Alternatives to Modal

Compute Engine (IaaS), a platform from Google that allows organizations to create and manage cloud-based virtual machines, is an infrastructure as a services (IaaS). Computing infrastructure in predefined sizes or custom machine shapes to accelerate cloud transformation. General purpose machines (E2, N1,N2,N2D) offer a good compromise between price and performance. Compute optimized machines (C2) offer high-end performance vCPUs for compute-intensive workloads. Memory optimized (M2) systems offer the highest amount of memory and are ideal for in-memory database applications. Accelerator optimized machines (A2) are based on A100 GPUs, and are designed for high-demanding applications. Integrate Compute services with other Google Cloud Services, such as AI/ML or data analytics. Reservations can help you ensure that your applications will have the capacity needed as they scale. You can save money by running Compute using the sustained-use discount, and you can even save more when you use the committed-use discount.

You can run code without worrying about servers. Only pay for the compute time that you use. AWS Lambda allows you to run code without having to provision or manage servers. You only pay for the compute time that you use. Lambda allows you to run code for any type of backend service or application - and all this with zero administration. Upload your code, and Lambda will take care of scaling your code with high availability. Your code can be set up to trigger automatically from other AWS services, or you can call it directly from any mobile or web app. AWS Lambda runs your code automatically without you having to manage or provision servers. Simply write the code and upload it directly to Lambda. AWS Lambda automatically scales the application by running code according to each trigger. Your code runs in parallel, processing each trigger separately, scaling exactly with the workload.

Protect and optimize mission-critical Kubernetes apps. Fairwinds Insights, a Kubernetes configuration validation tool, monitors your Kubernetes containers and recommends improvements. The software integrates trusted open-source tools, toolchain integrations and SRE expertise, based on hundreds successful Kubernetes deployments. The need to balance the speed of engineering and the reactive pace of security can lead to messy Kubernetes configurations, as well as unnecessary risk. It can take engineering time to adjust CPU or memory settings. This can lead to over-provisioning of data centers capacity or cloud compute. While traditional monitoring tools are important, they don't offer everything necessary to identify and prevent changes that could affect Kubernetes workloads.

Amazon CloudFront (CDN) is a fast content delivery network that delivers data, videos, and APIs to customers worldwide with low latency and high transfer speeds. It's also a developer-friendly service. CloudFront integrates with AWS. CloudFront can be used in both physical locations that are directly connected with the AWS global infrastructure as well as other AWS services. CloudFront integrates seamlessly with AWS Shield, Amazon S3, Elastic Load Balancing, or Amazon EC2 as origins of your applications. Lambda@Edge allows you to run custom code closer and customize the user experience. Lastly, if your AWS origins include Amazon S3, Amazon EC2 and Elastic Load Balancing you don't have to pay for data transferred between CloudFront and these services.

Fully managed compute platform to deploy and scale containerized applications securely and quickly. You can write code in your favorite languages, including Go, Python, Java Ruby, Node.js and other languages. For a simple developer experience, we abstract away all infrastructure management. It is built upon the open standard Knative which allows for portability of your applications. You can write code the way you want by deploying any container that listens to events or requests. You can create applications in your preferred language with your favorite dependencies, tools, and deploy them within seconds. Cloud Run abstracts away all infrastructure management by automatically scaling up and down from zero almost instantaneously--depending on traffic. Cloud Run only charges for the resources you use. Cloud Run makes app development and deployment easier and more efficient. Cloud Run is fully integrated with Cloud Code and Cloud Build, Cloud Monitoring and Cloud Logging to provide a better developer experience.

Spot Ocean allows you to reap the benefits Kubernetes without worrying too much about infrastructure, while simultaneously gaining deep cluster visibility. This can dramatically reduce costs. The key question is how can containers be used without the operational overhead of managing underlying VMs, while still maximizing the cost benefits associated to multi-cloud and spot instances. Spot Ocean solves this problem by managing containers within a "Serverless” environment. Ocean is an abstraction over virtual machines that allows Kubernetes clusters to be deployed without the need for managing the underlying VMs. Ocean makes use of multiple compute purchasing options such as Spot and Reserved instance pricing. It also has failover to On Demand instances whenever necessary, resulting in a 80% reduction in infrastructure expenses. Spot Ocean is a Serverless Compute Engine. It abstracts the provisioning (launching), autoscaling and management of worker nodes within Kubernetes clusters.

