Alternatives to Anyscale
Compare Anyscale alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Anyscale in 2024. Compare features, ratings, user reviews, pricing, and more from Anyscale competitors and alternatives in order to make an informed decision for your business.
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1
Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. -
2
BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
3
Amazon SageMaker
Amazon
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required. -
4
MosaicML
MosaicML
Train and serve large AI models at scale with a single command. Point to your S3 bucket and go. We handle the rest, orchestration, efficiency, node failures, and infrastructure. Simple and scalable. MosaicML enables you to easily train and deploy large AI models on your data, in your secure environment. Stay on the cutting edge with our latest recipes, techniques, and foundation models. Developed and rigorously tested by our research team. With a few simple steps, deploy inside your private cloud. Your data and models never leave your firewalls. Start in one cloud, and continue on another, without skipping a beat. Own the model that's trained on your own data. Introspect and better explain the model decisions. Filter the content and data based on your business needs. Seamlessly integrate with your existing data pipelines, experiment trackers, and other tools. We are fully interoperable, cloud-agnostic, and enterprise proved. -
5
VESSL AI
VESSL AI
Build, train, and deploy models faster at scale with fully managed infrastructure, tools, and workflows. Deploy custom AI & LLMs on any infrastructure in seconds and scale inference with ease. Handle your most demanding tasks with batch job scheduling, only paying with per-second billing. Optimize costs with GPU usage, spot instances, and built-in automatic failover. Train with a single command with YAML, simplifying complex infrastructure setups. Automatically scale up workers during high traffic and scale down to zero during inactivity. Deploy cutting-edge models with persistent endpoints in a serverless environment, optimizing resource usage. Monitor system and inference metrics in real-time, including worker count, GPU utilization, latency, and throughput. Efficiently conduct A/B testing by splitting traffic among multiple models for evaluation.Starting Price: $100 + compute/month -
6
cnvrg.io
cnvrg.io
Scale your machine learning development from research to production with an end-to-end solution that gives your data science team all the tools they need in one place. As the leading data science platform for MLOps and model management, cnvrg.io is a pioneer in building cutting-edge machine learning development solutions so you can build high-impact machine learning models in half the time. Bridge science and engineering teams in a clear and collaborative machine learning management environment. Communicate and reproduce results with interactive workspaces, dashboards, dataset organization, experiment tracking and visualization, a model repository and more. Focus less on technical complexity and more on building high impact ML models. Cnvrg.io container-based infrastructure helps simplify engineering heavy tasks like tracking, monitoring, configuration, compute resource management, serving infrastructure, feature extraction, and model deployment. -
7
Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. -
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The single development environment for the entire data science workflow. Natively analyze your data with a reduction in context switching between services. Data to training at scale. Build and train models 5X faster, compared to traditional notebooks. Scale-up model development with simple connectivity to Vertex AI services. Simplified access to data and in-notebook access to machine learning with BigQuery, Dataproc, Spark, and Vertex AI integration. Take advantage of the power of infinite computing with Vertex AI training for experimentation and prototyping, to go from data to training at scale. Using Vertex AI Workbench you can implement your training, and deployment workflows on Vertex AI from one place. A Jupyter-based fully managed, scalable, enterprise-ready compute infrastructure with security controls and user management capabilities. Explore data and train ML models with easy connections to Google Cloud's big data solutions.Starting Price: $10 per GB
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9
NeoPulse
AI Dynamics
The NeoPulse Product Suite includes everything needed for a company to start building custom AI solutions based on their own curated data. Server application with a powerful AI called “the oracle” that is capable of automating the process of creating sophisticated AI models. Manages your AI infrastructure and orchestrates workflows to automate AI generation activities. A program that is licensed by the organization to allow any application in the enterprise to access the AI model using a web-based (REST) API. NeoPulse is an end-to-end automated AI platform that enables organizations to train, deploy and manage AI solutions in heterogeneous environments, at scale. In other words, every part of the AI engineering workflow can be handled by NeoPulse: designing, training, deploying, managing and retiring. -
