Alternatives to AWS Deep Learning Containers
Compare AWS Deep Learning Containers alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to AWS Deep Learning Containers in 2026. Compare features, ratings, user reviews, pricing, and more from AWS Deep Learning Containers competitors and alternatives in order to make an informed decision for your business.
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Portainer Business
Portainer
Portainer is an intuitive container management platform for Docker, Kubernetes, and Edge-based environments. With a smart UI, Portainer enables you to build, deploy, manage, and secure your containerized environments with ease. It makes container adoption easier for the whole team and reduces time-to-value on Kubernetes and Docker/Swarm. With a simple GUI and a comprehensive API, the product makes it easy for engineers to deploy and manage container-based apps, triage issues, automate CI/CD workflows and set up CaaS (container-as-a-service) environments regardless of hosting environment or K8s distro. Portainer Business is designed to be used in a team environment with multiple users and clusters. The product includes a range of security features, including RBAC, OAuth integration, and logging - making it suitable for use in complex production environments. Portainer also allows you to set up GitOps automation for deployment of your apps to Docker and K8s based on Git repos.Starting Price: Free -
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Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service. Customers such as Duolingo, Samsung, GE, and Cook Pad use ECS to run their most sensitive and mission-critical applications because of its security, reliability, and scalability. ECS is a great choice to run containers for several reasons. First, you can choose to run your ECS clusters using AWS Fargate, which is serverless compute for containers. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design. Second, ECS is used extensively within Amazon to power services such as Amazon SageMaker, AWS Batch, Amazon Lex, and Amazon.com’s recommendation engine, ensuring ECS is tested extensively for security, reliability, and availability.
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Amazon SageMaker
Amazon
Amazon SageMaker is an advanced machine learning service that provides an integrated environment for building, training, and deploying machine learning (ML) models. It combines tools for model development, data processing, and AI capabilities in a unified studio, enabling users to collaborate and work faster. SageMaker supports various data sources, such as Amazon S3 data lakes and Amazon Redshift data warehouses, while ensuring enterprise security and governance through its built-in features. The service also offers tools for generative AI applications, making it easier for users to customize and scale AI use cases. SageMaker’s architecture simplifies the AI lifecycle, from data discovery to model deployment, providing a seamless experience for developers. -
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Docker
Docker
Docker takes away repetitive, mundane configuration tasks and is used throughout the development lifecycle for fast, easy and portable application development, desktop and cloud. Docker’s comprehensive end-to-end platform includes UIs, CLIs, APIs and security that are engineered to work together across the entire application delivery lifecycle. Get a head start on your coding by leveraging Docker images to efficiently develop your own unique applications on Windows and Mac. Create your multi-container application using Docker Compose. Integrate with your favorite tools throughout your development pipeline, Docker works with all development tools you use including VS Code, CircleCI and GitHub. Package applications as portable container images to run in any environment consistently from on-premises Kubernetes to AWS ECS, Azure ACI, Google GKE and more. Leverage Docker Trusted Content, including Docker Official Images and images from Docker Verified Publishers.Starting Price: $7 per month -
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Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
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Amazon SageMaker Studio Lab
Amazon
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately. -
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Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.
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Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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Amazon SageMaker Ground Truth
Amazon Web Services
Amazon SageMaker allows you to identify raw data such as images, text files, and videos; add informative labels and generate labeled synthetic data to create high-quality training data sets for your machine learning (ML) models. SageMaker offers two options, Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which give you the flexibility to use an expert workforce to create and manage data labeling workflows on your behalf or manage your own data labeling workflows. data labeling. If you want the flexibility to create and manage your own personal and data labeling workflows, you can use SageMaker Ground Truth. SageMaker Ground Truth is a data labeling service that makes data labeling easy and gives you the option of using human annotators via Amazon Mechanical Turk, third-party providers, or your own private staff.Starting Price: $0.08 per month -
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Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
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NVIDIA GPU-Optimized AMI
Amazon
The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'Starting Price: $3.06 per hour -
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Amazon SageMaker Autopilot
Amazon
Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality. You can use Amazon SageMaker Autopilot even when you have missing data. SageMaker Autopilot automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps. -
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Amazon SageMaker JumpStart
Amazon
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis. -
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AWS Deep Learning AMIs
Amazon
AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data. -
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Oracle Container Cloud Service (also known as Oracle Cloud Infrastructure Container Service Classic) offers Development and Operations teams the benefits of easy and secure Docker containerization when building and deploying applications. Provides an easy-to-use interface to manage the Docker environment. Provides out-of-the-box examples of containerized services and application stacks that can be deployed in one click. Enables developers to easily connect to their private Docker registries (so they can ‘bring their own containers’). Enables developers to focus on building containerized application images and Continuous Integration/Continuous Delivery (CI/CD) pipelines, not on learning complex orchestration technologies.