Workers Unbound is the fastest, most flexible, secure, and affordable serverless computing platform Cloudflare has introduced. Workers Unbound extends capacity limits while offering a compelling pricing structure for customers. Cloudflare Workers®, Unbound is a serverless platform that developers can use with unmatched flexibility, performance and security. It also makes it easy to use and costs very affordable. Cloudflare Workers Unbound enables developers to run complex computing workloads across the Cloudflare network, and only pay for what they use. - Limitless: Customers only pay for what they use, with unlocked CPU limits - No Cold Starts - Support for 0 nanosecond cold start - making Workers Unbound predictably quick - Fast Globally - Workers run on Cloudflare’s network in 200 cities and more than 100 countries - Wide Language Support: Flexibility to program in JavaScript, C++, Python and Go. - Robust Debugging Tool: New tools to simplify diagnosing and debugging problems

Each GPU contains 16896 CUDA Cores and 528 Tensor cores. This is the current flagship chip from NVidia®, which is unmatched in terms of raw performance for AI operations. We use the SXM5 module of NVLINK, which has a memory bandwidth up to 2.6 Gbps. It also offers 900GB/s bandwidth P2P. Fourth generation AMD Genoa with up to 384 Threads and a boost clock 3.7GHz. We only use the SXM4 "for NVLINK" module, which has a memory bandwidth exceeding 2TB/s as well as a P2P bandwidth up to 600GB/s. Second generation AMD EPYC Rome with up to 192 Threads and a boost clock 3.3GHz. The name 8A100.176V consists of 8x RTX, 176 CPU cores threads and virtualized. It is faster at processing tensor operations than the V100 despite having fewer tensors. This is due to its different architecture. Second generation AMD EPYC Rome with up to 96 threads and a boost clock speed of 3.35GHz.

AWS Inferentia Accelerators are designed by AWS for high performance and low cost for deep learning (DL), inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud, Amazon EC2 Inf1 instances. These instances deliver up to 2.3x more throughput and up 70% lower cost per input than comparable GPU-based Amazon EC2 instances. Inf1 instances have been adopted by many customers including Snap, Sprinklr and Money Forward. They have seen the performance and cost savings. The first-generation Inferentia features 8 GB of DDR4 memory per accelerator, as well as a large amount on-chip memory. Inferentia2 has 32 GB of HBM2e, which increases the total memory by 4x and memory bandwidth 10x more than Inferentia.

Get the fastest time to revenue available and government-level security with NXT1 LaunchIT, the world’s first 100% serverless, SaaS deployment and management platform. Go from code to published SaaS in 15 minutes. NXT1 LaunchIT enables instant availability by streamlining and automating every aspect of cloud infrastructure management required for SaaS delivery and sales – simply code and deploy. LaunchIT adheres to CISA’s Secure by Design guidelines and provides a direct path to FedRAMP compliance-readiness at a fraction of the traditional time and cost required, establishing new, impactful sales opportunities into state and federal government agencies. Built on Zero Trust principles, with integrated CI/CD management, multi-account and multi-region support, comprehensive performance management and observability, full ecommerce support, and GitHub integration, LaunchIT accelerates time to revenue for technology startups, legacy application migrations, enterprise expansions, systems integrations, and independent software development. Get started with a 15-day free trial at nxt1.cloud/go.

You code. We take care of the rest. Serverless code can be deployed instantly around the globe to provide exceptional performance, reliability, scale, and scaling. You don't have to configure auto-scaling or load balancers or pay for capacity you don’t use. Traffic is automatically routed to and load balanced across thousands upon thousands of servers. Your code scales seamlessly, so you can rest easy. Every deploy is made to a network data centers running V8 isolates. Cloudflare powers your code. It is only milliseconds from almost every Internet user. To get started building an app, creating functions, or writing an API, choose a template from your language. You will be up and running quickly with our tutorials, templates, and a CLI. Most serverless platforms experience a cold startup every time you deploy your service or increase in popularity. Your code can be run by workers instantly without any cold starts. The first 100,000 requests per day are free. Paid plans start at $5/10,000,000.

