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Service Connectivity Layer for managing fleet of clusters for better application performance

Multi - cluster chargeback by application and teams

Service gateway for multi-cloud applications

Enables creation of a virtual cluster that allows pods to be directly interconnected across distributed clusters.

KubeSlice

Predictive autoscaling based on application behaviors

Predictive autonomous scaling of pods and nodes

Reduce your cloud costs from 20-70% with continuous predictive autoscaling of Kubernetes resources driven by AI

Single/Multi-cluster and multi-cloud GPU provisioning and management platform

Elastic GPU Service

KubeAgent adds intelligence and autonomy to Kubernetes

KubeSlice Enterprise released version 1.15

Smart Scaler released version 2.13

Elastic GPU Service released version 1.12.0

Customers

Explore Resources for Elastic GPU Service

Navigating Key Metrics for Growth and Success

Source for Trends, Tips, and Timely Topics

The Blueprint for Mastering Tools and Processes

Success stories from our valued customers and partners

Bringing You the Top Stories as They Happen

Explore Our Library of Informative and Entertaining Clips

Exploring Critical Topics with Authoritative Research

Easily Track and Maximize Your Investment Returns

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Join Our Team and Shape the Future Together

Connecting You to Trends, Tools, and Thought Leaders

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Join us at KCD Costa Rica 2025, Saturday May 3rd!

Read the New Blog on Avesha KubeAgent

Avesha Announces Breakthrough AI Scaling with Smart Scaler, Delivering Up to 3x Performance Gains

Download Gartner Research Report: How Platform Engineering Teams Can Augment DevOps With AI 

Supporting a broad spectrum of Reasoning & Inferencing use cases requires smart GPU orchestration via intelligent scaling and GPU allocation– Maximize ROI of GPU investments -  Maximize ROI of GPU investments

Beyond Model-Level Optimization

The demand for high-performance AI inference and training continues to skyrocket, placing immense pressure on cloud and GPU infrastructure. AI models are getting larger, and workloads are more complex, making efficient resource utilization a critical factor in cost and performance optimization. Enter Avesha Smart Scaler — a reinforcement learning-based scaling solution that dynamically optimizes GPU/CPU resource allocation for AI workloads, delivering unprecedented throughput gains and reduced inference latency.

Scaling AI Workloads Smarter: How Avesha's Smart Scaler Delivers Up to 3x Performance Gains over Traditional HPA

KubeSlice provides a robust framework for securing Kubernetes environments by implementing logical slices that segment workloads, enforce network isolation, and integrate Zero Trust principles. This whitepaper explores the security features of KubeSlice, including role-based access control (RBAC), network segmentation, and encrypted communication across clusters.

 Security of KubeSlice

Avesha’s Gen AI Smart Scaler is a next-generation Horizontal Pod Autoscaler (HPA) replacement that uses AI-driven predictive scaling to optimize pod readiness specifically for AI inferencing workloads. Unlike traditional reactive scaling, Smart Scaler anticipates demand patterns and scales pods proactively, dramatically improving throughput and reducing latency.

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Technical Brief: Avesha Gen AI Smart Scaler Inferencing End Point (Smart Scaler IEP)

Despite advancements in ML scheduling tools like KubeFlow, optimizing GPU and CPU usage remains difficult. Mismatches between resource management and workload orchestration cause idle GPUs: creating delays, and inefficiencies in large-scale setups. Current GPU allocation relies on manual adjustment and lacks dynamic adaptation. Without standardized GPU rating and sharing approaches, advanced ML schedulers still struggle with scheduling, leading to bottlenecks and resource waste.

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Elastic GPU Service (EGS) -- Workload Automation, Optimization, Cost Reduction, and Observability

Customers can reduce the cost of nodes by ~ 56% when they introduce both, Karpenter for node auto scaling on EKS and replacing HPA with Smart Scaler for the pod autoscaling microservices. Karpenter simplifies Kubernetes infrastructure with the right nodes. Smart Scaler simplifies Kubernetes using GenAI to autoscale pod based on Application behavior and infrastructure metrics.

