Alternatives to KServe

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

Nscale is a Norway company that was founded in 2024, and produces a software product named Nscale. Nscale includes training through documentation. Nscale has a free trial. Nscale is a type of AI inference software. Nscale includes online support. Regarding deployment requirements, Nscale is offered as SaaS software.

You can deploy Mystic in your own Azure/AWS/GCP accounts or in our shared GPU cluster. All Mystic features can be accessed directly from your cloud. In just a few steps, you can get the most cost-effective way to run ML inference. Our shared cluster of graphics cards is used by hundreds of users at once. Low cost, but performance may vary depending on GPU availability in real time. We solve the infrastructure problem. A Kubernetes platform fully managed that runs on your own cloud. Open-source Python API and library to simplify your AI workflow. You get a platform that is high-performance to serve your AI models. Mystic will automatically scale GPUs up or down based on the number API calls that your models receive. You can easily view and edit your infrastructure using the Mystic dashboard, APIs, and CLI.

Production-ready AI models that are fast and accurate. High-availability inference API that leverages the most advanced NVIDIA GPUs. We have selected the most popular open-source natural language processing models (NLP) and deployed them for the community. You can fine-tune your models (including GPT-J) or upload your custom models. Then, deploy them to production. Upload your AI models, including GPT-J, to your dashboard and immediately use them in production.

Self-service machine learning platform that allows you to turn models into APIs with just a few mouse clicks. Sign up for a Deep Infra Account using GitHub, or login using GitHub. Choose from hundreds of popular ML models. Call your model using a simple REST API. Our serverless GPUs allow you to deploy models faster and cheaper than if you were to build the infrastructure yourself. Depending on the model, we have different pricing models. Some of our models have token-based pricing. The majority of models are charged by the time it takes to execute an inference. This pricing model allows you to only pay for the services you use. You can easily scale your business as your needs change. There are no upfront costs or long-term contracts. All models are optimized for low latency and inference performance on A100 GPUs. Our system will automatically scale up the model based on your requirements.

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).

GMI GPU Cloud allows you to create generative AI applications within minutes. GMI Cloud offers more than just bare metal. Train, fine-tune and infer the latest models. Our clusters come preconfigured with popular ML frameworks and scalable GPU containers. Instantly access the latest GPUs to power your AI workloads. We can provide you with flexible GPUs on-demand or dedicated private cloud instances. Our turnkey Kubernetes solution maximizes GPU resources. Our advanced orchestration tools make it easy to allocate, deploy and monitor GPUs or other nodes. Create AI applications based on your data by customizing and serving models. GMI Cloud allows you to deploy any GPU workload quickly, so that you can focus on running your ML models and not managing infrastructure. Launch pre-configured environment and save time building container images, downloading models, installing software and configuring variables. You can also create your own Docker images to suit your needs.

NVIDIA Picasso, a cloud service that allows you to build generative AI-powered visual apps, is available. Software creators, service providers, and enterprises can run inference on models, train NVIDIA Edify foundation model models on proprietary data, and start from pre-trained models to create image, video, or 3D content from text prompts. The Picasso service is optimized for GPUs. It streamlines optimization, training, and inference on NVIDIA DGX Cloud. Developers and organizations can train NVIDIA Edify models using their own data, or use models pre-trained by our premier partners. Expert denoising network to create photorealistic 4K images The novel video denoiser and temporal layers generate high-fidelity videos with consistent temporality. A novel optimization framework to generate 3D objects and meshes of high-quality geometry. Cloud service to build and deploy generative AI-powered image and video applications.

NetMind.AI, a decentralized AI ecosystem and computing platform, is designed to accelerate global AI innovations. It offers AI computing power that is affordable and accessible to individuals, companies, and organizations of any size by leveraging idle GPU resources around the world. The platform offers a variety of services including GPU rental, serverless Inference, as well as an AI ecosystem that includes data processing, model development, inference and agent development. Users can rent GPUs for competitive prices, deploy models easily with serverless inference on-demand, and access a variety of open-source AI APIs with low-latency, high-throughput performance. NetMind.AI allows contributors to add their idle graphics cards to the network and earn NetMind Tokens. These tokens are used to facilitate transactions on the platform. Users can pay for services like training, fine-tuning and inference as well as GPU rentals.

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.