There are options for every business to train deep and machine learning models efficiently. There are AI accelerators that can be used for any purpose, from low-cost inference to high performance training. It is easy to get started with a variety of services for development or deployment. Tensor Processing Units are ASICs that are custom-built to train and execute deep neural network. You can train and run more powerful, accurate models at a lower cost and with greater speed and scale. NVIDIA GPUs are available to assist with cost-effective inference and scale-up/scale-out training. Deep learning can be achieved by leveraging RAPID and Spark with GPUs. You can run GPU workloads on Google Cloud, which offers industry-leading storage, networking and data analytics technologies. Compute Engine allows you to access CPU platforms when you create a VM instance. Compute Engine provides a variety of Intel and AMD processors to support your VMs.

Scale your applications without the need to manage infrastructure. Scale your applications without the need to manage infrastructure. You can stay agile with support for many development languages and a variety of developer tools. You can quickly build and deploy apps using popular languages, or bring your own frameworks and runtimes. You can also manage your resources from the command-line, debug source code and run API backends easily. You can focus on writing code and not having to manage the infrastructure. Firewall capabilities, IAM rules, managed SSL/ TLS certificates can help protect your apps from security threats. You can operate in a serverless environment and not worry about over- or under provisioning. App Engine scales automatically based on app traffic and consumes resources only while your code is running.

Accelerate compute jobs such as machine learning and HPC. There are many GPUs available to suit different price points and performance levels. Flexible pricing and machine customizations are available to optimize your workload. High-performance GPUs available on Google Cloud for machine intelligence, scientific computing, 3D visualization, and machine learning. NVIDIA K80 and P100 GPUs, T4, V100 and A100 GPUs offer a variety of compute options to meet your workload's cost and performance requirements. You can optimize the processor, memory and high-performance disk for your specific workload by using up to 8 GPUs per instance. All this with per-second billing so that you only pay for what you use. You can run GPU workloads on Google Cloud Platform, which offers industry-leading storage, networking and data analytics technologies. Compute Engine offers GPUs that can be added to virtual machine instances. Learn more about GPUs and the types of hardware available.

The first hardware and software computing protocol that integrates both to train and fine tune your AI models. Reduce your training costs up to 80%. Deploy your model in seconds using open-source template models or bring your model. Debug containers easily with GPU, CPU and Memory metrics. You can monitor logs live. You can track all models and training set with cryptographic proofs to ensure complete accountability. You can control the entire training process with just a few commands. You can earn block rewards by adding your computer to the networking. Track key metrics like connectivity and uptime.

Substrate is a platform for agentic AI. Elegant abstractions, high-performance components such as optimized models, vector databases, code interpreter and model router, as well as vector databases, code interpreter and model router. Substrate was designed to run multistep AI workloads. Substrate will run your task as fast as it can by connecting components. We analyze your workload in the form of a directed acyclic network and optimize it, for example merging nodes which can be run as a batch. Substrate's inference engine schedules your workflow graph automatically with optimized parallelism. This reduces the complexity of chaining several inference APIs. Substrate will parallelize your workload without any async programming. Just connect nodes to let Substrate do the work. Our infrastructure ensures that your entire workload runs on the same cluster and often on the same computer. You won't waste fractions of a sec per task on unnecessary data transport and cross-regional HTTP transport.

Combining the performance of in-memory and the durability of disk storage allows for many uses beyond caching. Multi-region replication can be combined with global databases. True Serverless Kafka, where the price scales to zero. Per-request pricing means you only pay for what you use. The Kafka topic creator and consumer API is built-in. Start free and then only pay for what you use using per-request pricing. Don't waste your money on a server/instance. You can use Upstash as many times as you want, and you won't pay more than the cap price. Upstash REST API allows access from Cloudflare Workers or Fastly Compute@Edge. Access to your global database is possible from anywhere, with very low latency. Upstash is a great choice for the Jamstack or Serverless world due to its low latency, ease-of-use, and pay-per request pricing. Servers/instances can be rented for an hour or for a fixed price. Serverless charges per request.

