Find and compare the best Artificial Intelligence software for Amazon EKS in 2026
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Use the comparison tool below to compare the top Artificial Intelligence software for Amazon EKS on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Saturn Cloud
Saturn Cloud
$0.005 per GB per hour 104 RatingsSaturn 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. -
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AWS is the leading provider of cloud computing, delivering over 200 fully featured services to organizations worldwide. Its offerings cover everything from infrastructure—such as compute, storage, and networking—to advanced technologies like artificial intelligence, machine learning, and agentic AI. Businesses use AWS to modernize legacy systems, run high-performance workloads, and build scalable, secure applications. Core services like Amazon EC2, Amazon S3, and Amazon DynamoDB provide foundational capabilities, while advanced solutions like SageMaker and AWS Transform enable AI-driven transformation. The platform is supported by a global infrastructure that includes 38 regions, 120 availability zones, and 400+ edge locations, ensuring low latency and high reliability. AWS integrates with leading enterprise tools, developer SDKs, and partner ecosystems, giving teams the flexibility to adopt cloud at their own pace. Its training and certification programs help individuals and companies grow cloud expertise with industry-recognized credentials. With its unmatched breadth, depth, and proven track record, AWS empowers organizations to innovate and compete in the digital-first economy.
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Amazon CodeGuru
Amazon
1 RatingAmazon CodeGuru is an advanced developer tool that leverages machine learning to offer insightful suggestions for enhancing code quality and pinpointing the most costly lines of code within an application. By seamlessly incorporating Amazon CodeGuru into your current software development processes, you can benefit from integrated code reviews that highlight and optimize costly code segments, ultimately leading to cost savings. Additionally, Amazon CodeGuru Profiler assists developers in identifying the most expensive lines of code, providing detailed visualizations and actionable advice for optimizing performance and reducing expenses. Furthermore, the Amazon CodeGuru Reviewer employs machine learning techniques to detect significant issues and elusive bugs during the development phase, thereby elevating the overall quality of the codebase while facilitating more efficient application development. This powerful combination of tools ensures that developers not only write better code but also maintain a focus on cost efficiency throughout the software lifecycle. -
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DeepSeek R1
DeepSeek
Free 1 RatingDeepSeek-R1 is a cutting-edge open-source reasoning model created by DeepSeek, aimed at competing with OpenAI's Model o1. It is readily available through web, app, and API interfaces, showcasing its proficiency in challenging tasks such as mathematics and coding, and achieving impressive results on assessments like the American Invitational Mathematics Examination (AIME) and MATH. Utilizing a mixture of experts (MoE) architecture, this model boasts a remarkable total of 671 billion parameters, with 37 billion parameters activated for each token, which allows for both efficient and precise reasoning abilities. As a part of DeepSeek's dedication to the progression of artificial general intelligence (AGI), the model underscores the importance of open-source innovation in this field. Furthermore, its advanced capabilities may significantly impact how we approach complex problem-solving in various domains. -
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Datasaur
Datasaur
$349/month One tool can manage your entire data labeling workflow. We invite you to discover the best way to manage your labeling staff, improve data quality, work 70% faster, and get organized! -
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Edge Delta
Edge Delta
$0.20 per GBEdge Delta is a new way to do observability. We are the only provider that processes your data as it's created and gives DevOps, platform engineers and SRE teams the freedom to route it anywhere. As a result, customers can make observability costs predictable, surface the most useful insights, and shape your data however they need. 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. Data processing includes: * Shaping, enriching, and filtering data * Creating log analytics * Distilling metrics libraries into the most useful data * Detecting anomalies and triggering alerts 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. -
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Ray
Anyscale
FreeYou can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution. -
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StormForge
StormForge
FreeStormForge drives immediate benefits for organization through its continuous Kubernetes workload rightsizing capabilities — leading to cost savings of 40-60% along with performance and reliability improvements across the entire estate. As a vertical rightsizing solution, Optimize Live is autonomous, tunable, and works seamlessly with the HPA at enterprise scale. Optimize Live addresses both over- and under-provisioned workloads by analyzing usage data with advanced ML algorithms 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. -