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Amazon SageMaker Clarify
Amazon
Amazon SageMaker Clarify provides machine learning (ML) developers with purpose-built tools to gain greater insights into their ML training data and models. SageMaker Clarify detects and measures potential bias using a variety of metrics so that ML developers can address potential bias and explain model predictions. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias. SageMaker Clarify also includes feature importance scores that help you explain how your model makes predictions and produces explainability reports in bulk or real time through online explainability. You can use these reports to support customer or internal presentations or to identify potential issues with your model. -
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Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
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AWS AI Factories
Amazon
AWS AI Factories is a fully-managed solution that embeds high-performance AI infrastructure directly into a customer’s own data center. You supply the space and power, and AWS deploys a dedicated, secure AI environment optimized for training and inference. It includes leading AI accelerators (such as AWS Trainium chips or NVIDIA GPUs), low-latency networking, high-performance storage, and integration with AWS’s AI services, such as Amazon SageMaker and Amazon Bedrock, giving immediate access to foundational models and AI tools without separate licensing or contracts. AWS handles the full deployment, maintenance, and management, eliminating the typical months-long effort to build comparable infrastructure. Each deployment is isolated, operating like a private AWS Region, which meets strict data sovereignty, compliance, and regulatory requirements, making it particularly suited for sectors with sensitive data. -
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Amazon SageMaker Pipelines
Amazon
Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly. Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK. -
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Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. Built on Amazon DataZone, it integrates various AWS analytics and AI/ML services, such as Amazon EMR, AWS Glue, and Amazon Bedrock, into a single platform. Users can discover, access, and process data from various sources like Amazon S3 and Redshift, and develop generative AI applications. With tools for model development, governance, MLOps, and AI customization, SageMaker Unified Studio provides an efficient, secure, and collaborative environment for data teams.
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Amazon SageMaker Debugger
Amazon
Optimize ML models by capturing training metrics in real-time and sending alerts when anomalies are detected. Automatically stop training processes when the desired accuracy is achieved to reduce the time and cost of training ML models. Automatically profile and monitor system resource utilization and send alerts when resource bottlenecks are identified to continuously improve resource utilization. Amazon SageMaker Debugger can reduce troubleshooting during training from days to minutes by automatically detecting and alerting you to remediate common training errors such as gradient values becoming too large or too small. Alerts can be viewed in Amazon SageMaker Studio or configured through Amazon CloudWatch. Additionally, the SageMaker Debugger SDK enables you to automatically detect new classes of model-specific errors such as data sampling, hyperparameter values, and out-of-bound values. -
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Amazon SageMaker Edge
Amazon
The SageMaker Edge Agent allows you to capture data and metadata based on triggers that you set so that you can retrain your existing models with real-world data or build new models. Additionally, this data can be used to conduct your own analysis, such as model drift analysis. We offer three options for deployment. GGv2 (~ size 100MB) is a fully integrated AWS IoT deployment mechanism. For those customers with a limited device capacity, we have a smaller built-in deployment mechanism within SageMaker Edge. For customers who have a preferred deployment mechanism, we support third party mechanisms that can be plugged into our user flow. Amazon SageMaker Edge Manager provides a dashboard so you can understand the performance of models running on each device across your fleet. The dashboard helps you visually understand overall fleet health and identify the problematic models through a dashboard in the console. -
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Azure Container Registry
Microsoft
