Topogy provides an AI‑powered FinOps platform that consolidates multi‑cloud, data, and generative‑AI billing into a real‑time cost model. It automatically tags resources, visualizes dependencies, and monitors LLM token usage, while AI‑driven recommendations and predictive forecasts help enterprises right‑size assets and prevent spend overruns. The solution delivers role‑based dashboards and APIs to align technical metrics with financial KPIs.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises with multi‑cloud, data, and generative‑AI workloads often face fragmented billing data, manual tagging of resources, and opaque LLM token consumption. This lack of unified visibility leads to unexpected cost spikes, over‑provisioned assets, and difficulty aligning infrastructure spend with business outcomes.
Solution
Topogy delivers an AI‑powered FinOps platform that aggregates cloud, data, and AI billing streams into a single, real‑time view. The system automatically maps service dependencies and tags resources, eliminating manual inventory work. Integrated token‑usage analytics provide granular insight into LLM consumption across providers, enabling precise cost attribution. AI-driven recommendation engines suggest right‑sizing actions, idle‑resource shutdowns, and optimal model selections to reduce waste. Predictive forecasting models alert teams to upcoming spend anomalies before they materialize. All insights are presented through purpose‑built dashboards that tie technical metrics to financial KPIs, supporting faster, data‑driven decision making.
Target Audience
Primary customers are FinOps, Cloud Operations, and AI product teams at mid‑size to large enterprises that manage multi‑cloud infrastructure and generative‑AI workloads.
Features
- Automated ingestion of multi‑cloud billing APIs and consolidation of line‑item data into a unified cost model
- Real‑time network topology visualization with AI‑generated tagging of services, dependencies, and cost attribution
- Unified LLM token usage monitoring across major AI providers, including per‑model performance and unit‑economics analytics
- AI recommendation engine that prioritizes idle resource termination, right‑sizing, and model cost‑optimization actions
- Predictive forecasting engine leveraging time‑series and machine‑learning models to flag potential cost spikes
- Pre‑built, role‑based dashboards that map COGS, vendor spend, and AI usage to business outcomes
- Extensible REST/GraphQL API for integration with existing FinOps tooling, CI/CD pipelines, and custom reporting layers
- Enterprise‑grade security with end‑to‑end encryption, RBAC, and audit logging for compliance requirements