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In today's AI-driven landscape, enterprises face a critical challenge: ensuring consistent availability of GPU resources for mission-critical AI workloads without excessive infrastructure investments. This blog explores how Avesha's Elastic GPU Service (EGS) with Bursting capability solves this challenge through intelligent cross-datacenter resource allocation.

Maximizing AI Infrastructure ROI with Elastic GPU Service Bursting: A Business Use Case

We are thrilled to announce the official release of EGS version 1.14.0, now available as of June 5, 2025.

Announcing EGS EE 1.14.0 Release

Smart Scaler is an enterprise-grade solution designed to address the complexities of Kubernetes scaling and resource management. It provides intelligent autoscaling capabilities that go beyond traditional Horizontal Pod Autoscaler (HPA) by considering multiple metrics simultaneously and making scaling decisions based on advanced algorithms.

Announcing Smart Scaler 2.15.0

KubeTally is a resource and cost management feature tailored for Kubernetes environments, designed to enhance visibility, accountability, and optimization of resource usage and associated costs. By integrating seamlessly with Kubernetes clusters, KubeTally enables granular tracking and reporting of resource consumption, offering actionable insights to teams and organizations.

Announcing KubeTally EE 1.16.0 Release

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 Elastic GPU Service (EGS) enables organizations to dynamically expand GPU workloads across clusters and clouds, enhancing capacity and availability for AI and inference applications. When additional GPU resources are needed, EGS automatically bursts workloads to the most optimal cloud or data center based on performance, latency, or cost. EGS empowers enterprises to scale their AI operations flexibly for both short-term spikes and long-term growth, while maintaining control policies, multi-tenant access, and compliance with security and regional regulations.

Innovating AI Infrastructure by Automating GPU Workload Expansion

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We are thrilled to announce the official release of EGS version 1.16.0, now available as of May 21, 2025.

Announcing EGS EE 1.13.0 Release

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As AI workloads grow larger and more distributed, the need for scaling and bursting AI inference securely across clouds and clusters becomes critical. Yet scaling without strong security can open the door to vulnerabilities—especially now that inference happens across more dynamic, multi-cluster environments.

Scaling AI Inference Requires Security First: How KubeSlice Protects Distributed AI Workloads

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We're excited to announce the release of KubeSlice Enterprise 1.16.0, which is live as of May 12, 2025. This version introduces new features and enhancements designed to improve efficiency, security, and user experience.

Announcing KubeSlice Enterprise 1.16.0

We're excited to announce the release of Smart Scaler 2.14.0, our latest update that is live as of May 9, 2025 is aimed at enhancing predictive scaling capabilities in Kubernetes environments. This version introduces new features and improvements designed to optimize resource utilization, reduce costs, and provide better visibility into your cluster operations.

Announcing Smart Scaler 2.14.0

These are not your average chatbots or LLM wrappers. Ambient agents are autonomous, event-driven AI systems that operate continuously in the background, without requiring direct human prompting. They respond to changes in infrastructure, application signals, code pushes, telemetry, or policy shifts. They live inside the stack — often embedded in CI/CD pipelines, observability layers, security scanners, or workload schedulers. They don’t wait for you to ask; they act when the system changes.

Ambient Agents

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As enterprise AI teams move beyond training and into real-time production inference, they’re encountering a fundamental infrastructure problem: GPU resource management built for training doesn’t scale to inference.

Why AI Teams Need More Than Just GPU Scheduling - and How Avesha’s Elastic GPU Service Fills the Gap

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Kubernetes is the de facto standard for orchestrating containerized workloads. With the growing number of production Kubernetes deployments, continuous security is key to successful execution of applications.

KubeSlice: A way to secure Kubernetes cluster(s)

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Kubernetes (K8s) has emerged as the best orchestration solution for microservice deployments. However, fine tuning configurations to operate a cost efficient scaling that meets your SLO remains very challenging.

Smart Karpenter: AI Driven Cost Optimization Kubernetes (K8s) deployments 

AI SRE From Code Companion to 2AM On-Call Engineer

Let’s be real. At 2AM, when an alert goes off, the last thing you want is to open five dashboards, dig through logs, search Slack for tribal knowledge, and pray the runbook is still valid. But this is the reality for platform and SRE teams running production systems at scale.

