Alternatives to AWS AI Factories
Compare AWS AI Factories alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to AWS AI Factories in 2025. Compare features, ratings, user reviews, pricing, and more from AWS AI Factories competitors and alternatives in order to make an informed decision for your business.
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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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CoreWeave
CoreWeave
CoreWeave is a cloud infrastructure provider specializing in GPU-based compute solutions tailored for AI workloads. The platform offers scalable, high-performance GPU clusters that optimize the training and inference of AI models, making it ideal for industries like machine learning, visual effects (VFX), and high-performance computing (HPC). CoreWeave provides flexible storage, networking, and managed services to support AI-driven businesses, with a focus on reliability, cost efficiency, and enterprise-grade security. The platform is used by AI labs, research organizations, and businesses to accelerate their AI innovations. -
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AWS EC2 Trn3 Instances
Amazon
Amazon EC2 Trn3 UltraServers are AWS’s newest accelerated computing instances, powered by the in-house Trainium3 AI chips and engineered specifically for high-performance deep-learning training and inference workloads. These UltraServers are offered in two configurations, a “Gen1” with 64 Trainium3 chips and a “Gen2” with up to 144 Trainium3 chips per UltraServer. The Gen2 configuration delivers up to 362 petaFLOPS of dense MXFP8 compute, 20 TB of HBM memory, and a staggering 706 TB/s of aggregate memory bandwidth, making it one of the highest-throughput AI compute platforms available. Interconnects between chips are handled by a new “NeuronSwitch-v1” fabric to support all-to-all communication patterns, which are especially important for large models, mixture-of-experts architectures, or large-scale distributed training. -
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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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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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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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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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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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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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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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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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NVIDIA Confidential Computing secures data in use, protecting AI models and workloads as they execute, by leveraging hardware-based trusted execution environments built into NVIDIA Hopper and Blackwell architectures and supported platforms. It enables enterprises to deploy AI training and inference, whether on-premises, in the cloud, or at the edge, with no changes to model code, while ensuring the confidentiality and integrity of both data and models. Key features include zero-trust isolation of workloads from the host OS or hypervisor, device attestation to verify that only legitimate NVIDIA hardware is running the code, and full compatibility with shared or remote infrastructure for ISVs, enterprises, and multi-tenant environments. By safeguarding proprietary AI models, inputs, weights, and inference activities, NVIDIA Confidential Computing enables high-performance AI without compromising security or performance.
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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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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 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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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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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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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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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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IREN Cloud
IREN
IREN’s AI Cloud is a GPU-cloud platform built on NVIDIA reference architecture and non-blocking 3.2 TB/s InfiniBand networking, offering bare-metal GPU clusters designed for high-performance AI training and inference workloads. The service supports a range of NVIDIA GPU models with specifications such as large amounts of RAM, vCPUs, and NVMe storage. The cloud is fully integrated and vertically controlled by IREN, giving clients operational flexibility, reliability, and 24/7 in-house support. Users can monitor performance metrics, optimize GPU spend, and maintain secure, isolated environments with private networking and tenant separation. It allows deployment of users’ own data, models, frameworks (TensorFlow, PyTorch, JAX), and container technologies (Docker, Apptainer) with root access and no restrictions. It is optimized to scale for demanding applications, including fine-tuning large language models. -
