Alternatives to Zipher

Compute Engine (IaaS), a platform from Google that allows organizations to create and manage cloud-based virtual machines, is an infrastructure as a services (IaaS). Computing infrastructure in predefined sizes or custom machine shapes to accelerate cloud transformation. General purpose machines (E2, N1,N2,N2D) offer a good compromise between price and performance. Compute optimized machines (C2) offer high-end performance vCPUs for compute-intensive workloads. Memory optimized (M2) systems offer the highest amount of memory and are ideal for in-memory database applications. Accelerator optimized machines (A2) are based on A100 GPUs, and are designed for high-demanding applications. Integrate Compute services with other Google Cloud Services, such as AI/ML or data analytics. Reservations can help you ensure that your applications will have the capacity needed as they scale. You can save money by running Compute using the sustained-use discount, and you can even save more when you use the committed-use discount.

RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.

AWS Auto Scaling continuously observes your applications and automatically modifies capacity to ensure consistent and reliable performance while minimizing costs. This service simplifies the process of configuring application scaling for various resources across multiple services in just a few minutes. It features an intuitive and robust user interface that enables the creation of scaling plans for a range of resources, including Amazon EC2 instances, Spot Fleets, Amazon ECS tasks, Amazon DynamoDB tables and indexes, as well as Amazon Aurora Replicas. By providing actionable recommendations, AWS Auto Scaling helps you enhance performance, reduce expenses, or strike a balance between the two. If you are utilizing Amazon EC2 Auto Scaling for dynamic scaling of your EC2 instances, you can now seamlessly integrate it with AWS Auto Scaling to extend your scaling capabilities to additional AWS services. This ensures that your applications are consistently equipped with the appropriate resources precisely when they are needed, leading to improved overall efficiency. Ultimately, AWS Auto Scaling empowers businesses to optimize their resource management in a highly efficient manner.

FinOpsly is an AI-native control plane for managing Cloud, Data, and AI spend at enterprise scale. Built for organizations operating across multiple clouds and data platforms, FinOpsly shifts FinOps from passive reporting to active, governed execution. The platform connects cost, usage, and business context into a unified operating model—allowing teams to anticipate spend, enforce guardrails, and take automated action with confidence. FinOpsly brings together infrastructure (AWS, Azure, GCP), data platforms (Snowflake, Databricks, BigQuery), and AI workloads into a single decision and execution layer. With explainable AI agents operating under policy-based controls, teams can safely automate optimization, trace cost drivers to real workloads, and stop budget drift before it becomes a problem. Key capabilities include: Business-aware cost attribution across products, teams, and services Predictive insight into cost drivers with clear, explainable reasoning Policy-controlled automation to optimize spend without disrupting performance Early detection and prevention of overruns, inefficiencies, and financial drift FinOpsly enables engineering, finance, and platform teams to operate from the same source of truth—turning cloud and data spend into a controllable, measurable part of the business.

Harness the power of your data and create innovative artificial intelligence (AI) solutions using Azure Databricks, where you can establish your Apache Spark™ environment in just minutes, enable autoscaling, and engage in collaborative projects within a dynamic workspace. This platform accommodates multiple programming languages such as Python, Scala, R, Java, and SQL, along with popular data science frameworks and libraries like TensorFlow, PyTorch, and scikit-learn. With Azure Databricks, you can access the most current versions of Apache Spark and effortlessly connect with various open-source libraries. You can quickly launch clusters and develop applications in a fully managed Apache Spark setting, benefiting from Azure's expansive scale and availability. The clusters are automatically established, optimized, and adjusted to guarantee reliability and performance, eliminating the need for constant oversight. Additionally, leveraging autoscaling and auto-termination features can significantly enhance your total cost of ownership (TCO), making it an efficient choice for data analysis and AI development. This powerful combination of tools and resources empowers teams to innovate and accelerate their projects like never before.

