RosettaOps™ for LLM Spend
Real-time LLM and AI cost governance
Govern AI spend
while it is happening
AI is your fastest-growing line item, and for some teams it is now five to fifteen percent of revenue. Every user gets a sandboxed budget covering the model services they can reach, and LLM token cost lands against it within five minutes of the call.
What you get
Per-user AI budgets
Users work in sandboxed accounts, so the account budget is their budget. AI spend is priced within five minutes and drawn down alongside everything else. When it runs out, the next call fails at the cloud, not at our middleware.
Per-project model restrictions
Restrict which models each project, team, or environment can call. Production gets approved models only. Sandbox gets the experimental ones. Cost-per-customer math holds because model selection holds.
Token-spike detection
Continuous evaluation of token usage against a baseline. AI-generated bad SQL, runaway agent loops, model-version drift, and accidental retries all trigger before the bill closes.
GPU saturation alerts
Training and fine-tuning jobs that drift past their reserved budget surface in real time. GPU under-utilisation is flagged separately. Idle GPU is still the most expensive idle resource on your bill.
Live token audit trail
Every prompt, every response, every model call. Who, when, which model, how many tokens, what dollar cost. Audit-grade trail for compliance, customer support, and post-incident review.
Bedrock Sandbox
A governed AI sandbox for evaluation, prototyping, and small-team workloads. Pre-set budgets, model restrictions, and audit trail by default. New ML engineers get access without your team writing IAM policies.
Works with your gateway
A gateway only works if the
direct route is closed
A gateway governs the calls that reach it, and anyone with their own cloud account can go straight to the model instead. We close that route at the cloud, so the approved path is the only path. Keep the gateway you have.
See your own AI spend by user, project and model, in your own cloud account.
Closed-Loop FinOps™ for AI
The same loop,
pointed at model calls
The same closed-loop pattern that governs your cloud spend, applied to AI. One platform, one decision, every model call.
1. Define
Set the AI budget envelope
Budgets by user, project and environment, and the models each role is allowed to call, set once from one console.
2. Enforce
Cap at the cloud API
When budget is reached, the cloud account's permissions change. The next AI call fails at the provider's API, not at our middleware.
3. Detect
Live token tracking
Continuous evaluation against budget and baseline. Token spikes, model drift, GPU saturation, and runaway agents all surface before the bill closes.
4. Remediate
Auto-pause runaway jobs
Idle GPU pauses on a budget threshold. Runaway training jobs hibernate. Each remediation rule ships with a documented rollback contract so trust scales with adoption.
Your data stays in your cloud
Your AI cost and usage history stays in your own cloud account, queried by your cloud’s own engine. We hold a rolling 48 hour cache and nothing beyond it, all of it rebuildable from your own trail.
No per-token vendor tax, and no data lake to be locked into. Audit trails are yours, exportable in FOCUS 1.3 on demand.
Public reporting, 2025 and 2026
5 to 15%
of cost-of-goods-sold, up from under 1% two years ago
5 to 10×
over budget on AI projects
35%
more tokens on the same prompts after one model change
98%
of FinOps teams now manage AI spend
Why post-billing analysis is too slow for AI
A single weekend of a runaway agent loop or an over-permissive AI feature can double your monthly spend. Detection cadence is the difference between a $10K incident and a $100K one.
| Capability | Post-billing FinOps | RosettaOps for LLM Spend |
|---|---|---|
| Enforcement point | Dashboard alert | Cloud API refuses the call |
| Detection cadence | Hours to days after spend | Within five minutes, continuously |
| AI spend counts against the budget in real time | Not supported | Included |
| Per-project model restriction | Not supported | Included |
| Token audit trail | Aggregated to daily | Per call, queryable, FOCUS 1.3 native |
| Data residency | Vendor data lake | Customer's own cloud account |
| AI-generated bad SQL detection | Not supported | Native rule |
Who buys this
SaaS firms with AI features
Margin per product line is your KPI. AI cost is now five to fifteen percent of cost-of-goods-sold. Per-customer AI cost surfaced live changes which features you ship and which you price-tier.
AI-first startups at Series A and beyond
GPU spend burns runway and model bills compound. Engineering teams want per-user LLM quotas and audit, not finance reports. Closed-loop AI cost governance gates every model call before it hits the bill.
Regulated enterprises piloting AI
Audit trails, model restrictions, and customer-owned data are non-negotiable. RosettaOps ships compliance scanning across ten standards alongside the AI governance layer. Pilot rollout to a single team is a two-week motion.
Catch AI cost before the bill
Book a 15-minute demo. We will show your exact AI spend by user, project, and model in your own cloud account.
Common questions
How do I track LLM cost per user rather than per account?
Users work in their own sandboxed cloud accounts, so the account budget is their budget. Token cost is priced within five minutes of the call and drawn down against it alongside everything else they run.
Can I stop a team going over its AI budget?
Yes, and not with an alert. When the budget is reached the account permissions change, so the next model call fails at the cloud rather than in our console. That holds whether the call comes from an application, a notebook or a script.
We already run an AI gateway. Does this replace it?
No. A gateway governs the calls that reach it, and anyone with their own cloud account can go straight to the model instead. We close that route at the cloud, so the approved path is the only path. Keep the gateway you have.
Can I control which models each team is allowed to call?
Yes. Model access is set by role, so production can be restricted to approved models while a sandbox keeps the experimental ones. Cost per customer stays predictable because model selection stays predictable.
Where is our AI usage data stored?
In your own cloud account, queried by your cloud's own engine. We hold a rolling forty eight hour cache and nothing beyond it, all of it rebuildable from your own trail.