization audit

We analyze your usage, hand you a prioritized list of savings with an estimate against each, and implement the quick wins. A fixed-fee project designed to pay for itself — often several times over.

  • Usage analysis with an estimated-savings figure
  • Quick wins implemented, not just recommended

Fixed fee · scoped to a savings estimate

Ongoing FinOps retainer

Efficiency isn't a one-time fix — as usage grows and models change, new waste appears. We monitor consumption continuously and keep optimizing, so your cost-per-outcome falls even as your volume climbs.

  • Continuous consumption monitoring
  • Ongoing rightsizing as usage and models change

Monthly retainer · scales with your usage

Where the money goes

The four places AI spend leaks — and what we do about each.

None of this shows up in a demo. It shows up on the invoice three months after launch, and by then nobody remembers why. Here's where it hides.

  • Wrong model for the job — a frontier model doing work a smaller, cheaper one handles just as well. We match the model to the task, per task.
  • No caching — the same long context or system prompt paid for on every single call. Prompt caching often removes a large slice of the bill outright.
  • No batching — high-volume work run one call at a time at full price when it could be batched at a discount.
  • Never right-sized — prompts, retries, and output limits set on day one and never revisited as the workload settled. We tune them against real traffic.
# FinOps levers — assessed per workload
model_routing:
  - match: task complexity to model tier
  - route: simple work to smaller models
caching:
  - cache: stable system prompt + context
batching:
  - batch: high-volume, non-urgent jobs
rightsizing:
  - tune: prompt length + output caps
  - cap: retries and runaway loops
report:
  spend: tracked to value
  quality: held, not traded

How it runs

Run to an SLA. Save without trading quality.

Operations is a monthly retainer, billed in advance and scaled to how critical the workload is. The FinOps audit is a fixed fee scoped to what it will save — designed to pay for itself.

To an SLA
Uptime and response targets agreed up front, reported monthly
Pays for itself
The audit is scoped against the savings it's expected to find
Quality held
Spend comes down; the output your users see does not

Where this fits

This is the last stage — and the one that never ends.

Operations and FinOps sit at the far end of our process — assess, build, deploy, enable — and then keep going for as long as the system is live. You don't have to have built with us to hand it to us: if you have a running AI workload that nobody is truly watching, or a bill that's climbing faster than the value, that's exactly where we start.

Running hot, or costing too much?

Tell us what's live and what's worrying you — a reliability gap, a rising bill, or both. We'll propose the SLA, the audit, or the retainer that fixes it.

Talk about operations →Get your free assessment