Pepperdata autonomous, application-level cost optimization delivers 30-47% greater cost savings for data-intensive workloads such as Apache Spark on Amazon EMR and Amazon EKS with no application changes. Using patented algorithms, Pepperdata Capacity Optimizer autonomously optimizes CPU and memory in real time with no application code changes. Pepperdata automatically analyzes resource usage in real time, identifying where more work can be done, enabling the scheduler to add tasks to nodes with available resources and spin up new nodes only when existing nodes are fully utilized. The result: CPU and memory are autonomously and continuously optimized, without delay and without the need for recommendations to be applied, and the need for ongoing manual tuning is safely eliminated. Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing Spark utilization, and freeing developers from manual tuning to focus on innovation.

Lucidity serves as a versatile multi-cloud storage management solution, adept at dynamically adjusting block storage across major platforms like AWS, Azure, and Google Cloud while ensuring zero downtime, which can lead to savings of up to 70% on storage expenses. This innovative platform automates the process of resizing storage volumes in response to real-time data demands, maintaining optimal disk usage levels between 75-80%. Additionally, Lucidity is designed to function independently of specific applications, integrating effortlessly into existing systems without necessitating code alterations or manual provisioning. The AutoScaler feature of Lucidity, accessible via the AWS Marketplace, provides businesses with an automated method to manage live EBS volumes, allowing for expansion or reduction based on workload requirements, all without any interruptions. By enhancing operational efficiency, Lucidity empowers IT and DevOps teams to recover countless hours of work, which can then be redirected towards more impactful projects that foster innovation and improve overall effectiveness. This capability ultimately positions enterprises to better adapt to changing storage needs and optimize resource utilization.

StormForge 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.

Sync Computing's Gradient is an advanced AI-driven optimization engine designed to streamline and enhance cloud-based data infrastructure. Utilizing cutting-edge machine learning technology developed at MIT, Gradient enables organizations to optimize the performance of their cloud workloads on CPUs and GPUs while significantly reducing costs. The platform offers up to 50% savings on Databricks compute expenses, ensuring workloads consistently meet runtime service level agreements (SLAs). With continuous monitoring and dynamic adjustments, Gradient adapts to changing data sizes and workload patterns, delivering peak efficiency across complex pipelines. Seamlessly integrating with existing tools and supporting various cloud providers, Sync Computing provides a robust solution for optimizing modern data infrastructure.

Mogenius offers an all-in-one platform that integrates visibility, observability, and automation for effective Kubernetes management. By connecting and visualizing your Kubernetes clusters and workloads, it ensures that the entire team has access to crucial insights. You can easily pinpoint misconfigurations within your workloads and take corrective measures directly through the mogenius interface. The platform streamlines Kubernetes operations with features like service catalogs, enabling developer self-service, and creating ephemeral environments. This self-service capability simplifies the deployment process for developers, allowing them to work more efficiently. Additionally, mogenius helps optimize resource allocation and prevents configuration drift through the use of standardized, automated workflows. By eliminating redundant tasks and promoting the reuse of resources with service catalogs, your team can enhance productivity. Gain complete visibility into your Kubernetes infrastructure and deploy a cloud-agnostic Kubernetes operator for a holistic view of your clusters and workloads. Furthermore, developers can quickly establish local and ephemeral testing environments that replicate the production setup in just a few clicks, ensuring a seamless development experience. Ultimately, mogenius empowers teams to manage their Kubernetes environments with greater ease and efficiency.

NVIDIA DGX Cloud Serverless Inference provides a cutting-edge, serverless AI inference framework designed to expedite AI advancements through automatic scaling, efficient GPU resource management, multi-cloud adaptability, and effortless scalability. This solution enables users to reduce instances to zero during idle times, thereby optimizing resource use and lowering expenses. Importantly, there are no additional charges incurred for cold-boot startup durations, as the system is engineered to keep these times to a minimum. The service is driven by NVIDIA Cloud Functions (NVCF), which includes extensive observability capabilities, allowing users to integrate their choice of monitoring tools, such as Splunk, for detailed visibility into their AI operations. Furthermore, NVCF supports versatile deployment methods for NIM microservices, granting the ability to utilize custom containers, models, and Helm charts, thus catering to diverse deployment preferences and enhancing user flexibility. This combination of features positions NVIDIA DGX Cloud Serverless Inference as a powerful tool for organizations seeking to optimize their AI inference processes.