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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 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 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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Amazon SageMaker HyperPod
Amazon
Amazon SageMaker HyperPod is a purpose-built, resilient compute infrastructure that simplifies and accelerates the development of large AI and machine-learning models by handling distributed training, fine-tuning, and inference across clusters with hundreds or thousands of accelerators, including GPUs and AWS Trainium chips. It removes the heavy lifting involved in building and managing ML infrastructure by providing persistent clusters that automatically detect and repair hardware failures, automatically resume workloads, and optimize checkpointing to minimize interruption risk, enabling months-long training jobs without disruption. HyperPod offers centralized resource governance; administrators can set priorities, quotas, and task-preemption rules so compute resources are allocated efficiently among tasks and teams, maximizing utilization and reducing idle time. It also supports “recipes” and pre-configured settings to quickly fine-tune or customize foundation models. -
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GMI Cloud
GMI Cloud
GMI Cloud provides a complete platform for building scalable AI solutions with enterprise-grade GPU access and rapid model deployment. Its Inference Engine offers ultra-low-latency performance optimized for real-time AI predictions across a wide range of applications. Developers can deploy models in minutes without relying on DevOps, reducing friction in the development lifecycle. The platform also includes a Cluster Engine for streamlined container management, virtualization, and GPU orchestration. Users can access high-performance GPUs, InfiniBand networking, and secure, globally scalable infrastructure. Paired with popular open-source models like DeepSeek R1 and Llama 3.3, GMI Cloud delivers a powerful foundation for training, inference, and production AI workloads.Starting Price: $2.50 per hour -
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Mistral Compute
Mistral
Mistral Compute is a purpose-built AI infrastructure platform that delivers a private, integrated stack, GPUs, orchestration, APIs, products, and services, in any form factor, from bare-metal servers to fully managed PaaS. Designed to democratize frontier AI beyond a handful of providers, it empowers sovereigns, enterprises, and research institutions to architect, own, and optimize their entire AI environment, training, and serving any workload on tens of thousands of NVIDIA-powered GPUs using reference architectures managed by experts in high-performance computing. With support for region- and domain-specific efforts, defense technology, pharmaceutical discovery, financial markets, and more, it offers four years of operational lessons, built-in sustainability through decarbonized energy, and full compliance with stringent European data-sovereignty regulations. -
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Nscale
Nscale
Nscale is the Hyperscaler engineered for AI, offering high-performance computing optimized for training, fine-tuning, and intensive workloads. From our data centers to our software stack, we are vertically integrated in Europe to provide unparalleled performance, efficiency, and sustainability. Access thousands of GPUs tailored to your requirements using our AI cloud platform. Reduce costs, grow revenue, and run your AI workloads more efficiently on a fully integrated platform. Whether you're using Nscale's built-in AI/ML tools or your own, our platform is designed to simplify the journey from development to production. The Nscale Marketplace offers users access to various AI/ML tools and resources, enabling efficient and scalable model development and deployment. Serverless allows seamless, scalable AI inference without the need to manage infrastructure. It automatically scales to meet demand, ensuring low latency and cost-effective inference for popular generative AI models. -
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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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Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.
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Parasail
Parasail
Parasail is an AI deployment network offering scalable, cost-efficient access to high-performance GPUs for AI workloads. It provides three primary services, serverless endpoints for real-time inference, Dedicated instances for private model deployments, and Batch processing for large-scale tasks. Users can deploy open source models like DeepSeek R1, LLaMA, and Qwen, or bring their own, with the platform's permutation engine matching workloads to optimal hardware, including NVIDIA's H100, H200, A100, and 4090 GPUs. Parasail emphasizes rapid deployment, with the ability to scale from a single GPU to clusters within minutes, and offers significant cost savings, claiming up to 30x cheaper compute compared to legacy cloud providers. It supports day-zero availability for new models and provides a self-service interface without long-term contracts or vendor lock-in.Starting Price: $0.80 per million tokens -
