Avesha provides a technology platform that enables efficient management and orchestration of application workloads across cloud, multi-cloud, and edge environments using predictive algorithms and automated scaling. The platform addresses high Kubernetes costs and inefficient GPU utilization by optimizing resource allocation and performance in real-time.
Funding
$21.2M 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.
Founders
Product
Problem
Managing application workloads across diverse environments like cloud, multi-cloud, and edge computing introduces complexities in resource allocation and orchestration. Kubernetes environments often suffer from high costs and inefficient GPU utilization, hindering optimal performance and cost-effectiveness.
Solution
Avesha offers a platform that leverages predictive algorithms and automated orchestration to optimize application workload management across cloud, multi-cloud, and edge infrastructures. The platform addresses Kubernetes cost challenges and GPU inefficiencies by dynamically allocating resources and scaling performance in real-time. Avesha's Elastic GPU Service (EGS) provides unified orchestration for AI workloads, balancing CPU and GPU resources to maximize efficiency. By intelligently managing workloads, Avesha enables cost-effective scaling and enhanced performance for AI training, inference, and real-time applications. The platform's AI Agent Studio and KubeAgents add an intelligence layer, transforming Kubernetes clusters into self-optimizing fleets that predict demand and right-size resources.
Target Audience
Avesha targets enterprises and AI cloud providers seeking to optimize Kubernetes operations, reduce costs, and improve application performance across diverse environments, including cloud, multi-cloud, and edge deployments.
Features
- Predictive autoscaling based on application behaviors using Smart Scaler
- Multi-cluster chargeback by application and teams with KubeTally
- On-demand cloud capacity for datacenters via KubeBurst
- Service gateway for multi-cloud applications through KubeAccess
- Elastic GPU Service (EGS) for multi-cluster and multi-cloud GPU provisioning and management
- AI Agent Studio and KubeAgents for intelligent and autonomous Kubernetes operations
- Real-time observability and monitoring to eliminate inefficiencies and bottlenecks
- Dynamic scaling that provisions GPUs precisely when and where they’re needed
- Cross-cloud flexibility to orchestrate workloads across multiple cloud environments
- Automated provisioning and self-healing systems for streamlined operations