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We are excited to announce the release of Elastic GPU Service (EGS) version 1.12.0, which is live as of April, 10th, 2025. This latest version introduces several significant features and enhancements aimed at optimizing GPU utilization and streamlining workflows for AI projects.

Announcing EGS Version 1.12.0

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We're excited to announce the release of Smart Scaler 2.13.0, our latest update to the intelligent Kubernetes scaling platform. This release brings significant improvements to scalability, usability, and security, helping organizations optimize their Kubernetes environments more effectively than ever before.

Announcing Smart Scaler 2.13.0: Enhanced Kubernetes Scaling and Management

In financial services, resiliency, performance, and regulatory compliance aren’t nice-to-haves—they’re non-negotiable.

Avesha: The AI-Powered Kubernetes Platform for Resilient, Scalable, and Compliant Financial Services

This week, the KubeCon EU 2025 keynote delivered a compelling snapshot of the trends driving the cloud-native landscape, spotlighting unified observability, platform engineering, and AI-driven automation as pivotal forces. As organizations navigate rising complexity—often juggling 6 to 15 observability tools—there’s a clear shift toward consolidation. Research shows 54% of companies are prioritizing unified platforms to streamline operations and dismantle silos, a trend that’s reshaping how enterprises approach efficiency and visibility.

Transforming Cloud-Native Ecosystems: Unified Observability, AI, and Automation Take Center Stage

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NVIDIA GTC 2025 (March 17–21, San Jose) offered a front-row seat to the next wave of AI—and walking the floor, we saw it taking shape. AI is charging to the edge, with on-device processing reducing latency—crucial for real-time AI Ops. Multimodal AI fuses vision with action, and points to a future of more streamlined infrastructure, while robotics demos showcased intelligence in the physical world.

Avesha’s NVIDIA GTC 2025 Trip Report

I have discovered recently the Kubeslice project ( Cloud Native Computing Foundation (CNCF) Sandbox Project) which provides an overlay layer 3 network solution for connecting Kubernetes clusters privately.

Multi-Cluster Networking using KubeSlice

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.

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

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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.

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

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Operating a distributed Kafka cluster on KubeSlice includes several steps to guarantee high availability and performance within your Kubernetes environment. This configuration can connect various clusters across different cloud regions and multiple clouds, including those that span data centers and the cloud.

How to Deploy a Resilient Distributed Kafka Cluster using KubeSlice

Elastic GPU Service (EGS): The Orchestrator Powering On-Demand AI Inference

The future of artificial intelligence demands infrastructure that’s dynamic, scalable, and ruthlessly efficient. Traditional systems—hampered by static resource allocation, fragmented pipelines, and one-size-fits-all scaling—are buckling under the weight of modern AI workloads. Enter Elastic GPU Service (EGS), the intelligent orchestrator powering the next generation of Mixture of Experts (MoE) and Inference and Reasoning as a Service (IRaaS). By unifying data, compute, and intelligence, EGS doesn’t just optimize AI—it redefines what’s possible.

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When DeepSeek’s trillion-parameter Mixture of Experts (MoE) model processes a query, it doesn’t brute-force its way through every neuron. Instead, it dynamically activates only the specialized “experts” needed for the task—a vision model for images, a reasoning engine for logic, or a language specialist for translation. This architecture slashes computational waste by 70% compared to monolithic LLMs. But there’s a catch: MoE’s efficiency hinges on a framework that can instantly match the right data to the right expert at the right time.

IRaaS: The Silent Revolution Powering DeepSeek’s MoE and the Future of Adaptive AI

At Elastic GPU Services (EGS), we’re redefining how organizations harness the power of GPUintensive workloads. With EGS, observability, orchestration, and automation work in unison to unlock unparalleled efficiency, scalability, and cost-effectiveness—all tailored for AI, ML, and high-performance computing. 

