Best Artificial Intelligence Software for Amazon EKS
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Compare the Top Artificial Intelligence Software that integrates with Amazon EKS as of July 2025
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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.
What is Artificial Intelligence Software for Amazon EKS?
Artificial Intelligence (AI) software is computer technology designed to simulate human intelligence. It can be used to perform tasks that require cognitive abilities, such as problem-solving, data analysis, visual perception and language translation. AI applications range from voice recognition and virtual assistants to autonomous vehicles and medical diagnostics. Compare and read user reviews of the best Artificial Intelligence software for Amazon EKS currently available using the table below. This list is updated regularly.
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1
Saturn Cloud
Saturn Cloud
Saturn Cloud is an AI/ML platform available on every cloud. Data teams and engineers can build, scale, and deploy their AI/ML applications with any stack. Quickly spin up environments to test new ideas, then easily deploy them into production. Scale fast—from proof-of-concept to production-ready applications. Customers include NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. Get started for free at: saturncloud.ioStarting Price: $0.005 per GB per hour -
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Amazon CodeGuru
Amazon
Amazon CodeGuru is a developer tool powered by machine learning that provides intelligent recommendations for improving code quality and identifying an application’s most expensive lines of code. Integrate Amazon CodeGuru into your existing software development workflow where you will experience built-in code reviews to detect and optimize the expensive lines of code to reduce costs. Amazon CodeGuru Profiler helps developers find an application’s most expensive lines of code along with specific visualizations and recommendations on how to improve code to save money. Amazon CodeGuru Reviewer uses machine learning to identify critical issues and hard-to-find bugs during application development to improve code quality. -
3
DeepSeek R1
DeepSeek
DeepSeek-R1 is an advanced open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible via web, app, and API, it excels in complex tasks such as mathematics and coding, demonstrating superior performance on benchmarks like the American Invitational Mathematics Examination (AIME) and MATH. DeepSeek-R1 employs a mixture of experts (MoE) architecture with 671 billion total parameters, activating 37 billion parameters per token, enabling efficient and accurate reasoning capabilities. This model is part of DeepSeek's commitment to advancing artificial general intelligence (AGI) through open-source innovation.Starting Price: Free -
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Datasaur
Datasaur
Welcome to the best tool for managing your labeling team, improving data quality, and working 70% faster—all in one place.Starting Price: $349/month -
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Edge Delta
Edge Delta
Edge Delta is a new way to do observability that helps developers and operations teams monitor datasets and create telemetry pipelines. We process your log data as it's created and give you the freedom to route it anywhere. Our primary differentiator is our distributed architecture. We are the only observability provider that pushes data processing upstream to the infrastructure level, enabling users to process their logs and metrics as soon as they’re created at the source. We combine our distributed approach with a column-oriented backend to help users store and analyze massive data volumes without impacting performance or cost. By using Edge Delta, customers can reduce observability costs without sacrificing visibility. Additionally, they can surface insights and trigger alerts before data leaves their environment.Starting Price: $0.20 per GB -
6
Ray
Anyscale
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.Starting Price: Free -
7
StormForge
StormForge
StormForge Optimize Live continuously rightsizes Kubernetes workloads to ensure cloud-native applications are both cost effective and performant while removing developer toil. As a vertical rightsizing solution, Optimize Live is autonomous, tunable, and works seamlessly with the Kubernetes horizontal pod autoscaler (HPA) at enterprise scale. Optimize Live addresses both over- and under-provisioned workloads by analyzing usage data with advanced machine learning to recommend optimal resource requests and limits. Recommendations can be deployed automatically on a flexible schedule, accounting for changes in traffic patterns or application resource requirements, ensuring that workloads are always right-sized, and freeing developers from the toil and cognitive load of infrastructure sizing. Organizations see immediate benefits from the reduction of wasted resources — leading to cost savings of 40-60% along with performance and reliability improvements across the entire estate.Starting Price: Free -
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NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.Starting Price: Free
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9
Sedai
Sedai
Sedai is an autonomous cloud management platform powered by AI/ML delivering continuous optimization for cloud operations teams to maximize cloud cost savings, performance and availability at scale. Sedai enables teams to shift from static rules and threshold-based automation to modern ML-based autonomous operations. Using Sedai, organizations can reduce cloud cost by up to 50%, improve performance by up to 75%, reduce failed customer interactions (FCIs) by 75% and multiply SRE productivity by up to 6X for their modern applications. Sedai can perform work equivalent to a team of cloud engineers working behind the scenes to optimize resources and remediate issues, so organizations can focus on innovation.Starting Price: $10 per month -
10
Harness
Harness
