Best Artificial Intelligence Software for Amazon EKS - Page 2

Compare the Top Artificial Intelligence Software that integrates with Amazon EKS as of July 2026 - Page 2

This a list of Artificial Intelligence software that integrates with Amazon EKS. Use the filters on the left to add additional filters for products that have integrations with Amazon EKS. View the products that work with Amazon EKS in the table below.

  • 1
    Nutanix Enterprise AI
    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.
  • 2
    Amazon EC2 G4 Instances
    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.
  • 3
    AWS EC2 Trn3 Instances
    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.
  • 4
    StackState

    StackState

    StackState

    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
  • 5
    AWS Deep Learning Containers
    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.
  • 6
    AWS DevOps Agent
    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.
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