AI Spend Anomaly Detection Alerts That Explain Themselves
SAVI watches call rate, output size and spend rate on every AI event, scores them by type of problem, and sends alerts in plain language as events arrive, not on your invoice.
AI spend problems are cheapest to fix when they start
SAVI shows where AI cost comes from across providers, teams and models, and flags unusual spend while it is still small.
One view across providers
SAVI brings spend from the major providers into one view with consistent units and currency, so finance and engineering look at the same numbers.
Everyone sees their part
The CFO, the FinOps lead and the engineering manager can each see where cost comes from, which models drive it, and whether spend is within policy.
Baselines that fit the workload
Retrieval pipelines, agents, batch jobs and chat behave differently. Alerts use thresholds set for each type, so normal behaviour does not raise noise.
See spend across providers in one place
SAVI brings cost data from the major AI providers into a single view with consistent units, currency and categories. Everyone can see where AI costs come from, which models drive them, and whether spend is within policy.
- Compare models on the same footing
- Spend refreshed throughout the day, not once a month with the bill
- Drill down from the organisation to team, workflow and model
Example data. Spend anomaly monitor, Friday 11:43 PM, 312% above baseline
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Live tracks today. More signals are in testing.
Scoring that tells cost problems from reliability problems
SAVI groups signals into tracks, so each type of problem gets its own response. The cost track looks at output size and spend rate: an expensive but stable pipeline going over budget raises a soft alert for FinOps. The reliability track looks at call rate: an agent that suddenly calls far more often than its own history raises a circuit flag. When signals agree, the score rises, so a critical alert rests on more than one clue.
- Cost track: an expensive but stable pipeline raises a soft alert for FinOps
- Reliability track: an agent behaving unlike its own history raises a circuit flag
- Agreement between signals raises the score
Thresholds that fit each workload
A single 3x threshold fires constantly on retrieval pipelines, where large outputs are normal, and misses broken agents in chat, where 3x is already extreme. SAVI applies thresholds set for each workload type, which you tag when you set up the SDK. They live in configuration, so a new workload type needs no code change, and baselines recalibrate daily.
- Retrieval: looser thresholds, because large outputs are normal
- Agents: tighter thresholds, because a high output ratio is suspect
- Batch and chat have their own settings
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Example thresholds by workload type
Example data. Team caps, June 2026, $47,230 of $50K
Set budget caps and alerts per team
Set monthly or weekly spend caps per team or pool, and get alerts as they are approached and crossed. Blocking is an opt-in that you can switch on after a cap has run in trial mode, so you see its effect first. You find out about overruns as they happen, not on the invoice.
- An alert at 80% of a cap, and again when it is crossed
- Opt-in blocking after a trial period
- Projected month-end spend shown against each cap
Unit economics by team
SAVI shows cost per team and per workflow, and cost per outcome where you record outcomes, so AI dollars line up with the numbers your CFO and board care about. That helps answer questions like: for each $1 spent on AI-assisted support, how many tickets did we deflect?
- Each team sees its own spend
- Cost per workflow, and cost per outcome where you record outcomes
- Spend exported by team in a standard cost format
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Example data. Team spend, June 2026, 5 teams, $43,230 of $50K
Example alert rules
Accountability across teams
Alerts carry context: what fired, at what value, against what baseline. Send them to Slack, email, PagerDuty or a webhook so the right people see them. Showback gives each team a view of its own spend, and a standard-format export supports chargeback.
- Alerts to Slack, email, PagerDuty or a webhook
- Showback by team
- Spend export by team for chargeback
In short: Most tools fire one alert when one number crosses a line. SAVI checks several signals on every call, groups them by type of problem, and explains in plain English what fired and why.
Do you know what's driving costs in your AI environment?
5 questions. Answer honestly.
Can you see what caused your AI costs to spike last week, in one view and not a spreadsheet?
Would you hear about a cost anomaly the day it starts, not when the bill arrives?
Can you tie AI spend to business outcomes such as product features or customer segments?
Does each stakeholder have the view they need without asking for a custom report?
Do you know your AI unit economics: what each customer interaction, document or decision costs to run?
This checks one slice of your AI estate. Take the full AI Cost Ownership Assessment →
Your AI gap score
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Alerts that explain. Caps that protect.
Book a discovery call to see anomaly detection and budget caps working on your own data.