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NVIDIA Triton Inference Server
NVIDIA
FreeThe NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process. -
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Sedai
Sedai
$10 per monthSedai intelligently finds resources, analyzes traffic patterns and learns metric performance. This allows you to manage your production environments continuously without any manual thresholds or human intervention. Sedai's Discovery engine uses an agentless approach to automatically identify everything in your production environments. It intelligently prioritizes your monitoring information. All your cloud accounts are on the same platform. All of your cloud resources can be viewed in one place. Connect your APM tools. Sedai will identify and select the most important metrics. Machine learning intelligently sets thresholds. Sedai is able to see all the changes in your environment. You can view updates and changes and control how the platform manages resources. Sedai's Decision engine makes use of ML to analyze and comprehend data at large scale to simplify the chaos. -
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Northflank
Northflank
$6 per monthIntroducing a self-service development platform tailored for your applications, databases, and various tasks. You can begin with a single workload and effortlessly expand to manage hundreds, utilizing either compute or GPUs. Enhance every phase from code push to production with customizable self-service workflows, pipelines, templates, and GitOps practices. Safely launch preview, staging, and production environments while benefiting from built-in observability tools, backups, restoration capabilities, and rollback options. Northflank integrates flawlessly with your preferred tools, supporting any technology stack you choose. Regardless of whether you operate on Northflank’s secure infrastructure or utilize your own cloud account, you will enjoy the same outstanding developer experience, alongside complete control over your data residency, deployment regions, security measures, and cloud costs. By harnessing Kubernetes as its operating system, Northflank provides the advantages of a cloud-native environment without the associated complexities. Whether you opt for Northflank’s straightforward cloud or connect to your GKE, EKS, AKS, or even bare-metal setups, you can achieve a managed platform experience within minutes, thus optimizing your development workflow. This flexibility ensures that your projects can scale efficiently while maintaining robust performance across diverse environments. -
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OpsWorker
OpsWorker AI
Resolve production incidents and development issues with AI that understands your code, infrastructure, and telemetry — reducing MTTR by up to 80% and boosting engineering productivity by 50%. OpsWorker helps Software Developers, SREs, and DevOps Engineers reduce MTTR, resolve complex development issues, and manage high-incident environments. Through intelligent incident correlation, code-aware troubleshooting, and deep integration into your technical ecosystem, OpsWorker delivers actionable insights and autonomous remediation — ensuring resilient, high-performance operations across Kubernetes and Cloud workloads. Built as an AI SRE platform for modern AIOps, OpsWorker leverages AI Observability to analyze incidents across distributed systems, correlating signals from metrics, logs, traces, infrastructure state, and deployments to surface the most probable root cause within minutes. Designed with an EU-first approach, OpsWorker prioritizes data sovereignty, privacy, and enterprise-grade security while enabling engineering teams to investigate incidents faster and operate complex cloud-native environments with confidence. Recent platform capabilities include Resource Topology and Service Dependency mapping, giving engineers full visibility into upstream and downstream service interactions across HTTP, TCP, and gRPC workloads. OpsWorker now integrates with Grafana Alerting contact points and supports Bring Your Own LLM, allowing organizations to use their preferred AI models for investigations. Engineers can also enrich investigations with custom operational context, enabling deeper root-cause analysis for complex incidents. To reduce alert fatigue, OpsWorker delivers a Daily Diff Summary in Slack, highlighting meaningful changes in alerts and system behavior -
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Harness
Harness