10
ClearML
ClearML
ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.Starting Price: $15 -
11
VectorShift
VectorShift
Build, design, prototype, and deploy custom generative AI workflows. Improve customer engagement and team/personal productivity. Build and embed into your website in minutes. Connect the chatbot with your knowledge base, and summarize and answer questions about documents, videos, audio files, and websites instantly. Create marketing copy, personalized outbound emails, call summaries, and graphics at scale. Save time by leveraging a library of pre-built pipelines such as chatbots and document search. Contribute to the marketplace by sharing your pipelines with other users. Our secure infrastructure and zero-day retention policy mean your data will not be stored by model providers. Our partnerships begin with a free diagnostic where we assess whether your organization is generative already and we create a roadmap for creating a turn-key solution using our platform to fit into your processes today. -
12
Mystic
Mystic
With Mystic you can deploy ML in your own Azure/AWS/GCP account or deploy in our shared GPU cluster. All Mystic features are directly in your own cloud. In a few simple steps, you get the most cost-effective and scalable way of running ML inference. Our shared cluster of GPUs is used by 100s of users simultaneously. Low cost but performance will vary depending on real-time GPU availability. Good AI products need good models and infrastructure; we solve the infrastructure part. A fully managed Kubernetes platform that runs in your own cloud. Open-source Python library and API to simplify your entire AI workflow. You get a high-performance platform to serve your AI models. Mystic will automatically scale up and down GPUs depending on the number of API calls your models receive. You can easily view, edit, and monitor your infrastructure from your Mystic dashboard, CLI, and APIs.Starting Price: Free -
13
Hugging Face
Hugging Face
A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models. AutoTrain is an automatic way to train and deploy state-of-the-art Machine Learning models, seamlessly integrated with the Hugging Face ecosystem. Your training data stays on our server, and is private to your account. All data transfers are protected with encryption. Available today: text classification, text scoring, entity recognition, summarization, question answering, translation and tabular. CSV, TSV or JSON files, hosted anywhere. We delete your training data after training is done. Hugging Face also hosts an AI content detection tool.Starting Price: $9 per month -
14
IBM watsonx.ai
IBM
Now available—a next generation enterprise studio for AI builders to train, validate, tune and deploy AI models IBM® watsonx.ai™ AI studio is part of the IBM watsonx™ AI and data platform, bringing together new generative AI (gen AI) capabilities powered by foundation models and traditional machine learning (ML) into a powerful studio spanning the AI lifecycle. Tune and guide models with your enterprise data to meet your needs with easy-to-use tools for building and refining performant prompts. With watsonx.ai, you can build AI applications in a fraction of the time and with a fraction of the data. Watsonx.ai offers: End-to-end AI governance: Enterprises can scale and accelerate the impact of AI with trusted data across the business, using data wherever it resides. Hybrid, multi-cloud deployments: IBM provides the flexibility to integrate and deploy your AI workloads into your hybrid-cloud stack of choice. -
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IBM watsonx
IBM
Watsonx is our upcoming enterprise-ready AI and data platform designed to multiply the impact of AI across your business. The platform comprises three powerful components: the watsonx.ai studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose store for the flexibility of a data lake and the performance of a data warehouse; plus the watsonx.governance toolkit, to enable AI workflows that are built with responsibility, transparency and explainability. Watsonx is our enterprise-ready AI and data platform designed to multiply the impact of AI across your business. The platform comprises three powerful products: the watsonx.ai studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose data store, built on an open lakehouse architecture; and the watsonx.governance toolkit, to accelerate AI workflows that are built with responsibility, transparency and explainability. -
16
Klu
Klu
Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context. Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity. Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.Starting Price: $97 -
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NVIDIA AI Enterprise
NVIDIA
The software layer of the NVIDIA AI platform, NVIDIA AI Enterprise accelerates the data science pipeline and streamlines development and deployment of production AI including generative AI, computer vision, speech AI and more. With over 50 frameworks, pretrained models and development tools, NVIDIA AI Enterprise is designed to accelerate enterprises to the leading edge of AI, while also simplifying AI to make it accessible to every enterprise. The adoption of artificial intelligence and machine learning has gone mainstream, and is core to nearly every company’s competitive strategy. One of the toughest challenges for enterprises is the struggle with siloed infrastructure across the cloud and on-premises data centers. AI requires their environments to be managed as a common platform, instead of islands of compute. -
18
Deep Infra
Deep Infra
Powerful, self-serve machine learning platform where you can turn models into scalable APIs in just a few clicks. Sign up for Deep Infra account using GitHub or log in using GitHub. Choose among hundreds of the most popular ML models. Use a simple rest API to call your model. Deploy models to production faster and cheaper with our serverless GPUs than developing the infrastructure yourself. We have different pricing models depending on the model used. Some of our language models offer per-token pricing. Most other models are billed for inference execution time. With this pricing model, you only pay for what you use. There are no long-term contracts or upfront costs, and you can easily scale up and down as your business needs change. All models run on A100 GPUs, optimized for inference performance and low latency. Our system will automatically scale the model based on your needs.Starting Price: $0.70 per 1M input tokens -
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NVIDIA Base Command Platform
NVIDIA
NVIDIA Base Command™ Platform is a software service for enterprise-class AI training that enables businesses and their data scientists to accelerate AI development. Part of the NVIDIA DGX™ platform, Base Command Platform provides centralized, hybrid control of AI training projects. It works with NVIDIA DGX Cloud and NVIDIA DGX SuperPOD. Base Command Platform, in combination with NVIDIA-accelerated AI infrastructure, provides a cloud-hosted solution for AI development, so users can avoid the overhead and pitfalls of deploying and running a do-it-yourself platform. Base Command Platform efficiently configures and manages AI workloads, delivers integrated dataset management, and executes them on right-sized resources ranging from a single GPU to large-scale, multi-node clusters in the cloud or on-premises. Because NVIDIA’s own engineers and researchers rely on it every day, the platform receives continuous software enhancements. -
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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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21
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). -
22
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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23
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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24
Banana
Banana
Banana was started based on a critical gap that we saw in the market. Machine learning is in high demand. Yet, deploying models into production is deeply technical and complex. Banana is focused on building the machine learning infrastructure for the digital economy. We're simplifying the process to deploy, making productionizing models as simple as copying and pasting an API. This enables companies of all sizes to access and leverage state-of-the-art models. We believe that the democratization of machine learning will be one of the critical components fueling the growth of companies on a global scale. We see machine learning as the biggest technological gold rush of the 21st century and Banana is positioned to provide the picks and shovels.Starting Price: $7.4868 per hour -
25
Griptape
Griptape AI
Build, deploy, and scale end-to-end AI applications in the cloud. Griptape gives developers everything they need to build, deploy, and scale retrieval-driven AI-powered applications, from the development framework to the execution runtime. 🎢 Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. ☁️ Griptape Cloud is a one-stop shop to hosting your AI structures, whether they are built with Griptape, another framework, or call directly to the LLMs themselves. Simply point to your GitHub repository to get started. 🔥 Run your hosted code by hitting a basic API layer from wherever you need, offloading the expensive tasks of AI development to the cloud. 📈 Automatically scale workloads to fit your needs.Starting Price: Free -
