Best On-Premises AI Infrastructure Platforms

Mistral AI is a pioneering artificial intelligence startup specializing in open-source generative AI. The company offers a range of customizable, enterprise-grade AI solutions deployable across various platforms, including on-premises, cloud, edge, and devices. Flagship products include "Le Chat," a multilingual AI assistant designed to enhance productivity in both personal and professional contexts, and "La Plateforme," a developer platform that enables the creation and deployment of AI-powered applications. Committed to transparency and innovation, Mistral AI positions itself as a leading independent AI lab, contributing significantly to open-source AI and policy development.

Domino is an enterprise AI platform designed to help organizations build, deploy, and scale AI systems that deliver real business outcomes. It provides end-to-end support for the AI lifecycle, from data science experimentation to production deployment and governance. The platform enables teams to access data, tools, and compute resources through a self-service environment with built-in IT controls. Domino supports the development of machine learning models, generative AI applications, and AI agents using preferred tools and frameworks. It also includes governance features such as model tracking, audit trails, and policy enforcement to ensure compliance and transparency. With hybrid and multi-cloud capabilities, organizations can run AI workloads across on-premises and cloud environments. Overall, Domino helps enterprises operationalize AI at scale while maintaining control, security, and efficiency.

Introducing Ametnes: Streamlined Data Application Deployment and Management Experience the future of data application deployment with Ametnes. Our cutting-edge solution revolutionizes the way you handle data applications in your private environment. Say goodbye to the complexities and security concerns of manual deployment. Ametnes addresses these challenges head-on by automating the entire process, ensuring a seamless and secure experience for our valued customers. With our intuitive platform, deploying and managing data applications has never been more astonishingly easy. Unlock the full potential of your private environment with Ametnes. Embrace efficiency, security, and simplicity like never before. Elevate your data management game - choose Ametnes today!

Zerve is the agentic data workspace designed for anyone who works with data, from solo analysts, data scientists, business users and teams alike. Zerve brings together exploration, advanced analysis, collaboration, and production deployment into a single AI-native environment, so that important data work doesn’t stall, break, or disappear. Zerve’s AI agents understand the full context of a project and actively help plan, build, debug, and iterate across multi-step analyses. Agents assist with tasks like cleaning and transforming data, identifying issues, and testing approaches, reducing the manual effort that slows teams down. This means working at a higher level of abstraction without being slowed by setup or syntax. Zerve can be used as SaaS, self-hosted, or even on-premise for highly regulated environments. Zerve is used by data professionals in companies such as BBC, QVC, Dun & Bradstreet, Airbus, and many others.

GMI Cloud provides a complete platform for building scalable AI solutions with enterprise-grade GPU access and rapid model deployment. Its Inference Engine offers ultra-low-latency performance optimized for real-time AI predictions across a wide range of applications. Developers can deploy models in minutes without relying on DevOps, reducing friction in the development lifecycle. The platform also includes a Cluster Engine for streamlined container management, virtualization, and GPU orchestration. Users can access high-performance GPUs, InfiniBand networking, and secure, globally scalable infrastructure. Paired with popular open-source models like DeepSeek R1 and Llama 3.3, GMI Cloud delivers a powerful foundation for training, inference, and production AI workloads.

Mistral AI Studio is a unified builder-platform that enables organizations and development teams to design, customize, deploy, and manage advanced AI agents, models, and workflows from proof-of-concept through to production. The platform offers reusable blocks, including agents, tools, connectors, guardrails, datasets, workflows, and evaluations, combined with observability and telemetry capabilities so you can track agent performance, trace root causes, and govern production AI operations with visibility. With modules like Agent Runtime to make multi-step AI behaviors repeatable and shareable, AI Registry to catalogue and manage model assets, and Data & Tool Connections for seamless integration with enterprise systems, Studio supports everything from fine-tuning open source models to embedding them in your infrastructure and rolling out enterprise-grade AI solutions.

Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.

Instill Core is an all-in-one AI infrastructure tool for data, model, and pipeline orchestration, streamlining the creation of AI-first applications. Access is easy via Instill Cloud or by self-hosting from the instill-core GitHub repository. Instill Core includes: Instill VDP: The Versatile Data Pipeline (VDP), designed for unstructured data ETL challenges, providing robust pipeline orchestration. Instill Model: An MLOps/LLMOps platform that ensures seamless model serving, fine-tuning, and monitoring for optimal performance with unstructured data ETL. Instill Artifact: Facilitates data orchestration for unified unstructured data representation. Instill Core simplifies the development and management of sophisticated AI workflows, making it indispensable for developers and data scientists leveraging AI technologies.

Qubrid AI is an advanced Artificial Intelligence (AI) company with a mission to solve real world complex problems in multiple industries. Qubrid AI’s software suite comprises of AI Hub, a one-stop shop for everything AI models, AI Compute GPU Cloud and On-Prem Appliances and AI Data Connector! Train our inference industry-leading models or your own custom creations, all within a streamlined, user-friendly interface. Test and refine your models with ease, then seamlessly deploy them to unlock the power of AI in your projects. AI Hub empowers you to embark on your AI Journey, from concept to implementation, all in a single, powerful platform. Our leading cutting-edge AI Compute platform harnesses the power of GPU Cloud and On-Prem Server Appliances to efficiently develop and run next generation AI applications. Qubrid team is comprised of AI developers, researchers and partner teams all focused on enhancing this unique platform for the advancement of scientific applications.