CAST AI significantly reduces your compute costs with automated cost management and optimization. Within minutes, you can quickly optimize your GKE clusters thanks to real-time autoscaling up and down, rightsizing, spot instance automation, selection of most cost-efficient instances, and more. What you see is what you get – you can find out what your savings will look like with the Savings Report available in the free plan with K8s cost monitoring. Enabling the automation will deliver reported savings to you within minutes and keep the cluster optimized. The platform understands what your application needs at any given time and uses that to implement real-time changes for best cost and performance. It isn’t just a recommendation engine. CAST AI uses automation to reduce the operational costs of cloud services and enables you to focus on building great products instead of worrying about the cloud infrastructure. Companies that use CAST AI benefit from higher profit margins without any additional work thanks to the efficient use of engineering resources and greater control of cloud environments. As a direct result of optimization, CAST AI clients save an average of 63% on their Kubernetes cloud bills.

All public and private environments can be managed from one platform with a single CI/CD plug-in that connects to ALL automation toolchains. This plugin supports Jenkins, Kubernetes and Terraform as well as Cloud Formation, Azure ARm, Cloud Formation, Cloud Formation, and many other automation toolchains. No installation, no downloading... and free on us for the first thirty days. Integration with infrastructure orchestration domains such as AWS Cloud formation and Azure ARM, Ansible, Terraform, and Terraform. Service Composition Domain-Specific Language - This simplifies the relationship between services and handles cascading workflows. Shared resources, distributed life-cycle management, and more. Orchestration of cloud native Kubernetes service across multiple clusters using OpenShift and KubeSpray. A blueprint is available to automate the configuration and setup of clusters. Integration with Jenkins and other CI/CD platforms. This integration provides a 'one stop-shop' for all orchestration domains that can be integrated to your CI/CD pipeline.

Algorithms, advanced technologies, and continuous execution automatically combine Savings Plans with Reserved Instances to produce superior financial outcomes. Our customers see an average 68% increase in monthly savings. ProsperOps uses optimization and AI algorithms for tasks previously performed by humans. You enjoy the savings, we do the work. We combine savings instruments to provide the best savings and reduce your AWS financial lock-in, from years to days. We generate more savings than our cost so we add incremental dollars to your cloud budget net of our charge. ProsperOps can programmatically optimize your AWS compute savings plans and RIs. Algorithms combine multiple discount instruments to maximize savings, and minimize the commitment term.

Auto Scaling is a service designed to dynamically adjust computing resources in response to fluctuations in user demand. When there is an uptick in requests, it seamlessly adds ECS instances to accommodate the increased load, while conversely, it reduces the number of instances during quieter times to optimize resource allocation. This service not only adjusts resources automatically based on predefined scaling policies but also allows for manual intervention through scale-in and scale-out options, giving you the flexibility to manage resources as needed. During high-demand periods, it efficiently expands the available computing resources, ensuring optimal performance, and when demand wanes, Auto Scaling efficiently retracts ECS resources, helping to minimize operational costs. Additionally, this adaptability ensures that your system remains responsive and cost-effective throughout varying usage patterns.

The Xosphere Instance Orchestrator enhances cost efficiency through automated spot optimization by utilizing AWS Spot instances, ensuring that the infrastructure remains as reliable as on-demand instances. By diversifying Spot instances across different families, sizes, and availability zones, it minimizes potential disruptions caused by the reclamation of these instances. Instances that are backed by reservations will not be substituted with Spot instances, preserving their intended use. Additionally, the system is designed to automatically respond to Spot termination notifications, allowing for expedited replacement of on-demand instances. Furthermore, EBS volumes can be configured to attach seamlessly to newly provisioned replacement instances, facilitating uninterrupted operation of stateful applications. This orchestration ensures a robust infrastructure while optimizing costs effectively.