Setup should be simple. We've listened carefully to the feedback of our community in order to simplify the developer experience. Our billing model was designed from the ground up for cloud-native. You only pay for what you need and there are no surprises. Launch times that are industry-leading will boost productivity. Accelerate the development cycle, innovate and deliver faster results. Blazing fast, simplified, managed Kubernetes. Host applications and scale them as you need, with a 90-second cluster launch time and a free controller plane. Kubernetes-powered enterprise-class compute instances. Multi-region support, DDoS Protection, bandwidth pooling and all the developer tool you need. Fully managed, auto-scaling machine-learning environment. No Kubernetes, ML or Kubernetes expertise is required. Setup and scale managed databases easily from your Civo dashboard, or our developer API. Scale up or down as needed, and only pay for the resources you use.

Through our core capabilities in cloud-using technology, we provide business insight and expertise to assist your organization in making better-informed decisions that will lead to more productive and profitable outcomes. Our digital services platform uses continuous intelligence to use real-time context data to manage, modernize, and migrate multi-cloud within their organization. With finely tuned automation at each stage, you can easily migrate cloud, virtual and physical workloads to and fro any environment. Securely and automatically copies all data. You can also capture any changes to one our cloud storage data centers. Manages two-speed information technology consumption, DevOps and monitoring with cloud transparency about AWS, Google, IBM and asset usage and costs. Experts in multi-cloud (AWS Azure, Google, IBM, and the next-generation tools)

Manage cloud resources with ease from a single screen. Accelerate the development process and get more computing for your budget. Reserved instances do not require any upfront capital investment. You can meet the fluctuating needs of your projects with confidence. Exostellar migrates HPC applications automatically to cheaper virtual machines. This optimizes resource utilization. Utilizes state-of-the art OVMA (Optimized virtual machine array), which consists a collection of instance type that share similar characteristics including cores and memory, SSD storage, bandwidth on the network, etc. Applications are continuously and seamlessly run without interruption. Switch between different instance types without any hassle. Maintains the same addresses and network connections. Enter your current AWS computing to discover the potential savings and enhanced performance that Exostellar’s XSpot technology could deliver to your business or application.

Amazon EC2 Trn2 instances powered by AWS Trainium2 are designed for high-performance deep-learning training of generative AI model, including large language models, diffusion models, and diffusion models. They can save up to 50% on the cost of training compared to comparable Amazon EC2 Instances. Trn2 instances can support up to 16 Trainium2 accelerations, delivering up to 3 petaflops FP16/BF16 computing power and 512GB of high bandwidth memory. Trn2 instances support up to 1600 Gbps second-generation Elastic Fabric Adapter network bandwidth. NeuronLink is a high-speed nonblocking interconnect that facilitates efficient data and models parallelism. They are deployed as EC2 UltraClusters and can scale up to 30,000 Trainium2 processors interconnected by a nonblocking, petabit-scale, network, delivering six exaflops in compute performance. The AWS neuron SDK integrates with popular machine-learning frameworks such as PyTorch or TensorFlow.

Fully managed infrastructure, tools and workflows allow you to build, train and deploy models faster. Scale inference and deploy custom AI & LLMs in seconds on any infrastructure. Schedule batch jobs to handle your most demanding tasks, and only pay per second. Optimize costs by utilizing GPUs, spot instances, and automatic failover. YAML simplifies complex infrastructure setups by allowing you to train with a single command. Automate the scaling up of workers during periods of high traffic, and scaling down to zero when inactive. Deploy cutting edge models with persistent endpoints within a serverless environment to optimize resource usage. Monitor system and inference metrics, including worker counts, GPU utilization, throughput, and latency in real-time. Split traffic between multiple models to evaluate.