Fireworks works with the leading generative AI researchers in the world to provide the best models at the fastest speed. Independently benchmarked for the fastest inference providers. Use models curated by Fireworks, or our multi-modal and functionality-calling models that we have trained in-house. Fireworks is also the 2nd most popular open-source model provider, and generates more than 1M images/day. Fireworks' OpenAI-compatible interface makes it simple to get started. Dedicated deployments of your models will ensure uptime and performance. Fireworks is HIPAA-compliant and SOC2-compliant and offers secure VPC connectivity and VPN connectivity. Own your data and models. Fireworks hosts serverless models, so there's no need for hardware configuration or deployment. Fireworks.ai provides a lightning fast inference platform to help you serve generative AI model.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

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.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

OpenAI compatible, fast, lightweight, portable and powered by rust. We work with cloud providers to support microservices in web apps, especially edge cloud/CDN computing providers. Use cases include AI inferences, database accesses, CRM, ecommerce and workflow management. We work with streaming frameworks, databases and data to support embedded functions for data filtering. The serverless functions may be database UDFs. They could be embedded into data ingest streams or query results. Write once and run anywhere. Take full advantage of GPUs. In just 5 minutes, you can get started with the Llama 2 models on your device. Retrieval - Argumented Generation (RAG) has become a popular way to build AI agents using external knowledge bases. Create an HTTP microservice to classify images. It runs YOLO models and Mediapipe models natively at GPU speed.

NVIDIA Modulus, a neural network framework, combines the power of Physics in the form of governing partial differential equations (PDEs), with data to create high-fidelity surrogate models with near real-time latency. NVIDIA Modulus is a tool that can help you solve complex, nonlinear, multiphysics problems using AI. This tool provides the foundation for building physics machine learning surrogate models that combine physics and data. This framework can be applied to many domains and uses, including engineering simulations and life sciences. It can also be used to solve forward and inverse/data assimilation issues. Parameterized system representation that solves multiple scenarios in near real-time, allowing you to train once offline and infer in real-time repeatedly.

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.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Users build recommendation applications on Vespa, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

Nebula enables you to scale and deploy your AI projects quickly and easily2 on a highly robust GPU infrastructure. Nebula Cloud powered by Nvidia GPUs on demand allows you to train and infer models easily and securely. You can also create and manage containerized workloads using Nebula's easy-to-use orchestration layer. Access Nebula’s MLOps, low-code/no code engines and AI-powered applications to quickly and seamlessly deploy AI-powered apps for business teams. Choose from the Nebula containerized AI Cloud, your on-prem or any cloud. The Nebula Unify platform allows you to build and scale AI-enabled use cases for business in a matter weeks, not months.

Outspeed provides networking infrastructure and inference infrastructure for building fast, real-time AI voice and video apps. AI-powered speech and natural language processing for intelligent voice assistants. Automated transcription and voice-controlled system. Create interactive digital characters to be used as virtual hosts, AI tutors or customer service. Real-time animations and natural conversations are key to engaging digital interactions. Real-time AI visual for quality control, surveillance and touchless interaction. High-speed and accurate processing and analysis of video streams and images. AI-driven content generation for creating vast, detailed digital worlds efficiently. Ideal for virtual reality, architectural visualizations and game environments. Adapt's flexible SDK, infrastructure and SDK allows you to create custom multimodal AI solutions. Combine AI models, data and interaction modes to create innovative applications.

Generative AI has a profound impact on virtually every industry. It opens up new opportunities for creative workers and knowledge to solve the world's most pressing problems. NVIDIA is empowering generative AI with a powerful suite of cloud services, pretrained foundation models, cutting-edge frameworks and optimized inference engines. NVIDIA AI Foundations is an array of cloud services that enable customization across use cases in areas like text (NVIDIA NeMo™, NVIDIA Picasso), or biology (NVIDIA BIONeMo™. Enjoy the full potential of NeMo, Picasso and BioNeMo cloud-based services powered by NVIDIA DGX™ Cloud, an AI supercomputer. Marketing copy, storyline creation and global translation in many different languages. News, email, meeting minutes and information synthesis.

Groq's mission is to set the standard in GenAI inference speeds, enabling real-time AI applications to be developed today. LPU, or Language Processing Unit, inference engines are a new end-to-end system that can provide the fastest inference possible for computationally intensive applications, including AI language applications. The LPU was designed to overcome two bottlenecks in LLMs: compute density and memory bandwidth. In terms of LLMs, an LPU has a greater computing capacity than both a GPU and a CPU. This reduces the time it takes to calculate each word, allowing text sequences to be generated faster. LPU's inference engine can also deliver orders of magnitude higher performance on LLMs than GPUs by eliminating external memory bottlenecks. Groq supports machine learning frameworks like PyTorch TensorFlow and ONNX.

Amazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance.

Zyphra, an artificial intelligence company with offices in Palo Alto and Montreal, is growing in London. We're developing MaiaOS, an agent system that combines advanced research in next-gen neuronal network architectures (SSM-hybrids), long-term memories & reinforcement learning. We believe that the future of AGI is a combination of cloud-based and on-device strategies, with an increasing shift towards local inference. MaiaOS was built around a deployment platform that maximizes the efficiency of inference for real-time Intelligence. Our AI and product teams are drawn from top organizations and institutions, including Google DeepMind and Anthropic. They also come from Qualcomm, Neuralink and Apple. We have deep expertise across AI models, learning algorithms, and systems/infrastructure with a focus on inference efficiency and AI silicon performance. The Zyphra team is dedicated to democratizing advanced artificial intelligence systems.

NVIDIA is a United States company that was founded in 1993, and produces a software product named NVIDIA TensorRT. NVIDIA TensorRT includes training through documentation, webinars, in person sessions, and videos. NVIDIA TensorRT has a free version. NVIDIA TensorRT is a type of AI inference software. NVIDIA TensorRT includes phone support support and online support. Regarding deployment requirements, NVIDIA TensorRT is offered as SaaS and Windows software. Pricing starts at Free.

Active Inference is an innovative framework for agentic AI that uses world models. It is the result of over 30 years' research in computational neurology. We offer an AI that is built for power, computational efficiency and designed to be used on devices and at the edge. Our intelligent decision-making system integrates with traditional computer vision stacks to provide an explainable outcome that allows organizations and products to be held accountable. We are incorporating neuroscience into AI to build software that will enable robots and embodied platform to make autonomous decisions, just like the human brain.

A platform that manages model, project, and hosting. This platform allows companies to transform data and algorithms into comprehensive, execution-ready AI strategies. Securely build, train, and manage models. You can create products that use AI models from anywhere and at any time. Reduce the risks associated with AI investments while increasing strategic flexibility. Fully automated model testing, evaluation deployment and scaling. From real-time, low latency, high-throughput, inference to batch-running inference. Pay-per-second-of-use model, with an SLA, and full governance, monitoring and auditing tools. A user-friendly interface that serves as a central hub for managing projects, visualizing data, and training models through collaborative and reproducible workflows.

Amazon SageMaker Feature Store can be used to store, share and manage features for machine-learning (ML) models. Features are inputs to machine learning models that are used for training and inference. In an example, features might include song ratings, listening time, and listener demographics. Multiple teams may use the same features repeatedly, so it is important to ensure that the feature quality is high-quality. It can be difficult to keep the feature stores synchronized when features are used to train models offline in batches. SageMaker Feature Store is a secure and unified place for feature use throughout the ML lifecycle. To encourage feature reuse across ML applications, you can store, share, and manage ML-model features for training and inference. Any data source, streaming or batch, can be used to import features, such as application logs and service logs, clickstreams and sensors, etc.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

Pipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared.

Exafunction optimizes deep learning inference workloads, up to a 10% improvement in resource utilization and cost. Instead of worrying about cluster management and fine-tuning performance, focus on building your deep-learning application. Poor utilization of GPU hardware is a common problem in deep learning applications. Exafunction allows any GPU code to be moved to remote resources. This includes spot instances. Your core logic is still an inexpensive CPU instance. Exafunction has been proven to be effective in large-scale autonomous vehicle simulation. These workloads require complex custom models, high numerical reproducibility, and thousands of GPUs simultaneously. Exafunction supports models of major deep learning frameworks. Versioning models and dependencies, such as custom operators, allows you to be certain you are getting the correct results.

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.