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Together AI
Together AI
Whether prompt engineering, fine-tuning, or training, we are ready to meet your business demands. Easily integrate your new model into your production application using the Together Inference API. With the fastest performance available and elastic scaling, Together AI is built to scale with your needs as you grow. Inspect how models are trained and what data is used to increase accuracy and minimize risks. You own the model you fine-tune, not your cloud provider. Change providers for whatever reason, including price changes. Maintain complete data privacy by storing data locally or in our secure cloud.Starting Price: $0.0001 per 1k tokens -
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Azure OpenAI Service
Microsoft
Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.Starting Price: $0.0004 per 1000 tokens -
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aiXplain
aiXplain
We offer a unified set of world class tools and assets for seamless conversion of ideas into production-ready AI solutions. Build and deploy end-to-end custom Generative AI solutions on our unified platform, skipping the hassle of tool fragmentation and platform-switching. Launch your next AI solution through a single API endpoint. Creating, maintaining, and improving AI systems has never been this easy. Discover is aiXplain’s marketplace for models and datasets from various suppliers. Subscribe to models and datasets to use them with aiXplain no-code/low-code tools or through the SDK in your own code. -
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Stacktape
Stacktape
Stacktape is a DevOps-free cloud framework that’s both powerful and easy at the same time. It allows you to develop, deploy and run applications on AWS. With 98% less configuration and without the need for DevOps or Cloud expertise. Unlike with other solutions, you can deploy both serverless (AWS lambda-based) and more traditional (container-based) applications. Stacktape also supports 20+ infrastructure components, including SQL databases, Load balancers, MongoDB Atlas clusters, Batch-jobs, Kafka topics, Redis clusters & more. Besides infrastructure management, Stacktape handles source code packaging, deployments, local/remote development, and much more. It also comes with a VScode extension and local development studio (GUI). Stacktape is a IaaC tool. A typical production-grade REST API is ~30 lines of config (compared to ~600-800 lines of CloudFormation/Terraform). The deployment can be done using a single command - from local machine or a CI/CD pipeline.Starting Price: $450/month -
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FluidStack
FluidStack
Unlock 3-5x better prices than traditional clouds. FluidStack aggregates under-utilized GPUs from data centers around the world to deliver the industry’s best economics. Deploy 50,000+ high-performance servers in seconds via a single platform and API. Access large-scale A100 and H100 clusters with InfiniBand in days. Train, fine-tune, and deploy LLMs on thousands of affordable GPUs in minutes with FluidStack. FluidStack unites individual data centers to overcome monopolistic GPU cloud pricing. Compute 5x faster while making the cloud efficient. Instantly access 47,000+ unused servers with tier 4 uptime and security from one simple interface. Train larger models, deploy Kubernetes clusters, render quicker, and stream with no latency. Setup in one click with custom images and APIs to deploy in seconds. 24/7 direct support via Slack, emails, or calls, our engineers are an extension of your team.Starting Price: $1.49 per month -
31
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. -
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Oblivus
Oblivus
Our infrastructure is equipped to meet your computing requirements, be it one or thousands of GPUs, or one vCPU to tens of thousands of vCPUs, we've got you covered. Our resources are readily available to cater to your needs, whenever you need them. Switching between GPU and CPU instances is a breeze with our platform. You have the flexibility to deploy, modify, and rescale your instances according to your needs, without any hassle. Outstanding machine learning performance without breaking the bank. The latest technology at a significantly lower cost. Cutting-edge GPUs are designed to meet the demands of your workloads. Gain access to computational resources that are tailored to suit the intricacies of your models. Leverage our infrastructure to perform large-scale inference and access necessary libraries with our OblivusAI OS. Unleash the full potential of your gaming experience by utilizing our robust infrastructure to play games in the settings of your choice.Starting Price: $0.29 per hour -
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Google Cloud TPU
Google