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AWS Inferentia
Amazon
AWS Inferentia accelerators are designed by AWS to deliver high performance at the lowest cost for your deep learning (DL) inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, which deliver up to 2.3x higher throughput and up to 70% lower cost per inference than comparable GPU-based Amazon EC2 instances. Many customers, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have adopted Inf1 instances and realized its performance and cost benefits. The first-generation Inferentia has 8 GB of DDR4 memory per accelerator and also features a large amount of on-chip memory. Inferentia2 offers 32 GB of HBM2e per accelerator, increasing the total memory by 4x and memory bandwidth by 10x over Inferentia. -
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NetApp AIPod
NetApp
NetApp AIPod is a comprehensive AI infrastructure solution designed to streamline the deployment and management of artificial intelligence workloads. By integrating NVIDIA-validated turnkey solutions, such as NVIDIA DGX BasePOD™ and NetApp's cloud-connected all-flash storage, AIPod consolidates analytics, training, and inference capabilities into a single, scalable system. This convergence enables organizations to rapidly implement AI workflows, from model training to fine-tuning and inference, while ensuring robust data management and security. With preconfigured infrastructure optimized for AI tasks, NetApp AIPod reduces complexity, accelerates time to insights, and supports seamless integration into hybrid cloud environments. -
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Baseten
Baseten
Baseten is a high-performance platform designed for mission-critical AI inference workloads. It supports serving open-source, custom, and fine-tuned AI models on infrastructure built specifically for production scale. Users can deploy models on Baseten’s cloud, their own cloud, or in a hybrid setup, ensuring flexibility and scalability. The platform offers inference-optimized infrastructure that enables fast training and seamless developer workflows. Baseten also provides specialized performance optimizations tailored for generative AI applications such as image generation, transcription, text-to-speech, and large language models. With 99.99% uptime, low latency, and support from forward deployed engineers, Baseten aims to help teams bring AI products to market quickly and reliably.Starting Price: Free -
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WhiteFiber
WhiteFiber
WhiteFiber is a vertically integrated AI infrastructure platform offering high-performance GPU cloud and HPC colocation solutions tailored for AI/ML workloads. Its cloud platform is purpose-built for machine learning, large language models, and deep learning, featuring NVIDIA H200, B200, and GB200 GPUs, ultra-fast Ethernet and InfiniBand networking, and up to 3.2 Tb/s GPU fabric bandwidth. WhiteFiber's infrastructure supports seamless scaling from hundreds to tens of thousands of GPUs, with flexible deployment options including bare metal, containers, and virtualized environments. It ensures enterprise-grade support and SLAs, with proprietary cluster management, orchestration, and observability software. WhiteFiber's data centers provide AI and HPC-optimized colocation with high-density power, direct liquid cooling, and accelerated deployment timelines, along with cross-data center dark fiber connectivity for redundancy and scale. -
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Lambda
Lambda
Lambda provides high-performance supercomputing infrastructure built specifically for training and deploying advanced AI systems at massive scale. Its Superintelligence Cloud integrates high-density power, liquid cooling, and state-of-the-art NVIDIA GPUs to deliver peak performance for demanding AI workloads. Teams can spin up individual GPU instances, deploy production-ready clusters, or operate full superclusters designed for secure, single-tenant use. Lambda’s architecture emphasizes security and reliability with shared-nothing designs, hardware-level isolation, and SOC 2 Type II compliance. Developers gain access to the world’s most advanced GPUs, including NVIDIA GB300 NVL72, HGX B300, HGX B200, and H200 systems. Whether testing prototypes or training frontier-scale models, Lambda offers the compute foundation required for superintelligence-level performance. -
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NVIDIA DGX Cloud
NVIDIA
NVIDIA DGX Cloud offers a fully managed, end-to-end AI platform that leverages the power of NVIDIA’s advanced hardware and cloud computing services. This platform allows businesses and organizations to scale AI workloads seamlessly, providing tools for machine learning, deep learning, and high-performance computing (HPC). DGX Cloud integrates seamlessly with leading cloud providers, delivering the performance and flexibility required to handle the most demanding AI applications. This service is ideal for businesses looking to enhance their AI capabilities without the need to manage physical infrastructure. -
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QumulusAI
QumulusAI
