Cost attribution
Spend resolved to individual user, project, application, and task type — not just the account. Every call is matched from your native cloud logs to the team and workload behind it, so each line of the AI bill has a clear owner.
AI Cost Intelligence, Multi-Cloud
Read from data you’re already logging across Bedrock, Azure OpenAI, and Vertex AI. No agents, no SDK wrapper, no code changes.
Built by Lauren Group · AWS Advanced Consulting Partner (Gen AI Competency)
The customer problem
“Our Bedrock bill is climbing and we can’t explain it.”
“Finance is asking which team is spending what.”
“We think we’re over-modelled but can’t prove it.”
Real account, real numbers
$0Monthly Bedrock spend analyzed
0%Of spend identified as addressable
$0Optimized monthly cost
0 weeksFrom assessment to plan
- Restrict premium models to approved use casesNone$433
- Enable Intelligent Prompt RoutingLow$492
- Route docs and email traffic to Nova LiteLow$260
- Enforce output token capsNone$46
How it works
No agent, no SDK wrapper, no proxy in front of any provider. The data is already in your accounts.
- 01
Native cloud logs
CloudTrail for Bedrock, Azure Monitor for OpenAI, Cloud Audit Logs for Vertex AI — already being recorded in your accounts.
- 02
Your own storage
Events delivered to storage you own in each cloud. The data never leaves your estate, on any provider.
- 03
Unified analysis engine
Enrichment, pricing, attribution and optimization modeling applied across every provider in one pipeline.
- 04
One dashboard
Near-real-time view across cost, usage, security and anomalies — consolidated across every cloud you run AI on.
One tool. Every cloud you run AI on.
- Amazon Bedrock
- Azure OpenAI
- Google Vertex AI
Built for every seat at the table
For Platform & IT teams
Attribute every dollar to the user, project, application, and model that caused it. Enforce model-access and token-cap policy. Get a ranked, effort-scored remediation plan instead of a raw bill.
One pipeline, full control
Optimization engine
Prompt caching, batch inference, cross-region inference, and provisioned-throughput right-sizing — named techniques, not just a model swap, with the saving quantified before anything changes.
Proactive policy controls
Block leaked secrets, mask PII, or auto-downgrade at a budget threshold — plus quota and throttle monitoring with alerting, so limits surface before they block you.
Security & governance
Findings surfaced from the same log pipeline — no second collection to build.
Access control & data privacy
SSO-based group segregation, metadata-first logging, and opt-in prompt retention — the same principles we ship to your own customers.
Spend forecasting
Projected month-end spend tracked against actual burn, refreshed continuously.
Multi-cloud consolidation
Model-to-model cost mapping across Bedrock, Azure OpenAI, and Vertex AI, with a consolidation decision framework.
Migration cost assessment
See what your usage would cost on another cloud before you move a workload — then let the built-in scanner rewrite the calls for you.
Enterprise deployment
Deploys as a template into your own AWS account — Control Tower compatible, multi-account and OU aware from day one.
From report to control layer
Recommend, Notify, Require approval, or Auto-act — chosen per policy, every action written to an audit log.
Recommend
Surfaced as a ranked action. Nothing changes until someone acts on it.
Notify
The right team is alerted the moment a policy is crossed.
Require approval
Routed to Slack, Teams, or email before anything executes.
Auto-act
Executes immediately — block, downgrade, or reroute — fully logged.
| Trigger | Action | Enforced at |
|---|---|---|
| Leaked key or secret in a request | Block the request, alert security | Gateway guardrail |
| PII detected in a prompt | Block or mask | Gateway guardrail |
| User or team crosses spend threshold | Auto-block or route for approval | Gateway policy |
| Budget reaches 80%, then 100% | Notify, then block or downgrade model | Gateway policy |
| Sudden spend spike (e.g. runaway agent) | Throttle and alert | Anomaly detection |
Runrait pricing
Find out how much of your AI spend is recoverable, then keep the fixes running with approvals, budgets and enforcement.
Assessment
Free
One run on your own usage, results in 24 hours
One read-only run of Runrait on your AI usage logs. Within 24 hours, see where your spend goes and how much of it you can recover.
What you get
- One run on your Amazon Bedrock, Azure OpenAI or Google Vertex AI logs
- Results delivered within 24 hours
- Spend attribution by user, project, application and model
- Recoverable-spend estimate for your account
- Ranked fixes with the saving for each
- Findings readout with your platform and finance teams
- Read-only access. No agents, no SDK wrapper
Control
Priced on your AI spend
30-day trial, then a monthly subscription scaled to spend
Runrait running continuously on your logs, with a live dashboard, priced fixes and controls that act on them.
Everything in Assessment, plus
- Live dashboard, refreshed continuously
- Cost per request and model-level trends
- Showback and chargeback reports by team
- Ranked actions, repriced as your usage changes
- Budgets and spike alerts to Slack, Teams or email
- Approval workflows before any policy change
- Bedrock, Azure OpenAI and Vertex AI in one view
- Business-hours support
Enterprise
Custom pricing
Scoped to your estate and support needs
Runrait with Lauren Group behind it: round-the-clock support and hands-on consulting for AI spend at scale.
Everything in Control, plus
- 24/7 support with a named escalation contact
- Consultation with Lauren Group cloud architects: quarterly cost reviews and model-selection guidance
- Assisted implementation and onboarding
- Auto-act enforcement: block, downgrade or reroute by policy
- Per-customer margin attributionRoadmap
- Human vs agent traffic splitRoadmap
- Custom policies and API access
Read-only access to your cloud provider logs — Bedrock, Azure OpenAI, or Vertex AI. A quantified optimization plan out — plus a live dashboard in your own environment, yours to keep.