Transforming your GPU infrastructure into a competitive advantage

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In today’s cloud-native landscape, businesses are increasingly adopting multi-cluster, multi-cloud and hybrid-cloud strategies to achieve higher availability, improved disaster recovery, and enhanced scalability. However, managing distributed databases across multiple Kubernetes clusters can be challenging. Enter KubeSlice — a powerful solution that simplifies distributed MongoDB deployments across multiple clusters and clouds.

Building Distributed MongoDB Deployments Across Multi-Cluster/Multi-Cloud Environments with KubeSlice

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In today’s rapidly evolving cloud landscape, organizations frequently need to migrate Kubernetes services between different cloud providers while maintaining access to their existing managed cloud services. This presents a significant challenge for Site Reliability Engineers (SREs) and Platform Engineers: how do you maintain secure access to managed services private endpoints in the original cloud after moving workloads to a different cloud provider? Enter KubeSlice, a CNCF Network TAG Sandbox project that’s revolutionizing multi-cloud Kubernetes service migration.

KubeSlice: The Bridge to Seamless Multi-Cloud Kubernetes Service Migration

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Payments infrastructure demands precision in performance and cost efficiency, especially with workloads that must scale seamlessly to meet customer needs in real time. Kubernetes autoscaling is essential for these applications, but achieving fully automated scaling that accurately adjusts to dynamic traffic patterns and usage fluctuations can be challenging.

Optimizing Payments Infrastructure with Smart Karpenter: A Case Study

At Avesha, we know that real-time inference is critical to modern AI & GenAI applications. Whether processing large datasets, training models, or fine-tuning them in real-time, businesses need the ability to handle data with precision, scalability, and efficiency

Optimizing GPU Allocation for Real-Time Inference with Avesha EGS

In AI, Retrieval-Augmented Generation (RAG) is revolutionizing how organizations handle complex queries, delivering precise, context-aware responses by blending retrieval techniques with generative models.

Scaling RAG in Production with Elastic GPU Service (EGS)

Do You Love Your Cloud Credits? Here's How You Can Get More… Or…Let's discuss how you can SAVE MORE from your EXISTING pool of cloud credits.

Do You Love Your Cloud Credits? Here's How You Can Get More…

#1 Myth or Mantra of spike scaling – "throw more resources at it." Is there a better way? Imagine scaling up a microservice that’s upstream of a chokepoint without first investigating the bottlenecks. Picture simulating various traffic conditions to figure it out, all while struggling to maintain a fast product delivery schedule within budget constraints. How many of you grapple with these challenges every day?

#1 Myth or Mantra of spike scaling – "throw more resources at it."

Last week, I had a conversation with a DevOps leader from a $15B public company around "migration" or, in this case, "Cloud re-hosting." We concluded that most migrations are done wrong. His solution was to go for "gradual" migration. Here are 5 highlights from his approach.

Migration should be 'gradual' and 'continuous'

Are your APM tools solving problems or creating them? Is it time for a wake-up call in the world of Application Performance Monitoring?

The APM Paradox: When Solution Becomes the Problem

Hack your scaling and pay for a European Escape?  Do you feel you have paid for a lot of cloud capacity waiting for traffic. If so, you are not alone, according to a recent analysis [1] of AWS spend, the average resource utilization for compute across all AWS customers is just 6%, with more than 90% of compute capacity paid for and unused.

Hack your scaling and pay for a European Escape?

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I'm saying this from a real-world experience we recently had - a fintech leader dramatically reduced their Kubernetes costs by implementing some of these methods.

Here Are 3 Ways You Can Slash Your Kubernetes Costs by 50%

In cloud-native architectures, efficient autoscaling mechanisms are indispensable for optimizing resource utilization and ensuring optimal performance.

A completely new way for K8s Autoscaling: Why Predictive Pod Scaling with Smart Scaler and Karpenter is needed before plain VPA

To deploy complex application microservices at scale in Kubernetes and to control the dynamic expansion and contraction of microservices in response to changes in load, one must use an auto-scaler (such as Horizontal Pod Autoscaling (HPA)).

