List of the Top 5 Infrastructure-as-a-Service (IaaS) Providers for Vertex AI in 2025

Google Cloud Platform (GCP) provides Infrastructure-as-a-Service (IaaS) that includes adaptable computing, storage, and networking capabilities. Through services such as Google Compute Engine, users can create customizable virtual machine instances tailored to their workload requirements. GCP’s IaaS solutions enable organizations to provision infrastructure as needed and adjust it dynamically to meet changing demands. New users are welcomed with $300 in complimentary credits, allowing them to explore, test, and implement various workloads without any initial costs. These services are maintained on Google's extensive global infrastructure, guaranteeing optimal performance and minimal latency. Additionally, GCP's IaaS solutions prioritize security, featuring integrated encryption and identity management tools to safeguard applications and data.

Google Compute Engine stands out as a powerful Infrastructure-as-a-Service (IaaS) platform that delivers scalable computing power via virtual machines. It allows users to access resources as needed, ensuring they only incur costs for what they utilize, which facilitates easy scaling of infrastructure to accommodate fluctuating workloads. This service removes the necessity for physical servers, providing enhanced flexibility, security, and rapid resource deployment to align with business demands. Additionally, new users are granted $300 in free credits, allowing them to experience the diverse functionalities and scalability of Google Compute Engine's cloud offerings.

Today, companies have a wide array of choices for training their deep learning and machine learning models in a cost-effective manner. AI accelerators are designed to address multiple use cases, offering solutions that vary from budget-friendly inference to comprehensive training options. Initiating the process is made easy with a multitude of services aimed at supporting both development and deployment stages. Custom ASICs known as Tensor Processing Units (TPUs) are crafted specifically to optimize the training and execution of deep neural networks, leading to enhanced performance. With these advanced tools, businesses can create and deploy more sophisticated and accurate models while keeping expenditures low, resulting in quicker processing times and improved scalability. A broad assortment of NVIDIA GPUs is also available, enabling economical inference or boosting training capabilities, whether by scaling vertically or horizontally. Moreover, employing RAPIDS and Spark in conjunction with GPUs allows users to perform deep learning tasks with exceptional efficiency. Google Cloud provides the ability to run GPU workloads, complemented by high-quality storage, networking, and data analytics technologies that elevate overall performance. Additionally, users can take advantage of CPU platforms upon launching a VM instance on Compute Engine, featuring a range of Intel and AMD processors tailored for various computational demands. This holistic strategy not only empowers organizations to tap into the full potential of artificial intelligence but also ensures effective cost management, making it easier for them to stay competitive in the rapidly evolving tech landscape. As a result, companies can confidently navigate their AI journeys while maximizing resources and innovation.

Pipeshift is a versatile orchestration platform designed to simplify the development, deployment, and scaling of open-source AI components such as embeddings, vector databases, and various models across language, vision, and audio domains, whether in cloud-based infrastructures or on-premises setups. It offers extensive orchestration functionalities that guarantee seamless integration and management of AI workloads while being entirely cloud-agnostic, thus granting users significant flexibility in their deployment options. Tailored for enterprise-level security requirements, Pipeshift specifically addresses the needs of DevOps and MLOps teams aiming to create robust internal production pipelines rather than depending on experimental API services that may compromise privacy. Key features include an enterprise MLOps dashboard that allows for the supervision of diverse AI workloads, covering tasks like fine-tuning, distillation, and deployment; multi-cloud orchestration with capabilities for automatic scaling, load balancing, and scheduling of AI models; and proficient administration of Kubernetes clusters. Additionally, Pipeshift promotes team collaboration by equipping users with tools to monitor and tweak AI models in real-time, ensuring that adjustments can be made swiftly to adapt to changing requirements. This level of adaptability not only enhances operational efficiency but also fosters a more innovative environment for AI development.

Google Distributed Cloud provides a robust array of managed hardware and software solutions that enhance the capacity of Google Cloud in both edge environments and on-premises data centers. This offering, driven by Anthos, is ideal for local data processing, edge computing, and the modernization of existing infrastructure, simultaneously addressing critical issues related to data sovereignty, security, and privacy. By harnessing the power of Google’s cutting-edge AI, data analytics, and database technologies, users can uncover meaningful insights and break through conventional barriers tied to scale, performance, and expenses in data handling, irrespective of its physical location. Users can maintain authority and autonomy over their data and infrastructure, ensuring they meet stringent compliance requirements while utilizing cloud-native services customized for their unique situations. This adaptability empowers organizations to innovate swiftly while upholding the highest standards of data governance and security. Ultimately, this comprehensive approach not only meets the immediate needs of businesses but also positions them for future growth and technological advancements.