RosettaOps™ for LLM Spend
Real-time LLM and AI cost governance
Govern Amazon Bedrock and AI spend
while it is happening
AI is your fastest-growing line item, and only about one organisation in nine can forecast it to within 10%. 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
Each user’s sandboxed account budget covers AI too. Calls are priced within five minutes, and when the budget runs out, model access is removed at the cloud within minutes, not at a proxy.
Per-project model restrictions
Choose which models each project, team or environment can call: approved ones in production, experimental ones in sandboxes.
Token spikes seen as they happen
Input and output tokens are tracked continuously on Amazon Bedrock, agents running on Bedrock AgentCore included, so a runaway agent loop or a burst of retries shows up in live spend within minutes, not on next month’s bill.
Training jobs on the budget
Training and fine-tuning jobs running past their budget surface in real time, and GPU machines fall under the same idle rules as any other machine: a GPU machine left running is the most expensive idle resource on a bill.
Live token audit trail
Who called which model, when, with how many tokens and at what cost: an audit trail for compliance, support and incident review.
Bedrock Sandbox
A governed space for evaluation and prototyping, with budgets, model restrictions and an audit trail set by default. New engineers get access without anyone 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 person may call, one role per model, set once from one console.
2. Enforce
Cap at the cloud API
When budget is reached, the cloud account's permissions change, and model calls fail at the provider's API within minutes, not at our middleware.
3. Detect
Live token tracking
Token and GPU cost tracked continuously against budget, so spikes and runaway agents show up as they happen, not when the bill closes.
4. Remediate
Stop runaway spend
GPU machines stop at the budget threshold, with data kept where the service allows. Every automatic fix can be undone, and how to undo it is documented.
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, and costs also export in FOCUS 1.3.
Model access is a role
One role per model; a person’s roles add up
Cloud permissions
Written into the account by RosettaHub
A model is called
From a notebook, an app or an agent
Not allowed: refused by the cloud
No gateway to go around
Allowed: it runs, priced in minutes
On the cloud’s own AI service, from the same budget as compute and storage
Budget reached
Access removed, or an alert
Public reporting, 2026
11%
of organisations forecast AI spend to within 10% (Mavvrik and Benchmarkit)
50%
of generative AI initiatives expected to exceed their budgets by 2028 (Gartner)
30%
more tokens for the same text after one model change (Anthropic)
98%
of FinOps teams now manage AI spend (FinOps Foundation)
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 | Every call, by user, account, model and time, queryable in your account |
| Where the data is held | Vendor data lake | Customer's own cloud account |
Who buys this
SaaS firms with AI features
Margin per product line is your KPI. 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 AI budgets and an audit trail, not finance reports. Every call is priced within minutes and drawn from the same budget, and which models a team may call is a cloud permission.
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. A pilot with one team takes about two weeks.
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 is Amazon Bedrock priced?
Mostly per token: you pay for the input and output tokens of each call, at a rate that depends on the model, with provisioned throughput and batch as alternatives. RosettaHub prices each call within five minutes and draws it from the same budget as compute and storage.
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 48-hour cache and nothing beyond it, all of it rebuildable from your own trail.