Machine learning has produced business and research breakthroughs ranging from network security to medical diagnoses. We built the Tensor Processing Unit (TPU) in order to make it possible for anyone to achieve similar breakthroughs. Cloud TPU is the custom-designed machine learning ASIC that powers Google products like Translate, Photos, Search, Assistant, and Gmail. Here’s how you can put the TPU and machine learning to work accelerating your company’s success, especially at scale. Cloud TPU is designed to run cutting-edge machine learning models with AI services on Google Cloud. And its custom high-speed network offers over 100 petaflops of performance in a single pod, enough computational power to transform your business or create the next research breakthrough. Training machine learning models is like compiling code: you need to update often, and you want to do so as efficiently as possible. ML models need to be trained over and over as apps are built, deployed, and refined.Starting Price: $0.97 per chip-hour -
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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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OctoAI
OctoML
OctoAI is world-class compute infrastructure for tuning and running models that wow your users. Fast, efficient model endpoints and the freedom to run any model. Leverage OctoAI’s accelerated models or bring your own from anywhere. Create ergonomic model endpoints in minutes, with only a few lines of code. Customize your model to fit any use case that serves your users. Go from zero to millions of users, never worrying about hardware, speed, or cost overruns. Tap into our curated list of best-in-class open-source foundation models that we’ve made faster and cheaper to run using our deep experience in machine learning compilation, acceleration techniques, and proprietary model-hardware performance technology. OctoAI automatically selects the optimal hardware target, applies the latest optimization technologies, and always keeps your running models in an optimal manner. -
36
Nebius
Nebius
Training-ready platform with NVIDIA® H100 Tensor Core GPUs. Competitive pricing. Dedicated support. Built for large-scale ML workloads: Get the most out of multihost training on thousands of H100 GPUs of full mesh connection with latest InfiniBand network up to 3.2Tb/s per host. Best value for money: Save at least 50% on your GPU compute compared to major public cloud providers*. Save even more with reserves and volumes of GPUs. Onboarding assistance: We guarantee a dedicated engineer support to ensure seamless platform adoption. Get your infrastructure optimized and k8s deployed. Fully managed Kubernetes: Simplify the deployment, scaling and management of ML frameworks on Kubernetes and use Managed Kubernetes for multi-node GPU training. Marketplace with ML frameworks: Explore our Marketplace with its ML-focused libraries, applications, frameworks and tools to streamline your model training. Easy to use. We provide all our new users with a 1-month trial period.Starting Price: $2.66/hour -
37
Spot Ocean
Spot by NetApp
Spot Ocean lets you reap the benefits of Kubernetes without worrying about infrastructure while gaining deep cluster visibility and dramatically reducing costs. The key question is how to use containers without the operational overhead of managing the underlying VMs while also take advantage of the cost benefits associated with Spot Instances and multi-cloud. Spot Ocean is built to solve this problem by managing containers in a “Serverless” environment. Ocean provides an abstraction on top of virtual machines allowing to deploy Kubernetes clusters without the need to manage the underlying VMs. Ocean takes advantage of multiple compute purchasing options like Reserved and Spot instance pricing and failover to On-Demand instances whenever necessary, providing 80% reduction in infrastructure costs. Spot Ocean is a Serverless Compute Engine that abstracts the provisioning (launching), auto-scaling, and management of worker nodes in Kubernetes clusters. -
38
JFrog Platform
JFrog
Fully automated DevOps platform for distributing trusted software releases from code to production. Onboard DevOps projects with users, resources and permissions for faster deployment frequency. Fearlessly update with proactive identification of open source vulnerabilities and license compliance violations. Achieve zero downtime across your DevOps pipeline with High Availability and active/active clustering for your enterprise. Control your DevOps environment with out-of-the-box native and ecosystem integrations. Enterprise ready with choice of on-prem, cloud, multi-cloud or hybrid deployments that scale as you grow. Ensure speed, reliability and security of IoT software updates and device management at scale. Create new DevOps projects in minutes and easily onboard team members, resources and storage quotas to get coding faster.Starting Price: $98 per month -