Build, store, secure, scan, replicate, and manage container images and artifacts with a fully managed, geo-replicated instance of OCI distribution. Connect across environments, including Azure Kubernetes Service and Azure Red Hat OpenShift, and across Azure services like App Service, Machine Learning, and Batch. Geo-replication to efficiently manage a single registry across multiple regions. OCI artifact repository for adding helm charts, singularity support, and new OCI artifact-supported formats. Automated container building and patching including base image updates and task scheduling. Integrated security with Azure Active Directory (Azure AD) authentication, role-based access control, Docker content trust, and virtual network integration. Streamline building, testing, pushing, and deploying images to Azure with Azure Container Registry Tasks.Starting Price: $0.167 per day -
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Amazon SageMaker Studio
Amazon
Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. -
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AWS Trainium
Amazon Web Services
AWS Trainium is the second-generation Machine Learning (ML) accelerator that AWS purpose built for deep learning training of 100B+ parameter models. Each Amazon Elastic Compute Cloud (EC2) Trn1 instance deploys up to 16 AWS Trainium accelerators to deliver a high-performance, low-cost solution for deep learning (DL) training in the cloud. Although the use of deep learning is accelerating, many development teams are limited by fixed budgets, which puts a cap on the scope and frequency of training needed to improve their models and applications. Trainium-based EC2 Trn1 instances solve this challenge by delivering faster time to train while offering up to 50% cost-to-train savings over comparable Amazon EC2 instances. -
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WhaleDeck
WhaleDeck
Simplify your Docker workflow with WhaleDeck, the ultimate app for monitoring and controlling your Docker containers. With a user-friendly interface and powerful features, WhaleDeck is the only tool you need to manage your Docker environment. Monitor your containers with ease, thanks to WhaleDeck's real-time visualization of CPU, memory, drive and network usage. Keep track of your container logs and quickly identify issues with the built-in log viewer. And with support for multiple servers at once, you can easily manage all of your Docker environments from one place. Take control of your containers with the ability to run actions like start, stop, and pause on a single container or at multiple containers at once. And with the Split View feature, you can work more productively by seeing multiple parts of your Docker environment side by side. Whether you're a developer, DevOps engineer, or just someone who needs to manage Docker containers, WhaleDeck makes it simple.Starting Price: $1.99 -
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Swarm
Docker
Current versions of Docker include swarm mode for natively managing a cluster of Docker Engines called a swarm. Use the Docker CLI to create a swarm, deploy application services to a swarm, and manage swarm behavior. Cluster management integrated with Docker Engine: Use the Docker Engine CLI to create a swarm of Docker Engines where you can deploy application services. You don’t need additional orchestration software to create or manage a swarm. Decentralized design: Instead of handling differentiation between node roles at deployment time, the Docker Engine handles any specialization at runtime. You can deploy both kinds of nodes, managers and workers, using the Docker Engine. This means you can build an entire swarm from a single disk image. Declarative service model: Docker Engine uses a declarative approach to let you define the desired state of the various services in your application stack. -
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Instainer
Instainer
Instainer is a Docker container hosting service which allows run instantly any Docker container on the cloud with Heroku-style Git deployment. When we started migration to Docker in our company, we felt that something was still missing. Docker brought amazing capabilities to our DevOps team, but still there wasn't any service to click and run any Docker containers instantly. We developed Instainer for engineers who want to run Docker containers on the cloud instantly. Your feedbacks & thoughts are really welcome. Instainer provides Heroku-style Git deployment for your containers. After running your container; Instainer automatically creates Git repository for you and pushes your container’s data into this repository. You can easily clone and change your data using Git. The WordPress rich content management system can utilize plugins, widgets, and themes. -
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Podman
Containers