Cost Optimization

Streamlined data sharing, smooth communication, and a reliable and scalable solution to enhance operational efficiency and agility are essential for modern businesses to thrive. That's why many enterprises are constantly adapting KubeSlice to optimize their infrastructure and meet these evolving demands.

8 Quick Reasons KubeSlice is Your Trusted Business Ally

Kubernetes has become a cornerstone for automating container deployment, scaling, and management, earning widespread acclaim for its ability to foster agility, scalability, and crossenvironment portability for organizations around the globe. As Kubernetes continues to be embraced on a global scale, the issue of cost overruns has begun to surface more frequently, echoing concerns reminiscent of the early days of cloud computing adoption.

Maximizing efficiency and savings: Using Avesha Smart Scaler for cost-effective Kubernetes clusters in OKE

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Gone are the days of wrestling with complex multi-cluster setups. These steps will guide you through the easy and straightforward process to deploy KubeSlice on Linode, unlocking the power of Kubernetes management across multiple clusters.

Effortless Multi-Cluster Management: Setting Up KubeSlice on Linode

The landscape of cloud computing is evolving. Organizations are continuously looking for innovative solutions to boost connectivity. It streamlines operations and provides seamless data flow across diverse cloud environments.

What Is The Innovative Multi-Cloud Pod-To-Pod Network Solution?

Event-based Autoscaling: Ensuring Smooth Operations on Your Peak Days

Microservice Twins in Kubernetes - breaking new grounds

Bursting Workloads to the Cloud 

Deploying Kafka Brokers in Multiple Clouds with KubeSlice

In today's rapidly evolving tech landscape, the ability to seamlessly connect and manage multiple Kubernetes clusters is paramount.

Integrating Avesha's KubeSlice with Dell PowerFlex for Multi-Cluster Connectivity

Zero RTO for Kubernetes Disaster Recovery with KubeSlice

How multi-cloud app distribution just changed the future

No one can crash this game!

KubeSlice is an exquisitely simple way to get the best of both worlds. By creating a direct Service Connectivity between Kubernetes clusters or namespaces, KubeSlice obviates the need for manually configuring individual connectivity between Kubernetes clusters running Kafka instances.

Kafka Multi-Cluster Deployment on Kubernetes - simplified !

All you need to know about KubeSlice and its benefits

Learn how this comprehensive framework enhances scalability, performance, security, and resource optimization in a multi-cluster, multi-tenant environment. With KubeSlice Manager's user-friendly UI, managing large-scale applications and infrastructure becomes effortless, allowing organizations to focus on their core business activities with increased efficiency and effectiveness.

Disrupting retail with Generative AI and Edge

Elegance At The Edge

7 Common mistakes in a Multi-Cloud Journey

Your executive leadership launched a 'Cloud First' strategy with much fanfare a few months ago. At first it was simple and seemed inexpensive to move workloads to your primary cloud service provider.

KubeSlice, a powerful networking solution, facilitates secure and highly available connectivity between multiple Kubernetes clusters. By creating a flat overlay network, KubeSlice enables seamless communication between pods of an application running across different clusters.

A Step-by-Step Guide to Acquiring and Running KubeSlice Enterprise with a Trial License

The unenforceable and imaginary cloud SLA

The Cloud Problem No One Talks About

KubeSlice: It came, It saw, It conquered.

Once upon a time, in a world of dynamic container orchestration, managing a fleet of clusters was as difficult a task as commanding a fleet of warships at sea.

Ah Kubernetes! The panacea to all our DevOps challenges. The open source container orchestration tool that was supposed to speed up software delivery, secure our applications, lower our costs and reduce our headaches.

Kubernetes resiliency (RTO/RPO) in Multi-Cluster deployments

In today's digital age, large enterprises are increasingly realizing the potential of cloud computing for driving innovation, scalability, and cost optimization. Migrating from on-premises data centers to the cloud has become a crucial step in the digital transformation journey. 

Continuous Migration from Data Center to Cloud

Streamlining Worker Operator Installation

In the ever-evolving landscape of software engineering, streamlining processes and automating tasks are paramount to enhancing efficiency and productivity.