Featherless, an AI model provider, offers its subscribers access to an ever-expanding library of Hugging Faces. You need dedicated tools to keep pace with the hype. With hundreds of models being added daily, you will need dedicated tools. Featherless lets you find and use the latest AI models, no matter what your use case is. LLaMA-3 models are supported, including LLaMA-3, QWEN-2, and LLaMA-3. Note that QWEN-2 models can only be supported up to 16 000 context length. Soon, we plan to add new architectures to the list of supported architectures. As new models become available on Hugging Face, we continue to add them. As we grow, our goal is to automate the process so that all Hugging Face models available publicly with compatible architecture are included. To ensure fair account usage, the number of concurrent requests is limited based on the plan selected. The output is delivered between 10-40 tokens/second, depending on the prompt size and model.

Fal is a serverless Python Runtime that allows you to scale your code on the cloud without any infrastructure management. Build real-time AI apps with lightning-fast inferences (under 120ms). You can start building AI applications with some of the models that are ready to use. They have simple API endpoints. Ship custom model endpoints that allow for fine-grained control of idle timeout, maximum concurrency and autoscaling. APIs are available for models like Stable Diffusion Background Removal ControlNet and more. These models will be kept warm for free. Join the discussion and help shape the future AI. Scale up to hundreds GPUs and down to zero GPUs when idle. Pay only for the seconds your code runs. You can use fal in any Python project simply by importing fal and wrapping functions with the decorator.

Alibaba Cloud Function Compute provides a fully managed, event-driven computing service. Function Compute lets you focus on writing and uploading code, without the need to manage servers or infrastructure. Function Compute offers compute resources that allow you to run your code efficiently and reliably. Function Compute also offers a generous amount free resources. There are no fees for up to 1,000,000 Invocations per month and 400,000 CU second compute resources.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

We provide a geo-distributed, real-time database, stream processing, and compute runtime for event driven applications across up to 175 global edge data centers. Our platform is loved by API and app developers because it solves the most difficult problems of sharing mutable states across hundreds of locations around the world. We also have high consistency and low latency. Macrometa allows you to surgically expand your existing infrastructure to bring your application closer to your users. This allows you to improve performance and user experience, as well as comply with global data governance laws. Macrometa is a streaming, serverless NoSQL database that can be used for stream data processing, pub/sub, and compute engines. You can create stateful data infrastructure, stateful function & containers for long-running workloads, and process data streams real time. We do the ops and orchestration, you write the code.

A modern Kubernetes native cloud, specifically designed for large-scale, GPU-accelerated workloads. CoreWeave was designed with engineers and innovators as its primary focus. It offers unprecedented access to a wide range of compute solutions that are up 35x faster than traditional cloud providers and up to 80% cheaper than legacy ones. Each component of our infrastructure was carefully designed to allow our clients to access the compute power they need to create and innovate. Our core differentiation is the ability to scale up or down in seconds. We're always available to meet customer demand. We mean it when we say that you can access thousands of GPUs in a matter of seconds. We provide compute at a fair price and the flexibility to configure your instances to your requirements.

OpenNebula is the Cloud & Edge Computing Platform. It offers flexibility, scalability and simplicity to meet the growing demands of developers and DevOps practitioners. OpenNebula, an open-source platform that allows you to create and manage Enterprise Clouds, is powerful but simple to use. OpenNebula allows for unified management of IT infrastructures and applications. This avoids vendor lock-in, reduces complexity, resource consumption, and reduces operational costs. OpenNebula combines container and virtualization technologies with multi-tenancy and automatic provisioning to offer on-demand services and applications.

Network isolation via sandboxed container and virtual private cloud (VPC), ensures application runtime security. SAE offers high availability solutions for large-scale events, which require high capacity handling, high scaling, and service throttle and degradation. Fully managed IaaS with Kubernetes Clusters provides low-cost solutions to your business. SAE scales in seconds and improves Java application startup efficiency. One-Stop PaaS that seamlessly integrates basic services, microservices and DevOps products. SAE offers full-lifecycle management of applications. Different release policies can be implemented, including phased release or canary release. You can also use the traffic-ratio-based, canary release model. The release process can be reverted to earlier stages.

