AI Spend Anomaly Detection Five Signals, Three Tracks, Zero Blind Spots
SAVI's multi-layer detection stack runs five independent anomaly signals on every event, combines them into Cost, Reliability, and Risk track scores, and fires plain-language alerts in under 30 seconds - not on your monthly invoice.
Get the right visibility into AI spend anomalies - before they become emergencies
SAVI gives you the anomaly detection you need to gain a complete understanding of AI spend across every model, team, and project - and to catch cost spikes the moment they form.
See all your AI spend data across 8+ providers in one real-time dashboard
A FinOps team can't govern what it can't see. SAVI normalises cost data across providers into a single spend view with consistent units, currency, and taxonomy.
Build trust with accurate, normalised data - refreshed every 5 minutes
Everyone - CFO, FinOps lead, and engineering manager - can see where AI costs are generated, which models are driving them, and whether spend is within policy.
Best-in-class granularity with 18-24 month data retention
Hourly, daily, weekly, quarterly visibility - ensuring accurate seasonality and trend modelling for even long-cycle workloads.
Eliminate blind spots in your AI spend
A FinOps team can't govern what it can't see. SAVI normalises cost data across OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex, Cohere, and more - into a single spend view with consistent units, currency, and taxonomy. With SAVI, everyone - CFO, FinOps lead, and engineering manager - can see where AI costs are generated, which models are driving them, and whether spend is within policy.
- Cross-provider cost normalisation - apples-to-apples model comparison
- 5-minute data refresh - not daily batch jobs from provider billing APIs
- Drill down from org-level spend → team → project → model → individual request
Spend Anomaly Monitor - Anomaly detected Friday 11:43 PM · OpenAI GPT-4o · 312% above baseline
| Track | Signals | Trigger | Action |
|---|---|---|---|
| Cost | Token explosion (50%) + Spend rate (30%) | Over-budget pipeline | Flag FinOps - soft alert |
| Reliability | Velocity (60%) + Tool thrashing (25%) + Loop depth (15%) | Runaway agent | Circuit flag - incident |
| Risk | Intent anomaly + PII flags | Unexpected patterns | Escalate compliance |
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Three-track composite scoring
Three-track composite scoring: Cost, Reliability, Risk
SAVI's composite scorer separates signals into three independent tracks - each representing a different class of problem requiring a different response. Cost track (token explosion + spend rate + budget rate): an expensive but stable pipeline going over budget triggers a soft FinOps alert. Reliability track (velocity + tool thrashing + loop depth): a runaway agent behaviourally breaking down triggers a circuit flag and reliability incident. Risk track (intent cluster anomaly + PII flags): unexpected prompt patterns or sensitive data exposure escalates to compliance. When multiple tracks breach simultaneously, SAVI applies a signal-agreement interaction penalty - a medium velocity + medium tool thrashing together score higher than either alone.
- Cost track: expensive but stable pipeline → soft FinOps alert
- Reliability track: agent behaviourally breaking down → circuit flag + reliability incident
- Risk track: unexpected prompts or PII exposure → compliance escalation
Per-workload thresholds: RAG, agent-loop, batch, chat
A static 3× baseline threshold fires constantly on RAG pipelines (high token output by design) and misses broken agents in chat workloads (where 3× is already extreme). SAVI applies workload-specific thresholds based on the workload_type tag set at SDK initialisation. All thresholds are config-driven in config/alert_thresholds.json - no code change required to add new workload types. Calibration runs nightly; Redis caches the percentile baselines for < 1ms lookup at ingest time.
