Amazon EKS Integrations

Make enterprise AI apps and data easy to deploy, operate, and develop with secure AI endpoints using AI large language models and APIs for generative AI. Nutanix Enterprise AI simplifies and secures GenAI, empowering enterprises to pursue unprecedented productivity gains, revenue growth, and the value that GenAI promises. Streamline workflows to help monitor and manage AI endpoints conveniently, unleashing your inner AI talent. Deploy AI models and secure APIs effortlessly with a point-and-click interface. Choose from Hugging Face, NVIDIA NIM, or your own private models. Run enterprise AI securely, on-premises, or in public clouds on any CNCF-certified Kubernetes runtime while leveraging your current AI tools. Easily create or remove access to your LLMs with role-based access controls of secure API tokens for developers and GenAI application owners. Create URL-ready JSON code for API-ready testing in a single click.

Amazon Elastic File System (Amazon EFS) automatically grows and shrinks as you add and remove files with no need for management or provisioning. Share code and other files in a secure, organized way to increase DevOps agility and respond faster to customer feedback. Persist and share data from your AWS containers and serverless applications with zero management required. Easier to use and scale, Amazon EFS offers the performance and consistency needed for machine learning and big data analytics workloads. Simplify persistent storage for modern content management system workloads. Get your products and services to market faster, more reliably, and securely at a lower cost. Create and configure shared file systems simply and quickly for AWS compute services, with no provisioning, deploying, patching, or maintenance required. Scale workloads on-demand to petabytes of storage and gigabytes per second of throughput out of the box.

Amazon EC2 G4 instances are optimized for machine learning inference and graphics-intensive applications. It offers a choice between NVIDIA T4 GPUs (G4dn) and AMD Radeon Pro V520 GPUs (G4ad). G4dn instances combine NVIDIA T4 GPUs with custom Intel Cascade Lake CPUs, providing a balance of compute, memory, and networking resources. These instances are ideal for deploying machine learning models, video transcoding, game streaming, and graphics rendering. G4ad instances, featuring AMD Radeon Pro V520 GPUs and 2nd-generation AMD EPYC processors, deliver cost-effective solutions for graphics workloads. Both G4dn and G4ad instances support Amazon Elastic Inference, allowing users to attach low-cost GPU-powered inference acceleration to Amazon EC2 and reduce deep learning inference costs. They are available in various sizes to accommodate different performance needs and are integrated with AWS services such as Amazon SageMaker, Amazon ECS, and Amazon EKS.

Amazon EC2 Trn3 UltraServers are AWS’s newest accelerated computing instances, powered by the in-house Trainium3 AI chips and engineered specifically for high-performance deep-learning training and inference workloads. These UltraServers are offered in two configurations, a “Gen1” with 64 Trainium3 chips and a “Gen2” with up to 144 Trainium3 chips per UltraServer. The Gen2 configuration delivers up to 362 petaFLOPS of dense MXFP8 compute, 20 TB of HBM memory, and a staggering 706 TB/s of aggregate memory bandwidth, making it one of the highest-throughput AI compute platforms available. Interconnects between chips are handled by a new “NeuronSwitch-v1” fabric to support all-to-all communication patterns, which are especially important for large models, mixture-of-experts architectures, or large-scale distributed training.

The only real-time, analytics-driven multicloud monitoring solution for all environments (formerly SignalFx). Monitor any environment on a massively scalable streaming architecture. Open, flexible data collection and rapid visualizations of services in seconds. Purpose built for ephemeral and dynamic cloud-native environments at any scale (e.g., Kubernetes, container, serverless). Detect, visualize and resolve issues as soon as they arise. Monitor infrastructure performance in real-time at cloud scale through predictive streaming analytics. Over 200 pre-built integrations for cloud services and out-of-the-box dashboards for rapid visualization of your entire stack. Autodiscover, breakdown, group, and explore clouds, services and systems. Quickly and easily understand how your infrastructure behaves across different services, availability zones, Kubernetes clusters and more.

StackState's Topology and Relationship-Based Observability platform lets you manage your dynamic IT environment more effectively by unifying performance data from your existing monitoring tools into a single topology. Enabling you to: 1. 80% Decreased MTTR: by identifying the root cause and alerting the right teams with the correct information. 2. 65% Fewer Outages: through real-time unified observability and more planful planning. 3. 3x Faster Releases: by giving time back to developers to increase implementations. Get started today with our free guided demo: https://www.stackstate.com/schedule-a-demo

Manage all private and public environments from one platform using a single CI/CD plugin that connects to ALL automation toolchains. Including Jenkins, Kubernetes, Terraform, Cloud Formation, Azure ARM and more. No installation, no downloads … and on us for the first 30 days. Built-in integration with infrastructure orchestration domains including AWS Cloud formation, Azure ARM, Ansible and Terraform. Service Composition Domain-Specific Language (DSL) – simplifies the relationship between services, handling cascading workflows, shared resources, distributed life-cycle management and more. Orchestration of cloud native Kubernetes services across multiple clusters: OpenShift, GKE, EKS, AKS and KubeSpray. Access a built-in blueprint to automate cluster setup and configuration. Built-in integration with Jenkins and other CI/CD platforms providing a ‘one-stop-shop’ for integrating all orchestration domains to your CI/CD pipeline.

