Find and compare the best Artificial Intelligence software for Google Cloud AI Infrastructure in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Google Cloud AI Infrastructure on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Vertex AI
Google
Free ($300 in free credits) 666 RatingsVertex AI empowers organizations to seamlessly incorporate advanced intelligent systems into their workflows, revolutionizing customer engagement, data analysis, and decision-making processes. This platform features an extensive suite of AI-driven tools and models designed to tackle intricate challenges, streamline operations, and deliver valuable insights. Catering to a wide range of sectors, including finance and healthcare, Vertex AI offers adaptable solutions that can be customized to meet unique business requirements. New users benefit from $300 in complimentary credits, allowing them to explore various AI applications and expedite their digital transformation journey. With Vertex AI, companies are equipped with the essential resources to leverage AI technology, paving the way for new avenues of growth and innovation. -
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Google Cloud Platform
Google
Free ($300 in free credits) 55,297 RatingsThe Google Cloud Platform (GCP) offers a comprehensive collection of Artificial Intelligence (AI) and machine learning resources aimed at simplifying data analysis processes. It features a range of pre-trained models and APIs, including Vision AI, Natural Language, and AutoML, enabling businesses to effortlessly integrate AI into their applications without needing extensive knowledge of the subject. New users are also granted $300 in complimentary credits to experiment with, test, and implement workloads, allowing them to investigate the platform's AI functionalities and develop sophisticated machine learning applications without any upfront investment. GCP’s AI offerings are designed to work harmoniously with other services, facilitating the creation of complete machine learning workflows from data management to model deployment. Moreover, these tools are built for scalability, empowering organizations to explore AI and expand their AI-driven solutions as their requirements evolve. With these capabilities, companies can swiftly adopt AI for a variety of applications, including predictive analysis and automation. -
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Rayven
Rayven
$0Rayven is a user-friendly SaaS platform that solves businesses' challenges with interoperability, real-time data, and app development - without the need to replace legacy systems, increase risk, or endure lengthy development times. Combining iPaaS, Data, IoT, Workflow Automation, Analytics, BI, AI, App Development, and PaaS into one solution, Rayven makes system integration, real-time data processing, and custom app creation simple. It's designed to help you overcome complex business issues effortlessly. With Rayven, you can: - Connect any system, device, or data source - Quickly create workflows and applications - Add AI and automation, tailored to your needs, anywhere Rayven is intuitive, fully compatible with any technology, and infinitely scalable. It optimises your existing tools, making real-time, AI-driven decision-making accessible to businesses of all sizes at an affordable price. -
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Google Cloud TPU
Google
$0.97 per chip-hourAdvancements in machine learning have led to significant breakthroughs in both business applications and research, impacting areas such as network security and medical diagnostics. To empower a broader audience to achieve similar innovations, we developed the Tensor Processing Unit (TPU). This custom-built machine learning ASIC is the backbone of Google services like Translate, Photos, Search, Assistant, and Gmail. By leveraging the TPU alongside machine learning, companies can enhance their success, particularly when scaling operations. The Cloud TPU is engineered to execute state-of-the-art machine learning models and AI services seamlessly within Google Cloud. With a custom high-speed network delivering over 100 petaflops of performance in a single pod, the computational capabilities available can revolutionize your business or lead to groundbreaking research discoveries. Training machine learning models resembles the process of compiling code: it requires frequent updates, and efficiency is key. As applications are developed, deployed, and improved, ML models must undergo continuous training to keep pace with evolving demands and functionalities. Ultimately, leveraging these advanced tools can position your organization at the forefront of innovation. -
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Voxel51
Voxel51
Voxel51 is the driving force behind FiftyOne, an open-source toolkit designed to enhance computer vision workflows by elevating dataset quality and providing valuable insights into model performance. With FiftyOne, you can explore, search through, and segment your datasets to quickly locate samples and labels that fit your specific needs. The toolkit offers seamless integration with popular public datasets such as COCO, Open Images, and ActivityNet, while also allowing you to create custom datasets from the ground up. Recognizing that data quality is a crucial factor affecting model performance, FiftyOne empowers users to pinpoint, visualize, and remedy the failure modes of their models. Manual identification of annotation errors can be labor-intensive and inefficient, but FiftyOne streamlines this process by automatically detecting and correcting label inaccuracies, enabling the curation of datasets with superior quality. In addition, traditional performance metrics and manual debugging methods are often insufficient for scaling, which is where the FiftyOne Brain comes into play, facilitating the identification of edge cases, the mining of new training samples, and offering a host of other advanced features to enhance your workflow. Overall, FiftyOne significantly optimizes the way you manage and improve your computer vision projects. -
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Steamship
Steamship
Accelerate your AI deployment with fully managed, cloud-based AI solutions that come with comprehensive support for GPT-4, eliminating the need for API tokens. Utilize our low-code framework to streamline your development process, as built-in integrations with all major AI models simplify your workflow. Instantly deploy an API and enjoy the ability to scale and share your applications without the burden of infrastructure management. Transform a smart prompt into a sharable published API while incorporating logic and routing capabilities using Python. Steamship seamlessly connects with your preferred models and services, allowing you to avoid the hassle of learning different APIs for each provider. The platform standardizes model output for consistency and makes it easy to consolidate tasks such as training, inference, vector search, and endpoint hosting. You can import, transcribe, or generate text while taking advantage of multiple models simultaneously, querying the results effortlessly with ShipQL. Each full-stack, cloud-hosted AI application you create not only provides an API but also includes a dedicated space for your private data, enhancing your project's efficiency and security. With an intuitive interface and powerful features, you can focus on innovation rather than technical complexities. -
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Pangiam Project DARTMOUTH
Pangiam
Project DARTMOUTH represents a partnership between Pangiam and Google Cloud, leveraging AI and pattern recognition technologies to process and analyze extensive data in real-time, thereby detecting potentially banned items in various forms of luggage, airline cargo, and shipments. By merging Pangiam's National Security-grade technology with the extensive AI offerings from Google Cloud, this initiative delivers an AI-driven solution designed with open architecture standards, which guarantees seamless compatibility with current hardware and devices in a straightforward "plug and play" format. This approach significantly accelerates the modernization of security operations through cutting-edge software solutions. The system is designed to operate on open platforms and software, allowing for integration with any security technology, regardless of its manufacturer, thus enhancing scalability across multiple checkpoints and beyond. Moreover, by implementing software and machine learning models on independent edge devices, it empowers frontline operations with robust capabilities that can adapt to various security environments. Ultimately, Project DARTMOUTH aims to revolutionize airport security processes while ensuring a high level of efficiency and effectiveness in identifying potential threats.
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