Harness is a comprehensive AI-native software delivery platform designed to modernize DevOps practices by automating continuous integration, continuous delivery, and GitOps workflows across multi-cloud and multi-service environments. It empowers engineering teams to build faster, deploy confidently, and manage infrastructure as code with automated error reduction and cost control. The platform integrates new capabilities like database DevOps, artifact registries, and on-demand cloud development environments to simplify complex operations. Harness also enhances software quality through AI-driven test automation, chaos engineering, and predictive incident response that minimize downtime. Feature management and experimentation tools allow controlled releases and data-driven decision-making. Security and compliance are strengthened with automated vulnerability scanning, runtime protection, and supply chain security. Harness offers deep insights into engineering productivity and cloud spend, helping teams optimize resources. With over 100 integrations and trusted by top companies, Harness unifies AI and DevOps to accelerate innovation and developer productivity. -
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JFrog
JFrog
$98 per monthAn entirely automated DevOps platform designed for the seamless distribution of reliable software releases from development to production. Expedite the onboarding of DevOps initiatives by managing users, resources, and permissions to enhance deployment velocity. Confidently implement updates by proactively detecting open-source vulnerabilities and ensuring compliance with licensing regulations. Maintain uninterrupted operations throughout your DevOps process with High Availability and active/active clustering tailored for enterprises. Seamlessly manage your DevOps ecosystem using pre-built native integrations and those from third-party providers. Fully equipped for enterprise use, it offers flexibility in deployment options, including on-premises, cloud, multi-cloud, or hybrid solutions that can scale alongside your organization. Enhance the speed, dependability, and security of software updates and device management for IoT applications on a large scale. Initiate new DevOps projects within minutes while easily integrating team members, managing resources, and establishing storage limits, enabling quicker coding and collaboration. This comprehensive platform empowers your team to focus on innovation without the constraints of traditional deployment challenges. -
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AI-Surge
AI Surge Cloud
Currently, startups focus on intuition and gut feelings when making decisions rather than relying on data. This can lead to suboptimal decision-making and wasted resources. Because AI applications are COMPLEX, EXPENSIVE & TIME-CONSUMING - demanding army of data engineers & data scientists with domain knowledge… this is scary because they are just a few in numbers! Startups tend not to prioritize their data & analytics. This later becomes a challenging issue in cultivating Data- culture. The no-Code architecture of AI Surge helps startups to become data-driven from day one! -
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Amazon EC2 Inf1 Instances
Amazon
$0.228 per hourAmazon EC2 Inf1 instances are specifically designed to provide efficient, high-performance machine learning inference at a competitive cost. They offer an impressive throughput that is up to 2.3 times greater and a cost that is up to 70% lower per inference compared to other EC2 offerings. Equipped with up to 16 AWS Inferentia chips—custom ML inference accelerators developed by AWS—these instances also incorporate 2nd generation Intel Xeon Scalable processors and boast networking bandwidth of up to 100 Gbps, making them suitable for large-scale machine learning applications. Inf1 instances are particularly well-suited for a variety of applications, including search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers have the advantage of deploying their ML models on Inf1 instances through the AWS Neuron SDK, which is compatible with widely-used ML frameworks such as TensorFlow, PyTorch, and Apache MXNet, enabling a smooth transition with minimal adjustments to existing code. This makes Inf1 instances not only powerful but also user-friendly for developers looking to optimize their machine learning workloads. The combination of advanced hardware and software support makes them a compelling choice for enterprises aiming to enhance their AI capabilities. -
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Amazon EC2 G5 Instances
Amazon
$1.006 per hourThe Amazon EC2 G5 instances represent the newest generation of NVIDIA GPU-powered instances, designed to cater to a variety of graphics-heavy and machine learning applications. They offer performance improvements of up to three times for graphics-intensive tasks and machine learning inference, while achieving a remarkable 3.3 times increase in performance for machine learning training when compared to the previous G4dn instances. Users can leverage G5 instances for demanding applications such as remote workstations, video rendering, and gaming, enabling them to create high-quality graphics in real time. Additionally, these instances provide machine learning professionals with an efficient and high-performing infrastructure to develop and implement larger, more advanced models in areas like natural language processing, computer vision, and recommendation systems. Notably, G5 instances provide up to three times the graphics performance and a 40% improvement in price-performance ratio relative to G4dn instances. Furthermore, they feature a greater number of ray tracing cores than any other GPU-equipped EC2 instance, making them an optimal choice for developers seeking to push the boundaries of graphical fidelity. With their cutting-edge capabilities, G5 instances are poised to redefine expectations in both gaming and machine learning sectors. -