What is Podman? Podman is a daemonless container engine for developing, managing, and running OCI Containers on your Linux System. Containers can either be run as root or in rootless mode. Simply put: alias docker=podman. Manage pods, containers, and container images. Supporting docker swarm. We believe that Kubernetes is the defacto standard for composing Pods and for orchestrating containers, making Kubernetes YAML a defacto standard file format. Hence, Podman allows the creation and execution of Pods from a Kubernetes YAML file (see podman-play-kube). Podman can also generate Kubernetes YAML based on a container or Pod (see podman-generate-kube), which allows for an easy transition from a local development environment to a production Kubernetes cluster. -
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Azure App Service
Microsoft
Quickly build, deploy, and scale web apps and APIs on your terms. Work with .NET, .NET Core, Node.js, Java, Python or PHP, in containers or running on Windows or Linux. Meet rigorous, enterprise-grade performance, security and compliance requirements used a trusted, fully managed platform that handles over 40 billion requests per day. Fully managed platform with built-in infrastructure maintenance, security patching, and scaling. Built-in CI/CD integration and zero-downtime deployments. Rigorous security and compliance, including SOC and PCI, for seamless deployments across public cloud, Azure Government, and on-premises environments. Bring your code or container using the framework language of your choice. Increase developer productivity with tight integration of Visual Studio Code and Visual Studio. Streamline CI/CD with Git, GitHub, GitHub Actions, Atlassian Bitbucket, Azure DevOps, Docker Hub, and Azure Container Registry.Starting Price: $0.013 per hour -
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Amazon EC2 Trn1 Instances
Amazon
Amazon Elastic Compute Cloud (EC2) Trn1 instances, powered by AWS Trainium chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and latent diffusion models. Trn1 instances offer up to 50% cost-to-train savings over other comparable Amazon EC2 instances. You can use Trn1 instances to train 100B+ parameter DL and generative AI models across a broad set of applications, such as text summarization, code generation, question answering, image and video generation, recommendation, and fraud detection. The AWS Neuron SDK helps developers train models on AWS Trainium (and deploy models on the AWS Inferentia chips). It integrates natively with frameworks such as PyTorch and TensorFlow so that you can continue using your existing code and workflows to train models on Trn1 instances.Starting Price: $1.34 per hour -
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NVIDIA NGC
NVIDIA
NVIDIA GPU Cloud (NGC) is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. NGC manages a catalog of fully integrated and optimized deep learning framework containers that take full advantage of NVIDIA GPUs in both single GPU and multi-GPU configurations. NVIDIA train, adapt, and optimize (TAO) is an AI-model-adaptation platform that simplifies and accelerates the creation of enterprise AI applications and services. By fine-tuning pre-trained models with custom data through a UI-based, guided workflow, enterprises can produce highly accurate models in hours rather than months, eliminating the need for large training runs and deep AI expertise. Looking to get started with containers and models on NGC? This is the place to start. Private Registries from NGC allow you to secure, manage, and deploy your own assets to accelerate your journey to AI. -
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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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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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Store and distribute container images in a fully managed private registry. Push private images to conveniently run them in the IBM Cloud® Kubernetes Service and other runtime environments. Images are checked for security issues so you can make informed decisions about your deployments. Install the IBM Cloud Container Registry CLI to use the command line to manage your name spaces and Docker images in the IBM Cloud® private registry. View information about potential vulnerabilities and the security of images in the IBM Cloud Container Registry public and private repositories with the IBM Cloud console. Check the security status of container images that are provided by IBM, third parties or that are added to your organization's registry namespace. Advanced capabilities for security compliance insight. Access controls and image signing capabilities. Pre-integration with Kubernetes Service.
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Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data you want from a wide variety of data sources and import it quickly. Next, you can use the Data Quality and Insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly transform data without writing any code. Once you have completed your data preparation workflow, you can scale it to your full datasets using SageMaker data processing jobs; train, tune, and deploy models.