QumulusAI delivers supercomputing without constraint, combining scalable HPC with grid-independent data centers to break bottlenecks and power the future of AI. QumulusAI is universalizing access to AI supercomputing, removing the constraints of legacy HPC and delivering the scalable, high-performance computing AI demands today. And tomorrow too. No virtualization overhead, no noisy neighbors, just dedicated, direct access to AI servers optimized with NVIDIA’s latest GPUs (H200) and Intel/AMD CPUs. QumulusAI offers HPC infrastructure uniquely configured around your specific workloads, instead of legacy providers’ one-size-fits-all approach. We collaborate with you through design, deployment, to ongoing optimization, adapting as your AI projects evolve, so you get exactly what you need at each step. We own the entire stack. That means better performance, greater control, and more predictable costs than with other providers who coordinate with third-party vendors. -
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FPT Cloud
FPT Cloud
FPT Cloud is a next‑generation cloud computing and AI platform that streamlines innovation by offering a robust, modular ecosystem of over 80 services, from compute, storage, database, networking, and security to AI development, backup, disaster recovery, and data analytics, built to international standards. Its offerings include scalable virtual servers with auto‑scaling and 99.99% uptime; GPU‑accelerated infrastructure tailored for AI/ML workloads; FPT AI Factory, a comprehensive AI lifecycle suite powered by NVIDIA supercomputing (including infrastructure, model pre‑training, fine‑tuning, model serving, AI notebooks, and data hubs); high‑performance object and block storage with S3 compatibility and encryption; Kubernetes Engine for managed container orchestration with cross‑cloud portability; managed database services across SQL and NoSQL engines; multi‑layered security with next‑gen firewalls and WAFs; centralized monitoring and activity logging. -
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Hathora
Hathora
Hathora is a real-time compute orchestration platform designed to enable high-performance, low-latency applications by aggregating CPUs and GPUs across clouds, edge, and on-prem infrastructure. It supports universal orchestration, letting teams run workloads across their own data centers or Hathora’s global fleet with intelligent load balancing, automatic spill-over, and built-in 99.9% uptime. Edge-compute capabilities ensure sub-50 ms latency worldwide by routing workloads to the closest region, while container-native support allows any Docker-based workload, including GPU-accelerated inference, game servers, or batch compute, to deploy without re-architecture. Data-sovereignty features let organizations enforce region-locked deployments and meet compliance obligations. Use-cases span real-time inference, global game-server hosting, build farms, and elastic “metal” availability, all accessible through a unified API and global observability dashboards.Starting Price: $4 per month -
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Core Scientific
Core Scientific
Core Scientific delivers purpose-built high-density colocation infrastructure and intelligent software solutions designed for demanding compute workloads such as AI, machine learning, high-performance computing, and digital asset mining. It features ready-to-scale high-density compute environments with contracted power capacity of over 1.3 GW, faster deployment timelines, and optimized cooling and power systems tailored for intensive workloads. Core Scientific’s digital mining offering incorporates proprietary software for fleet management capable of handling up to one million miners, real-time thermal monitoring, and hash-price economics analysis to optimize profitability. In its colocation and AI-focused infrastructure business, Core Scientific combines high-density racks (50–200 kW+ per rack) and enterprise-grade infrastructure to support AI model training/inference, cloud workloads, financial services analytics, government mission-critical systems, and healthcare research. -
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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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Toolhouse
Toolhouse
Toolhouse is the first cloud platform that allows developers to quickly build, manage, and run AI function calling. It takes care of every aspect of connecting AI to the real world, from performance optimization to prompting to integrations with all foundational models, in just three lines of code. Toolhouse provides a 1-click platform to deploy efficient actions and knowledge for AI apps with a low-latency cloud. It offers high-quality, low-latency tools hosted on reliable and scalable infrastructure, with caching and optimization of tool responses.Starting Price: Free -
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Skyportal
Skyportal
Skyportal is a GPU cloud platform built for AI engineers, offering 50% less cloud costs and 100% GPU performance. It provides a cost-effective GPU infrastructure for machine learning workloads, eliminating unpredictable cloud bills and hidden fees. Skyportal has seamlessly integrated Kubernetes, Slurm, PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA Drivers, fully optimized for Ubuntu 22.04 LTS and 24.04 LTS, allowing users to focus on innovating and scaling with ease. It offers high-performance NVIDIA H100 and H200 GPUs optimized specifically for ML/AI workloads, with instant scalability and 24/7 expert support from a team that understands ML workflows and optimization. Skyportal's transparent pricing and zero egress fees provide predictable costs for AI infrastructure. Users can share their AI/ML project requirements and goals, deploy models within the infrastructure using familiar tools and frameworks, and scale their infrastructure as needed.Starting Price: $2.40 per hour -
