Find and compare the best Artificial Intelligence software for Google Kubernetes Engine (GKE) in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Google Kubernetes Engine (GKE) on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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New Relic
New Relic
Free 2,600 RatingsAround 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability. -
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Google Cloud Speech-to-Text
Google
Free ($300 in free credits) 378 RatingsGoogle Cloud Speech-to-Text utilizes advanced artificial intelligence technology to transform spoken words into written format. By employing deep learning techniques, it achieves impressive accuracy in detecting and transcribing speech, even amidst background noise. The underlying AI consistently evolves, accommodating a wide range of accents, dialects, and specialized vocabularies. This flexibility positions it as an essential resource for international companies that need precise transcriptions across diverse languages and regions. New users can benefit from a $300 credit, making this AI-driven solution ideal for organizations aiming to seamlessly implement advanced speech-to-text capabilities into their operations, delivering both exceptional performance and user-friendliness. -
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MongoDB Atlas stands out as the leading cloud database service available, offering unparalleled data distribution and seamless mobility across all major platforms, including AWS, Azure, and Google Cloud. Its built-in automation tools enhance resource management and workload optimization, making it the go-to choice for modern application deployment. As a fully managed service, it ensures best-in-class automation and adheres to established practices that support high availability, scalability, and compliance with stringent data security and privacy regulations. Furthermore, MongoDB Atlas provides robust security controls tailored for your data needs, allowing for the integration of enterprise-grade features that align with existing security protocols and compliance measures. With preconfigured elements for authentication, authorization, and encryption, you can rest assured that your data remains secure and protected at all times. Ultimately, MongoDB Atlas not only simplifies deployment and scaling in the cloud but also fortifies your data with comprehensive security features that adapt to evolving requirements.
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Datadog is the cloud-age monitoring, security, and analytics platform for developers, IT operation teams, security engineers, and business users. Our SaaS platform integrates monitoring of infrastructure, application performance monitoring, and log management to provide unified and real-time monitoring of all our customers' technology stacks. Datadog is used by companies of all sizes and in many industries to enable digital transformation, cloud migration, collaboration among development, operations and security teams, accelerate time-to-market for applications, reduce the time it takes to solve problems, secure applications and infrastructure and understand user behavior to track key business metrics.
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Ray
Anyscale
FreeYou can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution. -
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StormForge
StormForge
FreeStormForge drives immediate benefits for organization through its continuous Kubernetes workload rightsizing capabilities — leading to cost savings of 40-60% along with performance and reliability improvements across the entire estate. As a vertical rightsizing solution, Optimize Live is autonomous, tunable, and works seamlessly with the HPA at enterprise scale. Optimize Live addresses both over- and under-provisioned workloads by analyzing usage data with advanced ML algorithms to recommend optimal resource requests and limits. Recommendations can be deployed automatically on a flexible schedule, accounting for changes in traffic patterns or application resource requirements, ensuring that workloads are always right-sized, and freeing developers from the toil and cognitive load of infrastructure sizing. -
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Elastic Observability
Elastic
$16 per monthLeverage the most extensively utilized observability platform, founded on the reliable Elastic Stack (commonly referred to as the ELK Stack), to integrate disparate data sources, providing cohesive visibility and actionable insights. To truly monitor and extract insights from your distributed systems, it is essential to consolidate all your observability data within a single framework. Eliminate data silos by merging application, infrastructure, and user information into a holistic solution that facilitates comprehensive observability and alerting. By integrating limitless telemetry data collection with search-driven problem-solving capabilities, you can achieve superior operational and business outcomes. Unify your data silos by assimilating all telemetry data, including metrics, logs, and traces, from any source into a platform that is open, extensible, and scalable. Enhance the speed of problem resolution through automatic anomaly detection that leverages machine learning and sophisticated data analytics, ensuring you stay ahead in today's fast-paced environment. This integrated approach not only streamlines processes but also empowers teams to make informed decisions swiftly. -
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NVIDIA Triton Inference Server
NVIDIA
FreeThe NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process. -
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Sedai
Sedai
$10 per monthSedai intelligently finds resources, analyzes traffic patterns and learns metric performance. This allows you to manage your production environments continuously without any manual thresholds or human intervention. Sedai's Discovery engine uses an agentless approach to automatically identify everything in your production environments. It intelligently prioritizes your monitoring information. All your cloud accounts are on the same platform. All of your cloud resources can be viewed in one place. Connect your APM tools. Sedai will identify and select the most important metrics. Machine learning intelligently sets thresholds. Sedai is able to see all the changes in your environment. You can view updates and changes and control how the platform manages resources. Sedai's Decision engine makes use of ML to analyze and comprehend data at large scale to simplify the chaos. -
