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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

#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?

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.

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.

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

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.

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.

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

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)).

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.

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.

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.

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

How multi-cloud app distribution just changed the future

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.

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

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.

The unenforceable and imaginary cloud SLA

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.

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.

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.

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

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

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.

Burst workloads and the gaming experience.

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.

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

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

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

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

How Smart Scaler is leading the charge

"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.

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.

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

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.

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.

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.

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.

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

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.

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

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

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.

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."