Simplismart’s fastest inference engine allows you to fine-tune and deploy AI model with ease. Integrate with AWS/Azure/GCP, and many other cloud providers, for simple, scalable and cost-effective deployment. Import open-source models from popular online repositories, or deploy your custom model. Simplismart can host your model or you can use your own cloud resources. Simplismart allows you to go beyond AI model deployment. You can train, deploy and observe any ML models and achieve increased inference speed at lower costs. Import any dataset to fine-tune custom or open-source models quickly. Run multiple training experiments efficiently in parallel to speed up your workflow. Deploy any model to our endpoints, or your own VPC/premises and enjoy greater performance at lower cost. Now, streamlined and intuitive deployments are a reality. Monitor GPU utilization, and all of your node clusters on one dashboard. On the move, detect any resource constraints or model inefficiencies.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

Make AI an integral part of your business. With the flexibility and power of a cloud native platform, you can build better, deploy faster and audit easier. Import models and search cataloged model from across your organization. Save time by quickly annotating data with model-in the-loop hinting. Flyte's integration with Chariot allows you to quickly create and launch custom workflows. Understand the full origin of your data, models and workflows. Deploy models wherever you need them. This includes edge and IoT applications. Data scientists are not the only ones who can get valuable insights from their data. With Chariot's low code interface, teams can collaborate effectively.

Your deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

You can build and scale your computer vision model quickly and easily. NeuCore makes it easy to develop, train, and deploy your computer vision model in just minutes. You can scale it up to millions of times. One-stop platform that manages all aspects of the model lifecycle including training, development, deployment, maintenance, and maintenance. Advanced data encryption is used to protect your information throughout the entire process, from training to inference. Fully integrated vision AI models can be easily integrated into existing systems and workflows, or even onto edge devices. Seamless scaling allows for your evolving business needs and business requirements. Splits an image into sections that contain different objects. Machine-readable text extracted from images. This model can also be used to read handwriting. NeuCore makes it easy to build computer vision models. It's as simple as one-click and drag-and-drop. Advanced users can access code scripts and watch tutorial videos to customize the software.

Machine learning applications can be deployed to production in minutes instead of months. Automate the transformation of raw data and generate training data sets. Also, you can serve features for online inference at large scale. Replace bespoke data pipelines by robust pipelines that can be created, orchestrated, and maintained automatically. You can increase your team's efficiency and standardize your machine learning data workflows by sharing features throughout the organization. You can serve features in production at large scale with confidence that the systems will always be available. Tecton adheres to strict security and compliance standards. Tecton is neither a database nor a processing engine. It can be integrated into your existing storage and processing infrastructure and orchestrates it.

Horay.ai offers out-of-the box large model inference services, bringing an efficient user experience to generative AI applications. Horay.ai, a cutting edge cloud service platform, primarily offers APIs for large open-source models. Our platform provides a wide range of models, guarantees fast updates, and offers services at competitive rates. This allows developers to easily integrate advanced multimodal capabilities, natural language processing, and image generation into their applications. Horay.ai infrastructure allows developers to focus on innovation, rather than the complexity of model deployment and maintenance. Horay.ai was founded in 2024 by a team of AI experts. We are focused on serving generative AI developer, improving service quality and the user experience. Horay.ai offers reliable solutions for both startups and large enterprises to help them grow rapidly.

DeepCube is a company that focuses on deep learning technologies. This technology can be used to improve the deployment of AI systems in real-world situations. The company's many patent innovations include faster, more accurate training of deep-learning models and significantly improved inference performance. DeepCube's proprietary framework is compatible with any hardware, datacenters or edge devices. This allows for over 10x speed improvements and memory reductions. DeepCube is the only technology that allows for efficient deployment of deep-learning models on intelligent edge devices. The model is typically very complex and requires a lot of memory. Deep learning deployments today are restricted to the cloud because of the large amount of memory and processing requirements.

UbiOps provides an AI infrastructure platform to help teams run AI & ML workloads quickly as reliable and secure Microservices without disrupting their existing workflows. UbiOps can be integrated seamlessly into your data-science workbench in minutes. This will save you time and money by avoiding the hassle of setting up expensive cloud infrastructure. You can use UbiOps as a data science team in a large company or a start-up to launch an AI product. UbiOps is a reliable backbone to any AI or ML services. Scale AI workloads dynamically based on usage, without paying for idle times. Instantly access powerful GPUs for model training and inference, enhanced by serverless, multicloud workload distribution.

Launch GPU-accelerated instances that are highly configurable for your AI workload and budget. Reserve thousands of GPUs for training and inference in a next generation AI data center. The AI world is moving to GPU clouds in order to build and launch groundbreaking models without having the hassle of managing infrastructure or scarcity of resources. AI-centric cloud providers are outperforming traditional hyperscalers in terms of availability, compute costs, and scaling GPU utilization for complex AI workloads. Ori has a large pool with different GPU types that are tailored to meet different processing needs. This ensures that a greater concentration of powerful GPUs are readily available to be allocated compared to general purpose clouds. Ori offers more competitive pricing, whether it's for dedicated servers or on-demand instances. Our GPU compute costs are significantly lower than the per-hour and per-use pricing of legacy cloud services.