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SynapseAI
Habana Labs
Like our accelerator hardware, was purpose-designed to optimize deep learning performance, efficiency, and most importantly for developers, ease of use. With support for popular frameworks and models, the goal of SynapseAI is to facilitate ease and speed for developers, using the code and tools they use regularly and prefer. In essence, SynapseAI and its many tools and support are designed to meet deep learning developers where you are — enabling you to develop what and how you want. Habana-based deep learning processors, preserve software investments, and make it easy to build new models— for both training and deployment of the numerous and growing models defining deep learning, generative AI and large language models. -
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Azure Web App for Containers
Microsoft
It has never been easier to deploy container-based web apps. Just pull container images from Docker Hub or a private Azure Container Registry, and Web App for Containers will deploy the containerized app with your preferred dependencies to production in seconds. The platform automatically takes care of OS patching, capacity provisioning, and load balancing. Automatically scale vertically and horizontally based on application needs. Granular scaling rules are available to handle peaks in workload automatically while minimizing costs during off-peak times. Deploy data and host services across multiple locations with just few mouse clicks. -
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sloppy.io
sloppy.io
Containers have taken over the software world by storm — and for good reason. They’ve proven vital for DevOps and deployment, and have a multitude of uses for developers. In comparison to Virtual Machines, containers need few resources, deploy fast, and scale easily. Docker is the ideal tool for agile projects, products and companies. Kubernetes is complex. With sloppy.io you don’t have to worry about overlay networks, storage providers and ingress controllers. We manage the infrastructure for hosting your Docker containers, securely connecting them to your users and reliably storing your data. You can deploy and monitor your projects through our web-based UI, command line tools (CLI), and API. Our support chat connects you exclusively to software engineering and operations experts, ready to help.Starting Price: €19 per month -
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Kata Containers
Kata Containers
Kata Containers is Apache 2 licensed software consisting of two main components: the Kata agent, and the Kata Containerd shim v2 runtime. It also packages a Linux kernel and versions of QEMU, Cloud Hypervisor and Firecracker hypervisors. Kata Containers are as light and fast as containers and integrate with the container management layers—including popular orchestration tools such as Docker and Kubernetes (k8s)—while also delivering the security advantages of VMs. Kata Containers supports Linux (host and guest) for now. On the host side, we have installation instructions for several popular distributions. We also have out-of-the-box support for Clear Linux, Fedora, and CentOS 7 rootfs images through the OSBuilder which can also be used to roll your own guest images. -
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HashiCorp Nomad
HashiCorp
A simple and flexible workload orchestrator to deploy and manage containers and non-containerized applications across on-prem and clouds at scale. Single 35MB binary that integrates into existing infrastructure. Easy to operate on-prem or in the cloud with minimal overhead. Orchestrate applications of any type - not just containers. First class support for Docker, Windows, Java, VMs, and more. Bring orchestration benefits to existing services. Achieve zero downtime deployments, improved resilience, higher resource utilization, and more without containerization. Single command for multi-region, multi-cloud federation. Deploy applications globally to any region using Nomad as a single unified control plane. One single unified workflow for deploying to bare metal or cloud environments. Enable multi-cloud applications with ease. Nomad integrates seamlessly with Terraform, Consul and Vault for provisioning, service networking, and secrets management. -
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balenaEngine
balena
An engine purpose-built for embedded and IoT use cases, based on Moby Project technology from Docker. 3.5x smaller than Docker CE, packaged as a single binary. Available for a wide variety of chipset architectures, supporting everything from tiny IoT devices to large industrial gateways. Bandwidth-efficient updates with binary diffs, 10-70x smaller than pulling layers in common scenarios. Extract layers as they arrive to prevent excessive writing to disk, protecting your storage from eventual corruption. Atomic and durable image pulls defend against partial container pulls in the event of power failure. Prevents page cache thrashing during image pull, so your application runs undisturbed in low-memory situations. balenaEngine is a new container engine purpose-built for embedded and IoT use cases and compatible with Docker containers. Based on Moby Project technology from Docker, balenaEngine supports container deltas for 10-70x more efficient bandwidth usage. -