Harness is an AI-native software delivery platform that helps engineering teams achieve excellence by automating and streamlining the entire software delivery lifecycle. It enables continuous integration, continuous delivery, and GitOps for multi-cloud, multi-region deployments with increased speed and reliability. Harness simplifies infrastructure as code, database DevOps, and artifact management to improve collaboration and reduce errors. The platform offers AI-powered testing, incident response, chaos engineering, and feature management to enhance quality and resilience. Harness also provides cloud cost management, security testing orchestration, and developer insights to optimize performance and governance. Trusted by leading enterprises, Harness accelerates innovation while reducing manual effort and risk. -
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Amazon EC2 Inf1 Instances
Amazon
Amazon EC2 Inf1 instances are purpose-built to deliver high-performance and cost-effective machine learning inference. They provide up to 2.3 times higher throughput and up to 70% lower cost per inference compared to other Amazon EC2 instances. Powered by up to 16 AWS Inferentia chips, ML inference accelerators designed by AWS, Inf1 instances also feature 2nd generation Intel Xeon Scalable processors and offer up to 100 Gbps networking bandwidth to support large-scale ML applications. These instances are ideal for deploying applications such as search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers can deploy their ML models on Inf1 instances using the AWS Neuron SDK, which integrates with popular ML frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing for seamless migration with minimal code changes.Starting Price: $0.228 per hour -
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Amazon EC2 G5 Instances
Amazon
Amazon EC2 G5 instances are the latest generation of NVIDIA GPU-based instances that can be used for a wide range of graphics-intensive and machine-learning use cases. They deliver up to 3x better performance for graphics-intensive applications and machine learning inference and up to 3.3x higher performance for machine learning training compared to Amazon EC2 G4dn instances. Customers can use G5 instances for graphics-intensive applications such as remote workstations, video rendering, and gaming to produce high-fidelity graphics in real time. With G5 instances, machine learning customers get high-performance and cost-efficient infrastructure to train and deploy larger and more sophisticated models for natural language processing, computer vision, and recommender engine use cases. G5 instances deliver up to 3x higher graphics performance and up to 40% better price performance than G4dn instances. They have more ray tracing cores than any other GPU-based EC2 instance.Starting Price: $1.006 per hour -
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Amazon EC2 P4 Instances
Amazon
Amazon EC2 P4d instances deliver high performance for machine learning training and high-performance computing applications in the cloud. Powered by NVIDIA A100 Tensor Core GPUs, they offer industry-leading throughput and low-latency networking, supporting 400 Gbps instance networking. P4d instances provide up to 60% lower cost to train ML models, with an average of 2.5x better performance for deep learning models compared to previous-generation P3 and P3dn instances. Deployed in hyperscale clusters called Amazon EC2 UltraClusters, P4d instances combine high-performance computing, networking, and storage, enabling users to scale from a few to thousands of NVIDIA A100 GPUs based on project needs. Researchers, data scientists, and developers can utilize P4d instances to train ML models for use cases such as natural language processing, object detection and classification, and recommendation engines, as well as to run HPC applications like pharmaceutical discovery and more.Starting Price: $11.57 per hour -
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AWS Copilot
Amazon
Build common application architectures quickly with scalable, production-ready, and secure infrastructure-as-code (IaC) templates. Automate deployments with one command, and configure the delivery pipeline from a code repository to your application’s environment. Leverage end-to-end workflows, and build, release, and operate all your microservices using a single tool. AWS Copilot is a command line interface that you can use to quickly launch and manage containerized applications on AWS. It simplifies running applications on Amazon Elastic Container Service (ECS), AWS Fargate, and AWS App Runner. Simplify operations by automatically provisioning infrastructure, scaling resources, and optimizing costs, allowing you to focus on applications rather than cluster management. Create, release, and operate production-ready containerized applications and services on Amazon Elastic Container Service (ECS) and AWS Fargate with one command. -
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Doctor Droid
Doctor Droid
Doctor Droid is an AI-driven platform designed to revolutionize monitoring and troubleshooting for engineering teams. It automates complex investigations, following standard operating procedures to analyze data across multiple integrations, identify root causes, and execute standard runbooks for self-healing. By proactively listening for alerts, Doctor Droid prepares relevant data and insights, reducing on-call time by up to 80% and enabling engineers to respond swiftly. It facilitates rapid onboarding of new engineers by automating the search for documents, learning new tools, and understanding data, allowing them to become primary on-calls from day one. With the capability to perform ad-hoc investigations, such as analyzing Kubernetes clusters or checking recent deployments, Doctor Droid adapts and creates new plans based on suggestions and existing documents. It integrates seamlessly with over 40 tools across the stack.Starting Price: $99 per month -
16
AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
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ModelOp
ModelOp