Ametnes: A Streamlined Data App Deployment Management Ametnes is the future of data applications deployment. Our cutting-edge solution will revolutionize the way you manage data applications in your private environments. Manual deployment is a complex process that can be a security concern. Ametnes tackles these challenges by automating the whole process. This ensures a seamless, secure experience for valued customers. Our intuitive platform makes it easy to deploy and manage data applications. Ametnes unlocks the full potential of any private environment. Enjoy efficiency, security and simplicity in a way you've never experienced before. Elevate your data management game - choose Ametnes today!

You can reduce the complexity of your service architecture by writing only the core code and not worrying about peripheral components. SCF can scale up or down according to the number of requests, without any manual configuration. SCF can automatically allocate computing resources to meet your business needs, regardless of how many requests you have at any given moment. SCF can automatically use the infrastructure of other zones to execute code if a zone is unavailable due to a power outage or natural disaster. This eliminates the risk of service interruptions that are inherent in single-availability operations. SCF can trigger event-triggered workloads. It leverages different cloud services to meet different business scenarios. This will further strengthen your service architecture.

Our technology allows you create a hosted private cloud with all the features in just 45 seconds. Imagine it as the "first private cloud as a services". Cloud Core is the foundation of all hosted private clouds. OpenMetal Cloud Core is a hyperconverged set up of 3 hosted servers, of your choice of hardware type. OpenStack and Ceph power your cloud. This includes everything from Compute/VMs, Block Storage, powerful software defined networks to easy-to-deploy Kubernetes. Plus, tooling for Day 2 Operations, with built-in monitoring, all packaged up in a modern web portal. OpenMetal private clouds are API first systems that enable teams to use infrastructure like code. Terraform is recommended. Both CLI and GUI are available by default.

HPE Synergy, a software-defined infrastructure, is a composable infrastructure for hybrid cloud environments. It allows you to compose fluid pools for compute, storage and fabric resources in any configuration, for any workload, under a single API. All of this is available as a HPE GreenLake service. Manage one infrastructure to support current and next-gen applications, each with a vastly different infrastructure requirement and service-level objective. Accelerate application delivery and service delivery with a single interface. It precisely composes infrastructure in near-instantaneous speeds. HPE Synergy, powered by HPE OneView and designed with software-defined intelligent at its core, allows you to set up services with just a single line. Accelerate your business by using a developer-friendly architecture. The unified API allows for automation of infrastructure operations in conjunction with a large ecosystem of partners.

We have the infrastructure to meet all your computing needs, whether you need one or thousands GPUs or one vCPU or tens of thousand vCPUs. Our resources are available whenever you need them. Our platform makes switching between GPU and CPU instances a breeze. You can easily deploy, modify and rescale instances to meet your needs. You can get outstanding machine learning performance without breaking your bank. The latest technology for a much lower price. Modern GPUs are built to meet your workload demands. Get access to computing resources that are tailored for your models. Our OblivusAI OS allows you to access libraries and leverage our infrastructure for large-scale inference. Use our robust infrastructure to unleash the full potential of gaming by playing games in settings of your choosing.

The Barracuda Cloud, the first ecosystem to use on-demand cloud computing, simplifies data storage and IT management. Our cloud is an additional component to all Barracuda products and provides additional protection and scalability. You can choose how much Barracuda Cloud capability you require while still maintaining control on-premises over your digital information. Barracuda Cloud can be used on our onsite appliances, virtual appliances, and solutions on Amazon Web Services or Microsoft Azure. Software as a Service (SaaS), is also available for our email, web security, file sharing, electronic signature solutions, and file sharing. Barracuda security solutions include Barracuda Central subscriptions. This global operations center continuously monitors the Internet for threats and delivers solutions to customers in real-time.

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

Deep Learning Containers are Docker images pre-installed with the most popular deep learning frameworks. Deep Learning Containers allow you to quickly deploy custom ML environments without the need to build and optimize them from scratch. You can quickly deploy deep learning environments using prepackaged, fully tested Docker images. Integrate Amazon SageMaker, Amazon EKS and Amazon ECS to create custom ML workflows that can be used for validation, training, and deployment.

NVIDIA GPU Cloud is a GPU-accelerated cloud platform that is optimized for scientific computing and deep learning. NGC is responsible for a catalogue of fully integrated and optimized deep-learning framework containers that take full benefit of NVIDIA GPUs in single and multi-GPU configurations.