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Evaluation of Karpenter With Smart Scaler

Effectively managing connectivity across multiple clouds and clusters has become a pivotal challenge for today's enterprises. The complexities of network architecture, combined with the limitations of existing solutions like Cilium, Skupper, and Submariner, call for a robust, scalable, and user-friendly connectivity solution. Avesha KubeSlice addresses the connectivity needs of multicloud and multi-cluster environments, setting a new standard for efficient and secure networking.

Improving Multicloud Connectivity with Avesha KubeSlice

In this paper, Avesha CEO Raj Nair describes in detail the Smart Scaler solution, which is designed to dynamically scale pods within cloud- native environments based on traffic predictions and microservice pod capacity  estimations. Smart Scaler is built on predictive analytics, Generative AI and Reinforcement Learning (RL), and it enables efficient management of resources, enabling organizations to meet fluctuating demands while minimizing waste and operational costs.

Smart Scaler: Revolutionizing Pod Scaling and Resource Efficiency

In the age of cloud computing, businesses are tapping into the sheer might of scalable and adaptable
infrastructures to tackle the ever-shifting demands of dynamic workloads. 

Digital Twins for Intelligent Predictive Remediation of
Cloud Infrastructure: Revolutionizing Auto-Scaling

In this application note, we describe various use cases for Avesha's innovative product portfolio namely KubeSlice, Smart Scaler and Global Load Balancer. Together, these products offer a complete virtualization of Kubernetes and the freedom to connect workloads that are run in different locations using a programmatic or GUI control.

Avesha Application Note

In this report we evaluate five edge computing use cases that are strong potential areas of focus, and evaluate the maturity, benefits and challenges of addressing each.

The market outlook for edge computing: evaluating five key use cases

Smart Scaler is an essential tool in any Kubernetes toolbox. While HPA (Horizontal Pod Autoscaling) by itself is a significant improvement over cloud auto scaling. But, without the power of Reinforcement Learning, horizontal pod autoscaling will never fully meet the needs of organizations that require the full power of Kubernetes Scalability. 
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Dive into the world of horizontal pod autoscaling with Reinforcement Learning and discover how it can revolutionize your scaling strategies.

Trouble Scaling Kubernetes? Try the Smart Way with Horizontal Pod Autoscaling with Reinforcement Learning

Explore the role of an application fabric as a concept that captures the essence of a declarative distributed deployment model that enables a high-velocity infrastructure, which is essential for business applications for modern applications

Application Fabric for
Infrastructure control

Application fabric is a surface area of applications to migrate across clusters and clouds. In this writeup, we examine the underlying issues and develop the key concepts behind an application fabric.

Role of an application fabric in hybrid cloud

For securing your Kubernetes clusters, isolating applications is key. An easy way to isolate critical applications and reduce the attack surface is to put them on a Slice using KubeSlice.

Are you getting the development agility you aspire for?

A multi-cloud or hybrid strategy gives enterprises the freedom to use the best possible cloud native services for application workloads.

Simplify your Hybrid/Multi-Cluster, Multi-Cloud Kubernetes deployments with KubeSlice

Multi-tenancy in Kubernetes is a way to deploy multiple workloads in a shared cluster with isolated network traffic, resources, user access, and last but not least control plane access.

How KubeSlice implements Multi-tenancy

Cloud native application delivery – and Kubernetes orchestration technology specifically – are finally past the early adoption territory where only new cutting-edge vendors dared to tread.

The Challenges of Multi-Tenant Kubernetes

Providing a meaningful security posture for Kubernetes environments is crucial for the enterprise, however, it proves to be elusive for many cluster administrators. As enterprises adopt a shift-left mentality relating to their security posture, KubeSlice offers the ability to provide application-based security guardrails during the build cycle, enabling a richer application security posture for their applications and microservices running on Kubernetes.

KubeSlice: The Application Security Zone Accelerating Continuous Deployment

Avesha Systems optimizes cost and latency across multiple Kubernetes clusters and cloud infrastructures through an application mesh with a multitenant environment for connectivity, authorization and workload direction.