39
Shoreline
Shoreline.io
Shoreline is the Cloud Reliability platform — the only platform that lets DevOps engineers build automations in an afternoon, and fix issues forever. Shoreline reduces on-call complexity by running across clouds, Kubernetes clusters, and VMs allowing operators to manage their entire fleet as if it were a single box. Debugging and repairing issues is easy with advanced tooling for your best SREs, automated runbooks for the broader team, and a platform that makes building automations 30X faster. Shoreline does the heavy lifting, setting up monitors and building repair scripts, so that customers only need to configure them for their environment. Shoreline’s modern “Operations at the Edge” architecture runs efficient agents in the background of all monitored hosts. Agents run as a DaemonSet on Kubernetes or an installed package on VMs (apt, yum). The Shoreline backend is hosted by Shoreline in AWS, or deployed in your AWS virtual private cloud. -
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IBM Cloud® Schematics provides automation by offering declarative Terraform templates to ensure a desired provisioned cloud infrastructure. Native integration with Red Hat® Ansible extends configuration, management and provisioning to software and applications, and integrates with other IBM Cloud Services. With Terraform-as-a-Service, DevOps teams can use a high-level configuration language to model the resources they want in their cloud environment and enable Infrastructure as Code (IaC). Install software packages and application code on your infrastructure easily. Have your team build, deploy and iterate on your infrastructure automation processes. Improve the DevOps lifecycle, from planning and builds to software testing and application monitoring. Employ Satellite and Schematics to automate the creation of Satellite locations and Red Hat OpenShift® on IBM Cloud.
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41
Argonaut
Argonaut
Argonaut accelerates your shift to the cloud. You can go from provisioning environments to configuring your cloud to deploying your app in a matter of minutes. Our product provides you with built-in access to the best practices of your preferred stack, complete visibility into the health and costs of your infrastructure, and a plan that scales as you grow. Our customers have saved weeks of their cloud deployment timelines and thousands of dollars each month. You can get started for free and see the benefits for yourself.Starting Price: $25 per month -
42
Quali CloudShell
Quali
None of the bottlenecks, all the best practices. Automate multi-cloud provisioning and deployment, policy-driven compliance and cloud cost management. Torque is a SaaS platform delivering Infrastructure Automation at Scale for complex, application-centric environments on cloud technologies including AWS, Azure, and Kubernetes. IT leaders and DevOps innovators around the world trust Quali to enable self-service automation and governance to streamline application development, testing, and release to production. Seamlessly connect cloud automation, cost control, and security into your platform, and make the power of Environments as a Service accessible to any user through self-service, even if they are not cloud experts. Deliver the infrastructure & application resources your teams need with velocity and control. Give developers on-demand access to the application environments they need through their CI/CD tools, GUI, and CLI. -
43
Context Data
Context Data
Context Data is an enterprise data infrastructure built to accelerate the development of data pipelines for Generative AI applications. The platform automates the process of setting up internal data processing and transformation flows using an easy-to-use connectivity framework where developers and enterprises can quickly connect to all of their internal data sources, embedding models and vector database targets without having to set up expensive infrastructure or engineers. The platform also allows developers to schedule recurring data flows for refreshed and up-to-date data.Starting Price: $99 per month -
44
Azure AI Studio
Microsoft
Your platform for developing generative AI solutions and custom copilots. Build solutions faster, using pre-built and customizable AI models on your data—securely—to innovate at scale. Explore a robust and growing catalog of pre-built and customizable frontier and open-source models. Create AI models with a code-first experience and accessible UI validated by developers with disabilities. Seamlessly integrate all your data from OneLake in Microsoft Fabric. Integrate with GitHub Codespaces, Semantic Kernel, and LangChain. Access prebuilt capabilities to build apps quickly. Personalize content and interactions and reduce wait times. Lower the burden of risk and aid in new discoveries for organizations. Decrease the chance of human error using data and tools. Automate operations to refocus employees on more critical tasks. -