- workload_type: rag - loose thresholds (high output by design); agent_loop - tight thresholds (any ER > 3 is suspect)
- workload_type: batch - very loose (legitimate volume); chat - standard consumer interaction patterns
- All thresholds config-driven - no code change to add new workload types
| Workload | Velocity (warn/crit) | Token ER (warn/crit) | Why |
|---|---|---|---|
| rag | 4.0σ / 7.0σ | 6.0 / 12.0 | High output by design |
| agent_loop | 2.5σ / 4.0σ | 2.5 / 5.0 | Any ER > 3 is suspect |
| batch | 5.0σ / 10.0σ | 8.0 / 20.0 | Legitimate high volume |
| chat | 3.0σ / 5.5σ | 3.0 / 6.0 | Standard patterns |
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Workload-type calibrated thresholds
Budget Caps · June 2026 · Total AI Spend $47,230 of $50K cap
Auto-configure budget caps across every AI provider
FinOps teams can set monthly and weekly spend caps per provider, model, team, or project - and have SAVI automatically alert or block when they're breached. No more discovering overruns on invoices three weeks after they happened. Accurately track spend against caps in real time, assign caps by business dimension, and surface unallocated spend the moment it appears - before it becomes unallocated overspend.
- Soft caps (alert) and hard caps (auto-block) per provider and model
- Allocate 100% of AI spend to business dimensions without gaps
- Forecast vs actual variance - know which providers are trending over cap
Drive unit economics at the team level
SAVI helps you understand cost-per-team, cost-per-product-feature, and cost-per-outcome - aligning every AI dollar to the business dimensions that matter to your CFO and board. With SAVI, FinOps teams can calculate AI unit costs and answer questions like: "For each $1 spent on AI-assisted support, how many tickets did we deflect?"
- Team-level budget ownership - each team lead sees their own spend dashboard
- Cost-per-product dimension - cost per API call, cost per user, cost per feature
- Generate a CFO-ready budget variance report in under 5 minutes
| team | models | spend | utilisation |
|---|---|---|---|
| Engineering Platform | GPT-4oClaude Sonnet | $18,420 | 78% |
| Customer Support | Claude HaikuFlash | $9,840 | 52% |
| Marketing | GPT-4o-miniGemini | $7,120 | 95% |
| Finance & Compliance | Claude OpusGPT-4o | $5,290 | 38% |
| Operations | Bedrock Titan | $2,560 | 22% |
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Team Spend · June 2026 · Total (5 teams) $43,230 of $50K · ↓ 8% vs May
Alert Rules · Active
Enable accountability and build trust across teams
At its core, AgentOps is about establishing a culture of shared responsibility between finance, engineering, and executive leadership. Alerts that reach the right person at the right time - with context - drive the accountability that makes governance work. With SAVI, FinOps teams can implement Showback to give each team visibility into their AI spend, and integrate that data into internal financial reporting systems for accurate Chargeback.
- Route alerts to the right team automatically - not just to the FinOps inbox
- Integrate spend data into your internal financial reporting systems via API
- Full Showback and Chargeback with team-level cost breakdowns
In short: Most tools fire one alert when one number crosses a line. SAVI runs five different checks on every single AI call, scores them into three categories (Cost, Reliability, Risk), and tells you in plain English which one fired and why - in under 30 seconds, not on next month's invoice.
Anomaly Detection
Do you know what's driving costs in your AI environment?
6 questions. Answer honestly.
Can you see what caused your AI costs to spike last week - in one view, not a spreadsheet?
Is your AI spend data current within the hour - or are you working from yesterday's numbers?
Can you tie AI spend directly to business outcomes - product features, revenue lines, customer segments?
Does each stakeholder - CFO, engineering lead, ops manager - have the view they need without requesting a custom report?
Can you detect a cost anomaly across providers the moment it starts - not days after it appears on a bill?
Do you know your AI unit economics - what each customer interaction, document, or decision actually costs to run?
This checks one slice of your AI estate. Take the full AI Cost Ownership Assessment →
Your AI gap score
Answer to begin
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Detection that learns. Alerts that explain. Budgets that enforce.
Schedule a discovery call to see SAVI's multi-layer anomaly detection and budget caps in action - with live data from your environment in under 10 minutes from signup.