Cost Governance
Per-tenant metering for every Airflow DAG, Langflow flow, and LLM call. Budget enforcement, anomaly detection, and FinOps dashboards with >99% cost attribution accuracy — so no bill is ever a surprise.
>99%
Cost attribution accuracy
Per-tenant, per-workflow, per-LLM-call
<5%
Budget variance
With anomaly detection + alerts
Zero
Surprise overruns
Hard-stop budget guardrails enforce limits
100%
Chargeback accuracy
Exportable per-tenant cost reports
Who It's For
Only 44% of organizations have AI financial guardrails in place today. Vharta closes that gap across every persona that touches workflow and AI costs.
FinOps / Finance Teams
The problem today
No way to attribute AI and workflow costs to individual teams or products. Surprise bills at month end.
With Vharta
Per-tenant metering with exportable chargeback reports. Every DAG run, every Langflow flow, every LLM token is attributed to a tenant with sub-1% variance.
Platform Engineers
The problem today
Teams exceeding resource quotas cause noisy-neighbor problems. No automated enforcement — only manual intervention after the fact.
With Vharta
Resource quotas and budget alerts enforced at the Kubernetes and OPA layer. Hard-stop limits prevent overruns before they impact other tenants.
CTOs / VPs Engineering
The problem today
Only 44% of organizations have AI financial guardrails in place. AI spend is growing 30%+ YoY with little visibility.
With Vharta
Board-ready cost dashboards aggregating workflow execution and LLM spend. Trend analysis, budget vs. actual, and anomaly alerts in one view.
Platform Capabilities
From metering to enforcement to dashboards — every layer of cost governance built in, not bolted on.
Every workflow execution is metered at the tenant level. CPU-seconds, memory-hours, and execution count roll up to FinOps dashboards with per-workflow and per-user breakdowns.
Every AI/LLM call through the Vharta gateway is attributed to a tenant, workflow, and user. Token counts, model pricing, and cumulative spend tracked in real time.
Z-score and MAD-based anomaly detection runs hourly. Unusual spend spikes generate classified alerts (low/medium/high/critical) before they become invoice surprises.
Set monthly or per-execution budgets per tenant. Soft alerts warn at 80% of budget. Hard stops at 100% prevent overspend — enforced at the OPA layer, not just logged.
Pre-built Grafana dashboards for cost breakdown by tenant, workflow, engine, and LLM model. Designed for FinOps analysts — no query writing required.
Vharta reconciles platform metering with your actual cloud bills (AWS, GCP, Azure). Detect variance, identify waste, and right-size tenant quotas with accurate attribution.
Attribution Granularity
Drill from platform-level totals down to individual LLM calls. Every level exportable for chargeback and showback reports.
Platform
Total cost across all tenants — for platform owners and finance
Tenant
Cost per team or customer — for chargeback and showback
Workflow
Cost per DAG, Langflow flow, or Agentic task
Execution
Cost per individual run — for debugging and optimization
LLM Call
Token count, model, and cost per AI inference
User
Cost attributed to the individual who triggered execution
See how Vharta's FinOps dashboards and per-tenant budget enforcement eliminate surprise bills and enable accurate chargebacks.