45
DuploCloud
DuploCloud
No-code/low-code infrastructure automation for cloud security and compliance done right the first time. Use DuploCloud. Automated provisioning and orchestration across the network, compute, storage, containers, cloud-native services, continuous compliance and developer guardrails, with 24/7 support. DuploCloud accelerates time to compliance by natively integrating security controls into SecOps workflows the first time, including, monitoring and alerting for PCI-DSS, HIPAA, SOC 2 and GDPR. Easily migrate on-premises to cloud or cloud to cloud with seamless automation and unique data migration techniques to minimize downtime. DuploCloud’s no-code/low-code software platform is your DevSecOps expert, speeding time-to-market by translating high-level application specifications into detailed and fully managed cloud configurations. With pre-programmed knowledge of over 500 cloud services, the platform automatically creates and provisions all the necessary infrastructure-as-code for you app.Starting Price: $2,000 per month -
46
Katonic
Katonic
Build powerful enterprise-grade AI applications in minutes, without any coding on the Katonic generative AI platform. Boost the productivity of your employees and take your customer experience to the next level with the power of generative AI. Build AI-powered chatbots and digital assistants that can access and process information from documents or dynamic content refreshed automatically through pre-built connectors. Identify and extract essential information from unstructured text or surface insights in specialized domain areas without having to create any templates. Transform dense text into a personalized executive overview, capturing key points from financial reports, meeting transcriptions, and more. Build recommendation systems that can suggest products, services, or content to users based on their past behavior and preferences. -
47
Daria
XBrain
Daria’s advanced automated features allow users to quickly and easily build predictive models, significantly cutting back on days and weeks of iterative work associated with the traditional machine learning process. Remove financial and technological barriers to build AI systems from scratch for enterprises. Streamline and expedite workflows by lifting weeks of iterative work through automated machine learning for data experts. Get hands-on experience in machine learning with an intuitive GUI for data science beginners. Daria provides various data transformation functions to conveniently construct multiple feature sets. Daria automatically explores through millions of possible combinations of algorithms, modeling techniques and hyperparameters to select the best predictive model. Predictive models built with Daria can be deployed straight to production with a single line of code via Daria’s RESTful API. -
48
Lambda GPU Cloud
Lambda
Train the most demanding AI, ML, and Deep Learning models. Scale from a single machine to an entire fleet of VMs with a few clicks. Start or scale up your Deep Learning project with Lambda Cloud. Get started quickly, save on compute costs, and easily scale to hundreds of GPUs. Every VM comes preinstalled with the latest version of Lambda Stack, which includes major deep learning frameworks and CUDA® drivers. In seconds, access a dedicated Jupyter Notebook development environment for each machine directly from the cloud dashboard. For direct access, connect via the Web Terminal in the dashboard or use SSH directly with one of your provided SSH keys. By building compute infrastructure at scale for the unique requirements of deep learning researchers, Lambda can pass on significant savings. Benefit from the flexibility of using cloud computing without paying a fortune in on-demand pricing when workloads rapidly increase.Starting Price: $1.25 per hour -
49
GMI Cloud
GMI Cloud
Build your generative AI applications in minutes on GMI GPU Cloud. GMI Cloud is more than bare metal. Train, fine-tune, and infer state-of-the-art models. Our clusters are ready to go with scalable GPU containers and preconfigured popular ML frameworks. Get instant access to the latest GPUs for your AI workloads. Whether you need flexible on-demand GPUs or dedicated private cloud instances, we've got you covered. Maximize GPU resources with our turnkey Kubernetes software. Easily allocate, deploy, and monitor GPUs or nodes with our advanced orchestration tools. Customize and serve models to build AI applications using your data. GMI Cloud lets you deploy any GPU workload quickly and easily, so you can focus on running ML models, not managing infrastructure. Launch pre-configured environments and save time on building container images, installing software, downloading models, and configuring environment variables. Or use your own Docker image to fit your needs.Starting Price: $2.50 per hour -
50
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.