​ModelArts is a comprehensive AI development platform provided by Huawei Cloud, designed to streamline the entire AI workflow for developers and data scientists. It offers a full-lifecycle toolchain that includes data preprocessing, semi-automated data labeling, distributed training, automated model building, and flexible deployment options across cloud, edge, and on-premises environments. It supports popular open source AI frameworks such as TensorFlow, PyTorch, and MindSpore, and allows for the integration of custom algorithms tailored to specific needs. ModelArts features an end-to-end development pipeline that enhances collaboration across DataOps, MLOps, and DevOps, boosting development efficiency by up to 50%. It provides cost-effective AI computing resources with diverse specifications, enabling large-scale distributed training and inference acceleration.

NVIDIA Base Command™ 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.

Modular is a unified AI inference platform designed to run models efficiently across diverse hardware environments. It enables developers to deploy and scale AI workloads on GPUs, CPUs, and ASICs using a single, integrated stack. The platform optimizes performance from low-level GPU kernels to high-level API endpoints. Modular supports both managed cloud deployments and self-hosted environments, offering flexibility for different use cases. It allows users to run open-source or custom models with high performance and cost efficiency. With features like hardware portability and dynamic scaling, it reduces vendor lock-in and infrastructure complexity. By combining performance optimization and deployment simplicity, Modular helps teams build and run AI applications at scale.

Pipeshift is a modular orchestration platform designed to facilitate the building, deployment, and scaling of open source AI components, including embeddings, vector databases, large language models, vision models, and audio models, across any cloud environment or on-premises infrastructure. The platform offers end-to-end orchestration, ensuring seamless integration and management of AI workloads, and is 100% cloud-agnostic, providing flexibility in deployment. With enterprise-grade security, Pipeshift addresses the needs of DevOps and MLOps teams aiming to establish production pipelines in-house, moving beyond experimental API providers that may lack privacy considerations. Key features include an enterprise MLOps console for managing various AI workloads such as fine-tuning, distillation, and deployment; multi-cloud orchestration with built-in auto-scalers, load balancers, and schedulers for AI models; and Kubernetes cluster management.

Purpose-built to run AI anywhere on data everywhere. Our solution unlocks the value of your unstructured data, allowing you to access, prepare, train, fine-tune, and drive AI without any limitations. We’ve joined our industry-leading file and object storage portfolio, including PowerScale, ECS, and ObjectScale, with our PowerEdge server and open, modern data lakehouse approach. This gives you the power to bring AI to your unstructured data, on-premises, at the edge, and in any cloud with the highest performance and infinite scale. Access a full team of trained data scientists and industry experts who will help you implement AI use cases that deliver the most value for your business. Protect, detect, and respond to cyber attackers with hardened software and hardware security and real-time threat detection. Train and fine-tune your AI models using a single point of data access and the highest performance, on-premises, at the edge, and in any cloud.

SwarmOne is an autonomous infrastructure platform designed to streamline the entire AI lifecycle, from training to deployment, by automating and optimizing AI workloads across any environment. With just two lines of code and a one-click hardware installation, users can initiate instant AI training, evaluation, and deployment. It supports both code and no-code workflows, enabling seamless integration with any framework, IDE, or operating system, and is compatible with any GPU brand, quantity, or generation. SwarmOne's self-setting architecture autonomously manages resource allocation, workload orchestration, and infrastructure swarming, eliminating the need for Docker, MLOps, or DevOps. Its cognitive infrastructure layer and burst-to-cloud engine ensure optimal performance, whether on-premises or in the cloud. By automating tasks that typically hinder AI model development, SwarmOne allows data scientists to focus exclusively on scientific work, maximizing GPU utilization.

VMware Private AI Foundation is a joint, on‑premises generative AI platform built on VMware Cloud Foundation (VCF) that enables enterprises to run retrieval‑augmented generation workflows, fine‑tune and customize large language models, and perform inference in their own data centers, addressing privacy, choice, cost, performance, and compliance requirements. It integrates the Private AI Package (including vector databases, deep learning VMs, data indexing and retrieval services, and AI agent‑builder tools) with NVIDIA AI Enterprise (comprising NVIDIA microservices like NIM, NVIDIA’s own LLMs, and third‑party/open source models from places like Hugging Face). It supports full GPU virtualization, monitoring, live migration, and efficient resource pooling on NVIDIA‑certified HGX servers with NVLink/NVSwitch acceleration. Deployable via GUI, CLI, and API, it offers unified management through self‑service provisioning, model store governance, and more.

NVIDIA Confidential Computing secures data in use, protecting AI models and workloads as they execute, by leveraging hardware-based trusted execution environments built into NVIDIA Hopper and Blackwell architectures and supported platforms. It enables enterprises to deploy AI training and inference, whether on-premises, in the cloud, or at the edge, with no changes to model code, while ensuring the confidentiality and integrity of both data and models. Key features include zero-trust isolation of workloads from the host OS or hypervisor, device attestation to verify that only legitimate NVIDIA hardware is running the code, and full compatibility with shared or remote infrastructure for ISVs, enterprises, and multi-tenant environments. By safeguarding proprietary AI models, inputs, weights, and inference activities, NVIDIA Confidential Computing enables high-performance AI without compromising security or performance.