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Azure Data Science Virtual Machines
Microsoft
DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.Starting Price: $0.005 -
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NVIDIA Brev
NVIDIA
NVIDIA Brev is a cloud-based platform that provides instant access to fully configured GPU environments optimized for AI and machine learning development. Its Launchables feature offers prebuilt, customizable compute setups that let developers start projects quickly without complex setup or configuration. Users can create Launchables by specifying GPU resources, Docker images, and project files, then share them easily with collaborators. The platform also offers prebuilt Launchables featuring the latest AI frameworks, microservices, and NVIDIA Blueprints to jumpstart development. NVIDIA Brev provides a seamless GPU sandbox with support for CUDA, Python, and Jupyter Lab accessible via browser or CLI. This enables developers to fine-tune, train, and deploy AI models with minimal friction and maximum flexibility.Starting Price: $0.04 per hour -
45
Kublr
Kublr
Centrally deploy, run, and manage Kubernetes clusters across all of your environments with a comprehensive container orchestration platform that finally delivers on the Kubernetes promise. Optimized for large enterprises, Kublr is designed to provide multi-cluster deployments and observability. We made it easy, so your team can focus on what really matters: innovation and value generation. Enterprise-grade container orchestration might start with Docker and Kubernetes, but Kublr delivers the comprehensive, flexible tools that ensure you deploy enterprise-class Kubernetes clusters from Day One. The platform eases adoption for enterprises new to Kubernetes while providing the flexibility and control mature organizations need. While master self-healing is key, true high availability can only be achieved with additional node self-healing, ensuring worker nodes are as reliable as the cluster. -
46
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). -
47
Slim.AI
Slim.AI
Easily connect your own private registries and share images with your team. Explore the world’s largest public registries to find the right container image for your project. If you don’t know what’s in your containers, you can’t have software security. The Slim platform lifts the veil on container internals so you can analyze, optimize, and compare changes across multiple containers or versions. Use DockerSlim, our open-source project, to automatically optimize your container images. Remove bulky or dangerous packages, so you ship only what you need to produce. Find out how the Slim platform can help your team automatically improve software and supply chain security, tune containers for development, testing, and production, and ship secure container-based apps to the cloud. Accounts are free and there is no charge to use the platform at this time. We're container enthusiasts, not salespeople, so know that your privacy and security are the founding principles of our business. -
48
Amazon SageMaker Canvas
Amazon
Amazon SageMaker Canvas expands access to machine learning (ML) by providing business analysts with a visual interface that allows them to generate accurate ML predictions on their own, without requiring any ML experience or having to write a single line of code. Visual point-and-click interface to connect, prepare, analyze, and explore data for building ML models and generating accurate predictions. Automatically build ML models to run what-if analysis and generate single or bulk predictions with a few clicks. Boost collaboration between business analysts and data scientists by sharing, reviewing, and updating ML models across tools. Import ML models from anywhere and generate predictions directly in Amazon SageMaker Canvas. With Amazon SageMaker Canvas, you can import data from disparate sources, select values you want to predict, automatically prepare and explore data, and quickly and more easily build ML models. You can then analyze models and generate accurate predictions. -
49
Wallaroo.AI
Wallaroo.AI
Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency. Wallaroo is purpose-built from the ground up to be the easy way to deploy and manage ML in production, unlike Apache Spark, or heavy-weight containers. ML with up to 80% lower cost and easily scale to more data, more models, more complex models. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale. -
50
Apache Mesos
Apache Software Foundation
Mesos is built using the same principles as the Linux kernel, only at a different level of abstraction. The Mesos kernel runs on every machine and provides applications (e.g., Hadoop, Spark, Kafka, Elasticsearch) with API’s for resource management and scheduling across entire datacenter and cloud environments. Native support for launching containers with Docker and AppC images.Support for running cloud native and legacy applications in the same cluster with pluggable scheduling policies. HTTP APIs for developing new distributed applications, for operating the cluster, and for monitoring. Built-in Web UI for viewing cluster state and navigating container sandboxes.
