TeraOps provides an end‑to‑end cost attribution platform for AI workloads, breaking down cloud spend by token, model feature, customer, and application. Its deep discovery engine uncovers hidden cloud waste that standard monitoring tools miss, and a pre‑validated remediation engine lets engineers apply cost‑saving changes risk‑free, preserving production stability.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations running AI workloads on cloud platforms often lack detailed visibility into how resources are consumed, leading to hidden costs that standard monitoring tools miss. This obscurity makes it difficult to attribute spend to specific models, features, or customers and creates risk when attempting to optimize or eliminate waste.
Solution
TeraOps delivers end‑to‑end cost attribution for AI workloads, breaking down expenses by token, feature, customer, and application. By probing the “black box” of cloud infrastructure, it uncovers non‑obvious waste that typical consoles overlook. The platform then provides a pre‑validated, low‑risk remediation engine that lets engineers apply cost‑saving changes without jeopardizing production stability. Results are presented in a unified dashboard, giving teams clear unit‑economics insight and actionable recommendations to reduce cloud spend safely.
Target Audience
Primary customers are cloud engineering, DevOps, and AI product teams responsible for managing large‑scale AI workloads and optimizing cloud spend.
Features
- Granular cost breakdown per AI token, model feature, customer, and application
- Deep discovery engine that identifies hidden cloud waste beyond standard monitoring tools
- Pre‑validated remediation actions that can be applied risk‑free to production environments
- Safe execution engine that automates cost‑saving changes while preserving service reliability
- Unified visibility dashboard that consolidates cost data and remediation status
- Integration hooks for existing cloud consoles and CI/CD pipelines