Mavvrik is a unified financial control platform that aggregates cost, usage, and telemetry data from public clouds, private GPU clusters, Kubernetes, and SaaS services to give enterprises clear visibility into AI and hybrid infrastructure spend. It automatically normalizes billing signals, calculates true unit economics, and provides real‑time chargeback, allocation, and forecasting to help finance, IT, and FinOps teams prevent cost surprises and protect margins.
Funding
$6.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
4OSVFounders
Product
Problem
Enterprises struggle to gain clear visibility and control over spending on AI models, GPUs, multi‑cloud services, and SaaS tools. Traditional FinOps and spreadsheet approaches cannot attribute costs to specific products, features, or customers, leading to margin erosion, unexpected spend spikes, and difficulty forecasting AI‑related expenses.
Solution
Mavvrik offers a unified platform that aggregates cost, usage, and telemetry data from public clouds, private infrastructure, GPU clusters, Kubernetes, and SaaS services into a single financial control center. The platform automatically normalizes disparate billing signals, calculates true unit economics for each workload, and provides real‑time chargeback and allocation across departments, projects, and customers. Users can set guardrails and forecasts for GPU and model costs, receive alerts on cost anomalies, and generate transparent reports for finance, IT, and FinOps teams. By integrating with existing monitoring and cloud tools, Mavvrik eliminates manual data stitching and enables proactive margin protection and scalable AI investment decisions.
Target Audience
Primary customers are finance, FinOps, and IT teams at mid‑size to large enterprises that run AI workloads across hybrid multi‑cloud environments and need granular cost attribution and forecasting.
Features
- Automated ingestion of cost and usage data from major public clouds, on‑prem GPU clusters, Kubernetes, and SaaS platforms
- Real‑time unit‑economics calculations for AI models, features, and customers, supporting cost‑to‑serve and cost‑to‑outcome analyses
- Policy‑driven chargeback and allocation engine that distributes spend across teams, projects, and billing entities with full audit trails
- Forecasting and guardrail modules that predict GPU and model expenses before deployment and trigger alerts on budget breaches
- Integrated dashboards and API endpoints for finance, IT, and engineering to visualize spend, margins, and trend analytics
- Support for custom cost schemas, allowing external cost streams to be ingested and governed alongside native cloud data