Avesha: Slicing distributed cloud native networks to order

Adoption of cloud-native technology such as containers and Kubernetes is among the most disruptive forces in today's enterprise IT market. A growing number of organizations of all sizes and across industries are seeking cloud-native benefits including efficiency, developer speed and productivity, and application portability. Regardless of where they are on their digital transformation journey, enterprises can either take advantage of technology such as cloud native as widely as possible across their platforms, environments and applications, or risk getting left behind in the market by organizations more aggressively and broadly embracing innovation.

Kubernetes: It's not just for containers anymore

It might seem like a non-sequitur because of the tedious process involved in the traditional approach of modernizing monolith applications starting with breaking down the monolith into one or more microservices. The objective is to achieve a more resilient and flexible application deployment. However, the process of achieving this objective without significant development resources and time is a non-starter – that is, until now. In this article, we explore an alternative approach that offers the benefits of a modernized workload for monolith VM applications with little or no-coding.

Modernizing Monoliths – A new approach to an old problem

The web service landscape has galvanized toward a microservice-based SaaS that draws the parallel to the adoption of the Web in the early 2000s. However, underlying this massive transformation is a landscape of multi-cloud infrastructures with applications running over them designed to fit into the managed service platform of a particular cloud environment, unfit to the needs of globally distributed or disaggregated services.

Avesha Global Service Mesh Fabric

Create an intelligent multi-cluster/multi-cloud service mesh to automate your application connectivity

Global Application Slice for Managed Services

Avesha's automated RL-based Load Balancing (LB) optimizes performance and minimizes footprint, preventing overloading and low latency of servers. The Avesha automated LB solution assigns requests to servers of corresponding length, and optimizes utilization of servers with differing capacity limits. The RL-based LB automatically reacts to changes in server and network state, and takes actions to prevent high latency.

The Case for RL-based Load Balancing

The Avesha Application Slice customizes footprints to fit enterprise needs, using fewer integration points. The Application Slice comes with multiple benefits including reduced blast radius failover (increasing resilience), reduced air gap, segmentation, velocity, and simplified communication with increased reachability. Segmentation is key to increased security, and with an application split over multiple clusters, security is always the top priority.

Avesha Application Slice

The Avesha Smart Application Cloud Framework is one solution for faster deployment of services, easy integration, fluid workload mobility, run-time security and compliance, managed scalability, and an autonomous infrastructure.

Avesha Smart Application Cloud Framework

With Avesha’s Application Slice technology, each slice is strictly segmented and has a zero-trust security federation. This improves security, contains failovers, and prioritizes traffic within the slice. With Avesha’s Slice technology, there is a standby cluster that will re-route to the other slices in the active cluster when the one slice experiences a failure. This technology limits the amount of nodes that are wasted capacity and greatly improves database resiliency compared to a normal cloud deployment.

Database Resiliency with Avesha Application Slice Technology

Head-to-Head gamers have incurred an unfair disadvantage due to latency as a result of the distance between the gaming server and the AWS cloud. The Avesha Smart Application Cloud addresses this issue of latency with an overlay of application slices which edgify applications, and reduces high latency through segmentation technology to ensure fair advantages for all gamers.

Can mobile games be completely fair?

Avesha's Smart Application Cloud platform uses the core Avesha technology called an Application Slice – which is an overlay, where the underlay of various networks disappears from the application view. This simplification will help prevent increased inertia towards the idea of edgefication and disaggregation. Reducing such inertia is what will allow for a widespread adoption of edge technologies that assist in a multitude of applications from medical to gaming services.

Application Disaggregation and "Edgeification"

With Avesha's Smart Application Mesh, doctors are provided with a 'second set of eyes' to detect polyps in colonoscopy procedures with a 95% accuracy rate. This platform provides real-time results with features allowing for remote specialists to assist while not being physically present. It allows for hands-free voice commands by physicians and nurses using Voice NLP (Natural Language Processing) and automated reporting using Robotic Process Automation (RPA). 

Edge Video Inferencing & Doctor-to-Doctor collaboration for Medical Procedures

Avesha's Smart Application Cloud platform uses the edge in processing AI workloads and reducing high latency in a clinical setting specifically during colonoscopy procedures. This benefit to doctors and nurses greatly reduces errors, adds AI assistance, and provides better patient care through NLP (Natural Language Processing), and Automated Report technology.