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Amazon EC2 P4 Instances
Amazon
$11.57 per hourAmazon EC2 P4d instances are designed for optimal performance in machine learning training and high-performance computing (HPC) applications within the cloud environment. Equipped with NVIDIA A100 Tensor Core GPUs, these instances provide exceptional throughput and low-latency networking capabilities, boasting 400 Gbps instance networking. P4d instances are remarkably cost-effective, offering up to a 60% reduction in expenses for training machine learning models, while also delivering an impressive 2.5 times better performance for deep learning tasks compared to the older P3 and P3dn models. They are deployed within expansive clusters known as Amazon EC2 UltraClusters, which allow for the seamless integration of high-performance computing, networking, and storage resources. This flexibility enables users to scale their operations from a handful to thousands of NVIDIA A100 GPUs depending on their specific project requirements. Researchers, data scientists, and developers can leverage P4d instances to train machine learning models for diverse applications, including natural language processing, object detection and classification, and recommendation systems, in addition to executing HPC tasks such as pharmaceutical discovery and other complex computations. These capabilities collectively empower teams to innovate and accelerate their projects with greater efficiency and effectiveness. -
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AWS Copilot
Amazon
Rapidly develop standard application architectures using infrastructure-as-code (IaC) templates that are scalable, secure, and ready for production. With a single command, you can automate the deployment process, seamlessly configuring the delivery pipeline from your code repository to the environment of your application. Utilize comprehensive workflows to build, release, and manage all of your microservices through one unified tool. AWS Copilot serves as a command line interface designed to facilitate the quick launch and management of containerized applications within the AWS ecosystem. It streamlines the execution of applications on services like Amazon Elastic Container Service (ECS), AWS Fargate, and AWS App Runner. By automatically handling infrastructure provisioning, resource scaling, and cost optimization, it allows you to concentrate on application development rather than the intricacies of cluster management. With just one command, you can create, release, and operate production-ready containerized applications and services on ECS and Fargate, enhancing your efficiency and productivity in the cloud. This integration empowers developers to streamline their workflows and achieve faster time-to-market for their applications. -
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AWS Marketplace
Amazon
AWS Marketplace serves as a carefully organized digital platform that allows users to explore, buy, implement, and oversee third-party software, data products, AI agents, and services seamlessly within the AWS environment. This marketplace offers a vast array of options spanning various categories, including security, machine learning, business applications, and DevOps tools. By featuring adaptable pricing structures like pay-as-you-go, annual subscriptions, and free trials, AWS Marketplace makes it easier for customers to manage procurement and billing by consolidating expenses into a single AWS invoice. Additionally, it facilitates quick deployment of pre-configured software that can be easily launched on AWS infrastructure. This efficient model not only empowers businesses to spur innovation and reduce time-to-market but also enhances their ability to control software utilization and costs effectively. Ultimately, AWS Marketplace stands as an essential tool for organizations looking to optimize their software management and procurement processes. -
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Doctor Droid
Doctor Droid
$99 per monthDoctor Droid is an innovative AI-powered platform aimed at transforming how engineering teams monitor and resolve issues. It streamlines intricate investigations by adhering to established procedures, analyzing data from various integrations, pinpointing root causes, and implementing standardized runbooks for automated recovery. By actively monitoring alerts, Doctor Droid equips teams with pertinent data and insights, thereby cutting down on-call time by as much as 80% and enabling quick responses from engineers. Additionally, it enhances the onboarding experience for new engineers by automating document searches, familiarizing them with new tools, and helping them understand data, which allows them to take on primary on-call responsibilities right from the start. Furthermore, Doctor Droid is capable of conducting spontaneous investigations, such as scrutinizing Kubernetes clusters or reviewing recent deployments, while also adapting to create new strategies based on user recommendations and existing documentation. It boasts seamless integration with over 40 different tools throughout the technology stack, which significantly enhances its functionality and versatility. As a result, engineering teams can operate more efficiently and effectively in a rapidly evolving environment. -