Streamlining the Worker Operator Installation

In the IT space today, customers often intermix Multi Cloud and Hybrid Cloud terms without necessarily understanding the distinction between them.

7 Considerations for Multi-Cluster Kubernetes

The battle for container orchestration began in the early 2010s with the rise of containerization technology. Containerization allows developers to package their applications into small, portable, and self-contained units that can run on any infrastructure, from a laptop to a data center.

What is Kubernetes Right-Sizing and should I care ?


Burst workloads and the gaming experience.

Bursting to Prevent User Revolts

The dream of an Autonomous Infrastructure - "You know what the happiest animal on Earth is? It's a goldfish. You know why? It's got a 10-second memory. Be a goldfish, Sam," said TV’s famous football coach Ted Lasso to Sam Obisanya, a player in his team.

"Be a Goldfish, Sam"

Take any list of the benefits of cloud computing, and at the top you'll likely find massive horizontal scalability.

Trouble Scaling Kubernetes? Try the Smart Way

Hybrid Multi Cloud A powerful paradigm for enterprises

A multi-cloud or hybrid strategy gives enterprises the freedom to use the best possible cloud native services for revenue generating workloads. Many organizations are utilizing multi-cloud deployment use cases like

Hybrid/Multi-Cloud 

Predictive Load Balancing For Cloud Cost Optimization

Modern applications are composed of individual microservices that need to run in one or more locations (cloud regions or edges). 

Why Ted Lasso’s Tuesday premiere needed the Edge, 
...and ubiquitous connectivity from KubeSlice 



Paging Passenger Lasso

Sustainable Cloud Computing:

 "Parting is such sweet sorrow," wrote William Shakespeare in Romeo and Juliet. The words ring true even today as we bid farewell to old manual ways of inter-cluster networking and welcome the new.

From Sorrow to Simplicity : Benefits of Unifying your Clouds with One Network

How to choose the best network for microservices?

It's time to address the elephant in the room. 

Introducing Sandbox: The pre-packaged playground for KubeSlice. Experience the universal secure connectivity and rapid and easy networking via the sandbox environment.

Trying out KubeSlice takes four clicks: KubeSlice Playground

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

Hybrid/Multi-Cloud

The advent of the "cloud-native" age has enabled the creation of open-compute platforms that allows
individual workloads[1] to be run in different locations based on properties that meet business objectives — lower cost, data governance, closer proximity to end users, resiliency or regulatory needs.

Role of an application fabric in hybrid cloud

Teams working on digital transformations aspire to deliver and deploy apps faster. Adopting containers, Kubernetes, and cloud-native architectures were supposed to ensure that teams get the speed and agility they need to meet customer demands.

From Monoliths to Microservices: Are you getting the development agility you aspire for?

Multi-tenancy in Kubernetes enables multiple workloads to share clusters securely, reducing costs and operational overhead while improving scalability and efficiency. Learn how tools like KubeSlice simplify multi-tenancy management across clusters.

How KubeSlice implements Multi-tenancy in Kubernetes

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

Simplify Kubernetes with KubeSlice: Open-source, secure multi-tenancy for hybrid and multi-cloud deployments. Achieve application isolation, optimized resource usage, and reduced costs.

Presenting KubeSlice Open Source

Organizations undertaking modernization must support a hybrid deployment model where data and workloads often have to be in different locations — near premises (data center or edge) and public cloud. 

Why central observability?

With a growing number of applications getting modernized, organizations must address the issues of isolating activities of one team from impacting other teams.

Why multi-tenancy?

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

Hybrid deployments require secure and efficient inter-service communication across data centers, edge locations, and public clouds. This blog explores the challenges of traditional approaches and highlights how Avesha’s KubeSlice leverages multi-cluster zero trust networking to deliver automated, secure, and low-latency connectivity for modern workloads.

Why Multi Cluster?

Gartner defines edge computing as "a part of a distributed computing topology in which information processing is located close to the edge—where things and people produce or consume that information."

Avesha: The AI-Powered Kubernetes Platform for Resilient, Scalable, and Compliant Financial Services

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