Time-series analysis is crucial for many companies' day-to-day operations. The most popular uses include analyzing foot traffic and conversions for retailers, detecting data abnormalities, identifying correlations over sensor data, and generating high-quality suggestions. Cloud Inference API Alpha allows you to gather insights from your time-series data in real-time. You can get all the information you need to understand your API query results, including the groups of events examined, the number and background probabilities of each event returned. You can stream data in real time, which makes it possible to calculate correlations for real events. Rely on Google Cloud's entire infrastructure and defense-in depth approach to security, which has been innovating for over 15 years via consumer apps. Cloud Inference API integrates seamlessly with other Google Cloud Storage services.

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).

Cloud-hosted AI packages that are managed and cloud-hosted will make it easier to ship AI faster. GPT-4 support is fully integrated. API tokens do not need to be used. Use our low-code framework to build. All major models can be integrated. Get an instant API by deploying. Scale and share your API without having to manage infrastructure. Make prompts, prompt chains, basic Python, and managed APIs. A clever prompt can be turned into a publicly available API that you can share. Python allows you to add logic and routing smarts. Steamship connects with your favorite models and services, so you don't need to learn a different API for each provider. Steamship maintains model output in a standard format. Consolidate training and inference, vector search, endpoint hosting. Import, transcribe or generate text. It can run all the models that you need. ShipQL allows you to query across all the results. Packages are fully-stack, cloud-hosted AI applications. Each instance you create gives you an API and private data workspace.

ThirdAI (pronunciation is /TH@rdi/ Third eye), is an Artificial Intelligence startup that specializes in scalable and sustainable AI. ThirdAI accelerator develops hash-based processing algorithms to train and infer with neural networks. This technology is the result of 10 years' worth of innovation in deep learning mathematics. Our algorithmic innovation has shown that Commodity x86 CPUs can be made 15x faster than the most powerful NVIDIA GPUs to train large neural networks. This demonstration has reaffirmed the belief that GPUs are superior to CPUs when it comes to training neural networks. Our innovation will not only benefit AI training currently by switching to cheaper CPUs but also allow for the "unlocking” of AI training workloads on GPUs previously not possible.

Your offline data can be used to make real-time predictions, without the need for custom pipelines. Data consistency is achieved between offline training and online prediction, eliminating train-serve bias. Standardize data engineering workflows within a consistent framework. Feast is used by teams to build their internal ML platforms. Feast doesn't require dedicated infrastructure to be deployed and managed. Feast reuses existing infrastructure and creates new resources as needed. You don't want a managed solution, and you are happy to manage your own implementation. Feast is supported by engineers who can help with its implementation and management. You are looking to build pipelines that convert raw data into features and integrate with another system. You have specific requirements and want to use an open-source solution.

WebLLM is an in-browser, high-performance language model inference engine. It uses WebGPU to accelerate the hardware, enabling powerful LLM functions directly within web browsers, without server-side processing. It is compatible with the OpenAI API, allowing seamless integration of functionalities like JSON mode, function calling, and streaming. WebLLM supports a wide range of models including Llama Phi Gemma Mistral Qwen and RedPajama. Users can easily integrate custom models into MLC format and adapt WebLLM to their specific needs and scenarios. The platform allows for plug-and play integration via package managers such as NPM and Yarn or directly through CDN. It also includes comprehensive examples and a module design to connect with UI components. It supports real-time chat completions, which enhance interactive applications such as chatbots and virtual assistances.

Qubrid AI is a company that specializes in Artificial Intelligence. Its mission is to solve complex real-world problems across multiple industries. Qubrid AI’s software suite consists of AI Hub, an all-in-one shop for AI models, AI Compute GPU cloud and On-Prem appliances, and AI Data Connector. You can train infer-leading models, or your own custom creations. All within a streamlined and user-friendly interface. Test and refine models with ease. Then, deploy them seamlessly to unlock the power AI in your projects. AI Hub enables you to embark on a journey of AI, from conception to implementation, in a single powerful platform. Our cutting-edge AI Compute Platform harnesses the power from GPU Cloud and On Prem Server Appliances in order to efficiently develop and operate next generation AI applications. Qubrid is a team of AI developers, research teams and partner teams focused on enhancing the unique platform to advance scientific applications.