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AWS Neuron
Amazon Web Services
It enables efficient training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances powered by AWS Trainium. Additionally, for model deployment, it facilitates both high-performance and low-latency inference utilizing AWS Inferentia-based Amazon EC2 Inf1 instances along with AWS Inferentia2-based Amazon EC2 Inf2 instances. With the Neuron SDK, users can leverage widely-used frameworks like TensorFlow and PyTorch to effectively train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal alterations to their code and no reliance on vendor-specific tools. The integration of the AWS Neuron SDK with these frameworks allows for seamless continuation of existing workflows, requiring only minor code adjustments to get started. For those involved in distributed model training, the Neuron SDK also accommodates libraries such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), enhancing its versatility and scalability for various ML tasks. By providing robust support for these frameworks and libraries, it significantly streamlines the process of developing and deploying advanced machine learning solutions. -
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ModelOp
ModelOp
ModelOp stands at the forefront of AI governance solutions, empowering businesses to protect their AI projects, including generative AI and Large Language Models (LLMs), while promoting innovation. As corporate leaders push for swift integration of generative AI, they encounter various challenges such as financial implications, regulatory compliance, security concerns, privacy issues, ethical dilemmas, and potential brand damage. With governments at global, federal, state, and local levels rapidly establishing AI regulations and oversight, organizations must act promptly to align with these emerging guidelines aimed at mitigating AI-related risks. Engaging with AI Governance specialists can keep you updated on market dynamics, regulatory changes, news, research, and valuable perspectives that facilitate a careful navigation of the benefits and hazards of enterprise AI. ModelOp Center not only ensures organizational safety but also instills confidence among all stakeholders involved. By enhancing the processes of reporting, monitoring, and compliance across the enterprise, businesses can foster a culture of responsible AI usage. In a landscape that evolves quickly, staying informed and compliant is essential for sustainable success. -
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StackGen
StackGen
Generate secure, context-aware infrastructure as code (IaC) directly from application code without needing to modify that code. While we appreciate the benefits of infrastructure as code, there is certainly potential for enhancements. StackGen leverages the application’s existing code to produce IaC that is not only consistent and secure but also compliant with industry standards. This approach eliminates bottlenecks, reduces potential liabilities, and minimizes the risk of errors that often come from manual processes, allowing for a quicker time-to-market for your applications. By providing developers with a streamlined experience, they can focus on coding rather than having to become infrastructure specialists. Consistency, security, and policy compliance are integrated by default into the auto-generated IaC. The system generates context-aware IaC without requiring any changes to the original code, ensuring that it is properly supported and aligned with the principle of least-privileged access. There's no necessity to reconstruct your existing pipelines, as StackGen seamlessly integrates into your current workflows, bridging the gaps between teams. This empowers developers to automatically create IaC that adheres to your established provisioning checklist, enhancing overall efficiency and collaboration. Ultimately, this innovative approach not only accelerates development but also strengthens security protocols across the board. -
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Amazon EC2 P5 Instances
Amazon
Amazon's Elastic Compute Cloud (EC2) offers P5 instances that utilize NVIDIA H100 Tensor Core GPUs, alongside P5e and P5en instances featuring NVIDIA H200 Tensor Core GPUs, ensuring unmatched performance for deep learning and high-performance computing tasks. With these advanced instances, you can reduce the time to achieve results by as much as four times compared to earlier GPU-based EC2 offerings, while also cutting ML model training costs by up to 40%. This capability enables faster iteration on solutions, allowing businesses to reach the market more efficiently. P5, P5e, and P5en instances are ideal for training and deploying sophisticated large language models and diffusion models that drive the most intensive generative AI applications, which encompass areas like question-answering, code generation, video and image creation, and speech recognition. Furthermore, these instances can also support large-scale deployment of high-performance computing applications, facilitating advancements in fields such as pharmaceutical discovery, ultimately transforming how research and development are conducted in the industry.