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.

Burncloud is one of the leading cloud computing providers, focusing on providing businesses with efficient, reliable and secure GPU rental services. Our platform is based on a systemized design that meets the high-performance computing requirements of different enterprises. Core Services Online GPU Rental Services - We offer a wide range of GPU models to rent, including data-center-grade devices and edge consumer computing equipment, in order to meet the diverse computing needs of businesses. Our best-selling products include: RTX4070, RTX3070 Ti, H100PCIe, RTX3090 Ti, RTX3060, NVIDIA4090, L40 RTX3080 Ti, L40S RTX4090, RTX3090, A10, H100 SXM, H100 NVL, A100PCIe 80GB, and many more. Our technical team has a vast experience in IB networking and has successfully set up five 256-node Clusters. Contact the Burncloud customer service team for cluster setup services.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

Find, provision and configure AI-ready Cloud instances for development, training and deployment. Install CUDA and Python automatically, load the model and SSH in. Brev.dev can help you find a GPU to train or fine-tune your model. A single interface for AWS, GCP and Lambda GPU clouds. Use credits as you have them. Choose an instance based upon cost & availability. A CLI that automatically updates your SSH configuration, ensuring it is done securely. Build faster using a better development environment. Brev connects you to cloud providers in order to find the best GPU for the lowest price. It configures the GPU and wraps SSH so that your code editor can connect to the remote machine. Change your instance. Add or remove a graphics card. Increase the size of your hard drive. Set up your environment so that your code runs always and is easy to share or copy. You can either create your own instance or use a template. The console should provide you with a few template options.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

NVIDIA AI Enterprise is the software layer of NVIDIA AI Platform. It accelerates the data science pipeline, streamlines development and deployments of production AI including generative AI, machine vision, speech AI, and more. NVIDIA AI Enterprise has over 50 frameworks, pre-trained models, and development tools. It is designed to help enterprises get to the forefront of AI while simplifying AI to make it more accessible to all. Artificial intelligence and machine learning are now mainstream and a key part of every company's competitive strategy. Enterprises face the greatest challenges when it comes to managing siloed infrastructure in the cloud and on-premises. AI requires that their environments be managed as a single platform and not as isolated clusters of compute.

Amazon SageMaker makes it easy for you to deploy ML models to make predictions (also called inference) at the best price and performance for your use case. It offers a wide range of ML infrastructure options and model deployment options to meet your ML inference requirements. It integrates with MLOps tools to allow you to scale your model deployment, reduce costs, manage models more efficiently in production, and reduce operational load. Amazon SageMaker can handle all your inference requirements, including low latency (a few seconds) and high throughput (hundreds upon thousands of requests per hour).

Unlock prices that are 3-5x higher than those of traditional clouds. FluidStack aggregates GPUs from data centres around the world that are underutilized to deliver the best economics in the industry. Deploy up to 50,000 high-performance servers within seconds using a single platform. In just a few days, you can access large-scale A100 or H100 clusters using InfiniBand. FluidStack allows you to train, fine-tune and deploy LLMs for thousands of GPUs at affordable prices in minutes. FluidStack unifies individual data centers in order to overcome monopolistic GPU pricing. Cloud computing can be made more efficient while allowing for 5x faster computation. Instantly access over 47,000 servers with tier four uptime and security through a simple interface. Train larger models, deploy Kubernetes Clusters, render faster, and stream without latency. Setup with custom images and APIs in seconds. Our engineers provide 24/7 direct support through Slack, email, or phone calls.

AWS Trainium, the second-generation machine-learning (ML) accelerator, is specifically designed by AWS for deep learning training with 100B+ parameter model. Each Amazon Elastic Comput Cloud (EC2) Trn1 example deploys up to sixteen AWS Trainium accelerations to deliver a low-cost, high-performance solution for deep-learning (DL) in the cloud. The use of deep-learning is increasing, but many development teams have fixed budgets that limit the scope and frequency at which they can train to improve their models and apps. Trainium based EC2 Trn1 instance solves this challenge by delivering a faster time to train and offering up to 50% savings on cost-to-train compared to comparable Amazon EC2 instances.