Pipeshift is an adaptable orchestration platform developed to streamline the creation, deployment, and scaling of open-source AI components like embeddings, vector databases, and various models for language, vision, and audio, whether in cloud environments or on-premises settings. It provides comprehensive orchestration capabilities, ensuring smooth integration and oversight of AI workloads while being fully cloud-agnostic, thus allowing users greater freedom in their deployment choices. Designed with enterprise-level security features, Pipeshift caters specifically to the demands of DevOps and MLOps teams who seek to implement robust production pipelines internally, as opposed to relying on experimental API services that might not prioritize privacy. Among its notable functionalities are an enterprise MLOps dashboard for overseeing multiple AI workloads, including fine-tuning, distillation, and deployment processes; multi-cloud orchestration equipped with automatic scaling, load balancing, and scheduling mechanisms for AI models; and effective management of Kubernetes clusters. Furthermore, Pipeshift enhances collaboration among teams by providing tools that facilitate the monitoring and adjustment of AI models in real-time.

Anyscale is a configurable AI platform that unifies tools and infrastructure to accelerate the development, deployment, and scaling of AI and Python applications using Ray. At its core is RayTurbo, an enhanced version of the open-source Ray framework, optimized for faster, more reliable, and cost-effective AI workloads, including large language model inference. The platform integrates smoothly with popular developer environments like VSCode and Jupyter notebooks, allowing seamless code editing, job monitoring, and dependency management. Users can choose from flexible deployment models, including hosted cloud services, on-premises machine pools, or existing Kubernetes clusters, maintaining full control over their infrastructure. Anyscale supports production-grade batch workloads and HTTP services with features such as job queues, automatic retries, Grafana observability dashboards, and high availability. It also emphasizes robust security with user access controls, private data environments, audit logs, and compliance certifications like SOC 2 Type II. Leading companies report faster time-to-market and significant cost savings with Anyscale’s optimized scaling and management capabilities. The platform offers expert support from the original Ray creators, making it a trusted choice for organizations building complex AI systems.

We are the sole provider in the industry capable of autonomously tuning applications at scale, whether for an individual app or throughout the entire service delivery framework. Opsani optimizes your application independently, ensuring that your cloud solution operates more efficiently and effectively without added effort on your part. Utilizing advanced AI and Machine Learning technologies, Opsani COaaS enhances cloud workload performance by perpetually reconfiguring and adjusting with every code update, load profile modification, and infrastructure enhancement. This process is seamless, allowing integration with a singular application or throughout your service delivery ecosystem while scaling autonomously across thousands of services. With Opsani, you can address all three of these challenges independently and without compromise. By employing Opsani's AI-driven algorithms, you can achieve cost reductions of up to 71%. The optimization process carried out by Opsani involves continually assessing trillions of configuration combinations to identify the most effective resource allocations and parameter settings for your needs. As a result, users can expect not just efficiency, but also a significant boost in overall application performance.

UbiOps serves as a robust AI infrastructure platform designed to enable teams to efficiently execute their AI and ML workloads as dependable and secure microservices, all while maintaining their current workflows. In just a few minutes, you can integrate UbiOps effortlessly into your data science environment, thereby eliminating the tedious task of establishing and overseeing costly cloud infrastructure. Whether you're a start-up aiming to develop an AI product or part of a larger organization's data science unit, UbiOps provides a solid foundation for any AI or ML service you wish to implement. The platform allows you to scale your AI workloads in response to usage patterns, ensuring you only pay for what you use without incurring costs for time spent idle. Additionally, it accelerates both model training and inference by offering immediate access to powerful GPUs, complemented by serverless, multi-cloud workload distribution that enhances operational efficiency. By choosing UbiOps, teams can focus on innovation rather than infrastructure management, paving the way for groundbreaking AI solutions.