Accelerate your development by using a storage layer that works natively with your Kubernetes environment. Focus on running your application, while we make sure you have the persistent volumes that give you the scale and stability you need. Reduce complexity and increase efficiency in your app modernization journey by truly integrating stateful storage into Kubernetes. Run your database or any persistent workload in a Kubernetes environment without having to worry about managing the storage layer. Ondat gives you the ability to deliver a consistent storage layer across any platform. We give you the persistent volumes to allow you to run your own databases without paying for expensive hosted options. Take back control of your data layer in Kubernetes. Kubernetes-native storage with dynamic provisioning that works as it should. Fully API-driven, tight integration with your containerized applications.

Runtime attack analysis, threat assessment, and targeted protection for your infrastructure and applications. Stay ahead of attackers and neutralize zero-day attacks. Observe attack behavior. ThreatStryker observes, correlates, learns and acts to protect your applications and keep you one step ahead of attackers. Deepfence ThreatStryker discovers all running containers, processes, and online hosts, and presents a live and interactive color-coded view of the topology. It audits containers and hosts to detect vulnerable components and interrogates configuration to identify file system, process, and network-related misconfigurations. ThreatStryker assesses compliance using industry and community standard benchmarks. ThreatStryker performs deep inspection of network traffic, system, and application behavior, and accumulates suspicious events over time. Events are classified and correlated against known vulnerabilities and suspicious patterns of behavior.

Open source, multi-cloud platform for scanning, mapping, and ranking vulnerabilities in running containers, images, hosts, and repositories. ThreatMapper discovers the threats to your applications in production, across clouds, Kubernetes, serverless, and more. What you cannot see, you cannot secure. ThreatMapper auto-discovers your production infrastructure. It identifies and interrogates cloud instances, Kubernetes nodes, and serverless resources, discovering the applications and containers and mapping their topology in real-time. Use ThreatMapper to discover and visualize the external and internal attack surface for your applications and infrastructure. Exploiting known vulnerabilities in common dependencies is one of the easiest ways for bad actors to gain a foothold within your infrastructure. ThreatMapper scans hosts, containers, and applications for known vulnerable dependencies, taking threat feeds from over 50 different sources.

No need to compromise between the convenience of a graphical user interface and the power of infrastructure as code anymore. Kubestack allows you to design your Kubernetes platform in an intuitive, graphical user interface. And then export your custom stack to Terraform code for reliable provisioning and sustainable long-term operations. Platforms designed using Kubestack Cloud are exported to a Terraform root module, that's based on the Kubestack framework. All framework modules are open-source, lowering the long-term maintenance effort and allowing easy access to continued improvements. Adapt the tried and tested pull-request and peer-review based workflow to efficiently manage changes with your team. Reduce long-term effort by minimizing the bespoke infrastructure code you have to maintain yourself.

Deep Learning Containers are Docker images that are preinstalled and tested with the latest versions of popular deep learning frameworks. Deep Learning Containers lets you deploy custom ML environments quickly without building and optimizing your environments from scratch. Deploy deep learning environments in minutes using prepackaged and fully tested Docker images. Build custom ML workflows for training, validation, and deployment through integration with Amazon SageMaker, Amazon EKS, and Amazon ECS.

Clutch is addressing the increasingly critical challenge of non-human identity security within modern enterprises. As digital infrastructures expand and become more complex, the management and security of non-human identities, ranging from API keys and secrets to tokens and service accounts, have emerged as a pivotal yet often neglected aspect of cybersecurity. Recognizing this gap, Clutch is developing an enterprise platform dedicated to the comprehensive protection and management of these identities. Our solution is designed to fortify the digital backbone of enterprises, ensuring a secure, resilient, and trustworthy environment for their operations. Ever expanding, outpacing human identities by a staggering ratio of 45 to 1. Holds critical privileges and extensive access, essential for mission-critical automated processes. Lacks inherent security controls such as MFA and conditional access policies.

AWS DevOps Agent is a software from Amazon Web Services (AWS) designed to act as an autonomous, always-on operations engineer that resolves and proactively prevents incidents across your infrastructure, applications, and deployments. It automatically learns your application resources and their relationships, including infrastructure, code repositories, deployment pipelines, observability tools, and telemetry, then uses that knowledge to correlate logs, metrics, traces, deployment data, and recent code changes. When an alert, error spike, or support ticket arises, DevOps Agent immediately begins automated investigation; it triages incidents 24/7, runs root-cause analysis, and proposes detailed mitigation plans which can be automatically routed through team workflows (e.g., via Slack, ServiceNow, PagerDuty) or directly create support cases with AWS.