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NetMind AI
NetMind AI
NetMind.AI is a decentralized computing platform and AI ecosystem designed to accelerate global AI innovation. By leveraging idle GPU resources worldwide, it offers accessible and affordable AI computing power to individuals, businesses, and organizations of all sizes. The platform provides a range of services, including GPU rental, serverless inference, and an AI ecosystem that encompasses data processing, model training, inference, and agent development. Users can rent GPUs at competitive prices, deploy models effortlessly with on-demand serverless inference, and access a wide array of open-source AI model APIs with high-throughput, low-latency performance. NetMind.AI also enables contributors to add their idle GPUs to the network, earning NetMind Tokens (NMT) as rewards. These tokens facilitate transactions on the platform, allowing users to pay for services such as training, fine-tuning, inference, and GPU rentals. -
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VMware Private AI Foundation
VMware
VMware Private AI Foundation is a joint, on‑premises generative AI platform built on VMware Cloud Foundation (VCF) that enables enterprises to run retrieval‑augmented generation workflows, fine‑tune and customize large language models, and perform inference in their own data centers, addressing privacy, choice, cost, performance, and compliance requirements. It integrates the Private AI Package (including vector databases, deep learning VMs, data indexing and retrieval services, and AI agent‑builder tools) with NVIDIA AI Enterprise (comprising NVIDIA microservices like NIM, NVIDIA’s own LLMs, and third‑party/open source models from places like Hugging Face). It supports full GPU virtualization, monitoring, live migration, and efficient resource pooling on NVIDIA‑certified HGX servers with NVLink/NVSwitch acceleration. Deployable via GUI, CLI, and API, it offers unified management through self‑service provisioning, model store governance, and more. -
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NVIDIA Run:ai
NVIDIA
NVIDIA Run:ai is an enterprise platform designed to optimize AI workloads and orchestrate GPU resources efficiently. It dynamically allocates and manages GPU compute across hybrid, multi-cloud, and on-premises environments, maximizing utilization and scaling AI training and inference. The platform offers centralized AI infrastructure management, enabling seamless resource pooling and workload distribution. Built with an API-first approach, Run:ai integrates with major AI frameworks and machine learning tools to support flexible deployment anywhere. It also features a powerful policy engine for strategic resource governance, reducing manual intervention. With proven results like 10x GPU availability and 5x utilization, NVIDIA Run:ai accelerates AI development cycles and boosts ROI. -
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NVIDIA Picasso
NVIDIA
NVIDIA Picasso is a cloud service for building generative AI–powered visual applications. Enterprises, software creators, and service providers can run inference on their models, train NVIDIA Edify foundation models on proprietary data, or start from pre-trained models to generate image, video, and 3D content from text prompts. Picasso service is fully optimized for GPUs and streamlines training, optimization, and inference on NVIDIA DGX Cloud. Organizations and developers can train NVIDIA’s Edify models on their proprietary data or get started with models pre-trained with our premier partners. Expert denoising network to generate photorealistic 4K images. Temporal layers and novel video denoiser generate high-fidelity videos with temporal consistency. A novel optimization framework for generating 3D objects and meshes with high-quality geometry. Cloud service for building and deploying generative AI-powered image, video, and 3D applications. -
48
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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Substrate
Substrate
Substrate is the platform for agentic AI. Elegant abstractions and high-performance components, optimized models, vector database, code interpreter, and model router. Substrate is the only compute engine designed to run multi-step AI workloads. Describe your task by connecting components and let Substrate run it as fast as possible. We analyze your workload as a directed acyclic graph and optimize the graph, for example, merging nodes that can be run in a batch. The Substrate inference engine automatically schedules your workflow graph with optimized parallelism, reducing the complexity of chaining multiple inference APIs. No more async programming, just connect nodes and let Substrate parallelize your workload. Our infrastructure guarantees your entire workload runs in the same cluster, often on the same machine. You won’t spend fractions of a second per task on unnecessary data roundtrips and cross-region HTTP transport.Starting Price: $30 per month -
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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.
