Finout

Profile

Finout is an enterprise-grade Financial Operations (FinOps) platform designed to manage cloud spending across multi-cloud and SaaS environments. Founded in 2021 by experienced cloud technology professionals, the platform has established itself as a comprehensive solution for organizations requiring sophisticated cost allocation and optimization capabilities. Finout operates as a proprietary commercial SaaS platform serving major enterprises including Lyft, The New York Times, Choice Hotels, Wiz, and Tenable. The platform addresses the fundamental challenge of achieving complete visibility and accurate allocation of cloud costs in complex infrastructures spanning AWS, Google Cloud, Azure, Kubernetes, and various data platforms. Organizations typically achieve significant cost reductions and engineering time savings through automated waste detection and intelligent allocation mechanisms.

Focus

Finout solves the persistent challenge of cloud cost visibility and allocation in multi-cloud environments where traditional tagging approaches fail. Organizations struggle with incomplete cost allocation when resources remain untagged, shared infrastructure serves multiple teams, and billing data fragments across cloud providers and SaaS platforms. The platform enables finance teams, FinOps practitioners, and engineering leaders to understand true unit economics, allocate shared costs fairly, and identify optimization opportunities without manual spreadsheet reconciliation. Platform engineers benefit from cost awareness integrated into operational workflows, while finance teams gain forecasting capabilities aligned with business planning cycles. The solution particularly addresses Kubernetes cost allocation challenges where container abstractions don't map directly to billing dimensions.

Background

Finout was founded in 2021 by Roi Ravhon (CEO), Asaf Liveanu (Chief Product Officer), and Yizhar Gilboa (Chief Technology Officer), who previously collaborated at observability platform Logz.io. The founders' direct experience attempting to build in-house cost management tooling revealed the domain's complexity and motivated creating a specialized platform. Headquartered in Tel Aviv, Israel, Finout has raised substantial venture funding including a Series C round led by Insight Partners, bringing total investment to significant levels. The company acquired Cloudthread to integrate specialized optimization capabilities and added Mat Ellis, founder of Cloudability and FinOps pioneer, to its board of directors. The platform maintains active development with continuous feature releases and expanding team size supporting both product advancement and commercial growth.

Main features

Unified cost observability through normalized billing data

The MegaBill consolidates raw billing information from AWS, Google Cloud, Azure, Kubernetes, and SaaS platforms into a single normalized dataset, providing complete cost visibility without navigating multiple provider consoles. Unlike tools relying on pre-aggregated data, this architecture processes billing information at the most granular level available, enabling analysis at any organizational hierarchy from high-level views to individual resource details. The normalization converts each provider's unique billing format into consistent queryable fields, supporting dual tracking of both cost and usage metrics. This foundation enables downstream capabilities including allocation, anomaly detection, and forecasting to operate across the entire infrastructure rather than isolated cloud accounts.

Dynamic cost allocation without infrastructure modification

Virtual Tags enable instant cost allocation through rule-based logic applied to the normalized billing data, eliminating dependency on resource tagging at creation time. Organizations define allocation rules matching any billing field—account names, resource types, regions, or custom metadata—then combine these into logical groupings representing teams, environments, business units, or customers. Multiple Virtual Tags can coexist, allowing simultaneous allocation by different organizational dimensions without code changes or resource re-tagging. The system handles shared cost reallocation through telemetric-based distribution using external consumption data from sources like Prometheus, Datadog, or Snowflake, or through customized percentage-based splits matching governance structures.

Automated waste detection and optimization recommendations

CostGuard employs machine learning algorithms to continuously scan cloud infrastructure and identify cost optimization opportunities across compute, storage, and managed services. The system analyzes usage patterns to generate actionable recommendations including instance rightsizing, idle resource identification, over-provisioned capacity elimination, and commitment optimization for Reserved Instances. Recommendations include implementation effort levels and predicted cost impact, enabling prioritization based on effort-to-savings ratios. Integration with anomaly detection provides real-time cost spike identification by establishing baseline spending patterns from historical data, then alerting teams via Slack, email, or configured endpoints when deviations occur, accounting for seasonality and known infrastructure changes.