ModelOp is the leading AI governance software that helps enterprises safeguard all AI initiatives, including generative AI, Large Language Models (LLMs), in-house, third-party vendors, embedded systems, etc., without stifling innovation. Corporate boards and C‑suites are demanding the rapid adoption of generative AI but face financial, regulatory, security, privacy, ethical, and brand risks. Global, federal, state, and local-level governments are moving quickly to implement AI regulations and oversight, forcing enterprises to urgently prepare for and comply with rules designed to prevent AI from going wrong. Connect with AI Governance experts to stay informed about market trends, regulations, news, research, opinions, and insights to help you balance the risks and rewards of enterprise AI. ModelOp Center keeps organizations safe and gives peace of mind to all stakeholders. Streamline reporting, monitoring, and compliance adherence across the enterprise. -
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StackGen
StackGen
Generate context-aware, secure IaC from application code without code changes. We love infrastructure as code, but that doesn’t mean there isn’t room for improvement. StackGen uses an application’s code to generate consistent, secure, and compliant IaC. Remove bottlenecks, liabilities, and error-prone manual processes between DevOps, developers, and security to get your application to market faster. Allow developers a better, more productive experience without becoming infrastructure experts. Consistency, security, and policy guardrails are incorporated by default when IaC is auto-generated. Context-aware IaC is auto-generated, with no code changes required, supported, and rightsized with least-privileged access controls. No need to rebuild your pipelines. StackGen works alongside your existing workflows to remove silos between teams. Enable developers to auto-generate IaC that complies with your provisioning checklist. -
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Amazon EC2 P5 Instances
Amazon
Amazon Elastic Compute Cloud (Amazon EC2) P5 instances, powered by NVIDIA H100 Tensor Core GPUs, and P5e and P5en instances powered by NVIDIA H200 Tensor Core GPUs deliver the highest performance in Amazon EC2 for deep learning and high-performance computing applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce the cost to train ML models by up to 40%. These instances help you iterate on your solutions at a faster pace and get to market more quickly. You can use P5, P5e, and P5en instances for training and deploying increasingly complex large language models and diffusion models powering the most demanding generative artificial intelligence applications. These applications include question-answering, code generation, video and image generation, and speech recognition. You can also use these instances to deploy demanding HPC applications at scale for pharmaceutical discovery. -
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Amazon EC2 Capacity Blocks for ML enable you to reserve accelerated compute instances in Amazon EC2 UltraClusters for your machine learning workloads. This service supports Amazon EC2 P5en, P5e, P5, and P4d instances, powered by NVIDIA H200, H100, and A100 Tensor Core GPUs, respectively, as well as Trn2 and Trn1 instances powered by AWS Trainium. You can reserve these instances for up to six months in cluster sizes ranging from one to 64 instances (512 GPUs or 1,024 Trainium chips), providing flexibility for various ML workloads. Reservations can be made up to eight weeks in advance. By colocating in Amazon EC2 UltraClusters, Capacity Blocks offer low-latency, high-throughput network connectivity, facilitating efficient distributed training. This setup ensures predictable access to high-performance computing resources, allowing you to plan ML development confidently, run experiments, build prototypes, and accommodate future surges in demand for ML applications.
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21
Amazon EC2 UltraClusters
Amazon
Amazon EC2 UltraClusters enable you to scale to thousands of GPUs or purpose-built machine learning accelerators, such as AWS Trainium, providing on-demand access to supercomputing-class performance. They democratize supercomputing for ML, generative AI, and high-performance computing developers through a simple pay-as-you-go model without setup or maintenance costs. UltraClusters consist of thousands of accelerated EC2 instances co-located in a given AWS Availability Zone, interconnected using Elastic Fabric Adapter (EFA) networking in a petabit-scale nonblocking network. This architecture offers high-performance networking and access to Amazon FSx for Lustre, a fully managed shared storage built on a high-performance parallel file system, enabling rapid processing of massive datasets with sub-millisecond latencies. EC2 UltraClusters provide scale-out capabilities for distributed ML training and tightly coupled HPC workloads, reducing training times. -
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Amazon EC2 Trn2 Instances
Amazon
Amazon EC2 Trn2 instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and diffusion models. They offer up to 50% cost-to-train savings over comparable Amazon EC2 instances. Trn2 instances support up to 16 Trainium2 accelerators, providing up to 3 petaflops of FP16/BF16 compute power and 512 GB of high-bandwidth memory. To facilitate efficient data and model parallelism, Trn2 instances feature NeuronLink, a high-speed, nonblocking interconnect, and support up to 1600 Gbps of second-generation Elastic Fabric Adapter (EFAv2) network bandwidth. They are deployed in EC2 UltraClusters, enabling scaling up to 30,000 Trainium2 chips interconnected with a nonblocking petabit-scale network, delivering 6 exaflops of compute performance. The AWS Neuron SDK integrates natively with popular machine learning frameworks like PyTorch and TensorFlow. -
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Nutanix Enterprise AI
Nutanix
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. -
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Amazon EC2 G4 Instances
Amazon
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. -
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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 -
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AWS Deep Learning Containers
Amazon
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.
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