Kennari offers an AI‑driven platform that continuously monitors multi‑cloud workloads and automatically enforces best‑practice configurations for cost, performance, and security. Its machine‑learning engine detects anomalies, recommends right‑sizing actions, and remediates compliance drift in real time, while a unified dashboard provides visibility and control across AWS, Azure, and GCP.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often struggle with the complexity of managing multi-cloud environments, leading to inefficient resource usage, unexpected costs, and security compliance challenges.
Solution
Kennari provides an AI-driven platform that continuously monitors cloud workloads and automatically enforces best‑practice configurations. The system uses machine‑learning models to detect cost anomalies, recommend right‑sizing actions, and remediate security drift in real time. Users can define policy intents through a simple interface, and Kennari translates them into automated provisioning, scaling, and compliance checks across major cloud providers. All actions are logged and visualized in a unified dashboard, giving teams visibility and control without manual scripting.
Target Audience
Primary customers are DevOps engineers, cloud architects, and IT operations teams in mid‑size to large enterprises that manage multi‑cloud infrastructures.
Features
- Continuous, AI‑powered analysis of resource utilization, cost, and security posture across AWS, Azure, and GCP
- Automated right‑sizing and idle‑resource termination to reduce cloud spend
- Real‑time compliance enforcement with customizable policy templates and auto‑remediation
- Anomaly detection engine that alerts on unexpected usage patterns or potential breaches
- Centralized dashboard with drill‑down visualizations and API access for integration with existing CI/CD pipelines
- Multi‑cloud orchestration layer that abstracts provider‑specific APIs into a single control plane