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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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Google Cloud GPUs
Google
$0.160 per GPUAccelerate computational tasks such as those found in machine learning and high-performance computing (HPC) with a diverse array of GPUs suited for various performance levels and budget constraints. With adaptable pricing and customizable machines, you can fine-tune your setup to enhance your workload efficiency. Google Cloud offers high-performance GPUs ideal for machine learning, scientific analyses, and 3D rendering. The selection includes NVIDIA K80, P100, P4, T4, V100, and A100 GPUs, providing a spectrum of computing options tailored to meet different cost and performance requirements. You can effectively balance processor power, memory capacity, high-speed storage, and up to eight GPUs per instance to suit your specific workload needs. Enjoy the advantage of per-second billing, ensuring you only pay for the resources consumed during usage. Leverage GPU capabilities on Google Cloud Platform, where you benefit from cutting-edge storage, networking, and data analytics solutions. Compute Engine allows you to easily integrate GPUs into your virtual machine instances, offering an efficient way to enhance processing power. Explore the potential uses of GPUs and discover the various types of GPU hardware available to elevate your computational projects. -
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Accelerate the development of your deep learning project on Google Cloud: Utilize Deep Learning Containers to swiftly create prototypes within a reliable and uniform environment for your AI applications, encompassing development, testing, and deployment phases. These Docker images are pre-optimized for performance, thoroughly tested for compatibility, and designed for immediate deployment using popular frameworks. By employing Deep Learning Containers, you ensure a cohesive environment throughout the various services offered by Google Cloud, facilitating effortless scaling in the cloud or transitioning from on-premises setups. You also enjoy the versatility of deploying your applications on platforms such as Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm, giving you multiple options to best suit your project's needs. This flexibility not only enhances efficiency but also enables you to adapt quickly to changing project requirements.
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Harness
Harness
Harness is a comprehensive AI-native software delivery platform designed to modernize DevOps practices by automating continuous integration, continuous delivery, and GitOps workflows across multi-cloud and multi-service environments. It empowers engineering teams to build faster, deploy confidently, and manage infrastructure as code with automated error reduction and cost control. The platform integrates new capabilities like database DevOps, artifact registries, and on-demand cloud development environments to simplify complex operations. Harness also enhances software quality through AI-driven test automation, chaos engineering, and predictive incident response that minimize downtime. Feature management and experimentation tools allow controlled releases and data-driven decision-making. Security and compliance are strengthened with automated vulnerability scanning, runtime protection, and supply chain security. Harness offers deep insights into engineering productivity and cloud spend, helping teams optimize resources. With over 100 integrations and trusted by top companies, Harness unifies AI and DevOps to accelerate innovation and developer productivity. -
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ModelOp
ModelOp
ModelOp stands at the forefront of AI governance solutions, empowering businesses to protect their AI projects, including generative AI and Large Language Models (LLMs), while promoting innovation. As corporate leaders push for swift integration of generative AI, they encounter various challenges such as financial implications, regulatory compliance, security concerns, privacy issues, ethical dilemmas, and potential brand damage. With governments at global, federal, state, and local levels rapidly establishing AI regulations and oversight, organizations must act promptly to align with these emerging guidelines aimed at mitigating AI-related risks. Engaging with AI Governance specialists can keep you updated on market dynamics, regulatory changes, news, research, and valuable perspectives that facilitate a careful navigation of the benefits and hazards of enterprise AI. ModelOp Center not only ensures organizational safety but also instills confidence among all stakeholders involved. By enhancing the processes of reporting, monitoring, and compliance across the enterprise, businesses can foster a culture of responsible AI usage. In a landscape that evolves quickly, staying informed and compliant is essential for sustainable success. -
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Nutanix Enterprise AI
Nutanix
Nutanix Enterprise AI makes it simple to deploy, operate, and develop enterprise AI applications through secure AI endpoints that utilize large language models and generative AI APIs. By streamlining the process of integrating GenAI, Nutanix enables organizations to unlock extraordinary productivity boosts, enhance revenue streams, and realize the full potential of generative AI. With user-friendly workflows, you can effectively monitor and manage AI endpoints, allowing you to tap into your organization's AI capabilities. The platform's point-and-click interface facilitates the effortless deployment of AI models and secure APIs, giving you the flexibility to select from Hugging Face, NVIDIA NIM, or your customized private models. You have the option to run enterprise AI securely, whether on-premises or in public cloud environments, all while utilizing your existing AI tools. The system also allows for straightforward management of access to your language models through role-based access controls and secure API tokens designed for developers and GenAI application owners. Additionally, with just a single click, you can generate URL-ready JSON code, making API testing quick and efficient. This comprehensive approach ensures that enterprises can fully leverage their AI investments and adapt to evolving technological landscapes seamlessly.
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