Utilize widely-used open-source frameworks like Apache Hadoop, Spark, Hive, and Kafka with Azure HDInsight, a customizable and enterprise-level service designed for open-source analytics. Effortlessly manage vast data sets while leveraging the extensive open-source project ecosystem alongside Azure’s global capabilities. Transitioning your big data workloads to the cloud is straightforward and efficient. You can swiftly deploy open-source projects and clusters without the hassle of hardware installation or infrastructure management. The big data clusters are designed to minimize expenses through features like autoscaling and pricing tiers that let you pay solely for your actual usage. With industry-leading security and compliance validated by over 30 certifications, your data is well protected. Additionally, Azure HDInsight ensures you remain current with the optimized components tailored for technologies such as Hadoop and Spark, providing an efficient and reliable solution for your analytics needs. This service not only streamlines processes but also enhances collaboration across teams.

MinIO offers a powerful object storage solution that is entirely software-defined, allowing users to establish cloud-native data infrastructures tailored for machine learning, analytics, and various application data demands. What sets MinIO apart is its design centered around performance and compatibility with the S3 API, all while being completely open-source. This platform is particularly well-suited for expansive private cloud settings that prioritize robust security measures, ensuring critical availability for a wide array of workloads. Recognized as the fastest object storage server globally, MinIO achieves impressive READ/WRITE speeds of 183 GB/s and 171 GB/s on standard hardware, enabling it to serve as the primary storage layer for numerous tasks, including those involving Spark, Presto, TensorFlow, and H2O.ai, in addition to acting as an alternative to Hadoop HDFS. By incorporating insights gained from web-scale operations, MinIO simplifies the scaling process for object storage, starting with an individual cluster that can easily be federated with additional MinIO clusters as needed. This flexibility in scaling allows organizations to adapt their storage solutions efficiently as their data needs evolve.

MontyCloud presents an autonomous CloudOps platform that aims to streamline the management and operations of cloud resources. This solution allows IT teams to efficiently provision, oversee, and operate AWS infrastructure without requiring advanced cloud expertise. Users benefit from extensive visibility into their cloud inventories, ensuring security, compliance, and cost management, which enables organizations to enhance their cloud environments effectively. Additionally, it offers dedicated cloud project workspaces and sandbox environments, allowing secure access to cloud services. The platform incorporates autonomous governance through subtle guardrails, enhancing cloud security, compliance, and cost efficiency while ensuring adherence to established best practices. Users can achieve comprehensive visibility and control across their cloud resources and services, facilitating insights into resource utilization, performance metrics, and financial expenditures to support informed decision-making. By simplifying the management and monitoring of cloud environments, organizations can maintain thorough oversight of access, security, and compliance, further ensuring their cloud operations are both effective and efficient in the long run.

Espresso AI is a sophisticated data-warehouse optimization platform designed to lower compute and query expenses for services like Snowflake and Databricks SQL by utilizing machine-learning agents that handle scaling, scheduling, and query rewriting in real-time. It consists of three essential agents: an autoscaling agent that anticipates workload surges and cuts down on idle compute, a scheduling agent that efficiently directs queries across clusters to enhance utilization and minimize idle time, and a query agent that employs large language models along with formal verification techniques to rewrite SQL, ensuring that results remain consistent while enhancing performance. The system touts rapid deployment capabilities, claiming that users can get started in minutes instead of months, and features a pricing structure linked to the actual savings it generates, meaning you don't incur costs if it fails to lower your bill. By automating a vast number of optimization decisions each day, Espresso AI not only promises significant cost savings but also allows engineering teams to concentrate on developing features that add value. This innovative approach allows businesses to harness their data warehouse capabilities without the usual overhead, thus transforming the way they manage and utilize their data resources.

Zerops.io serves as a cloud solution tailored for developers focused on creating contemporary applications, providing features such as automatic vertical and horizontal autoscaling, precise resource management, and freedom from vendor lock-in. The platform enhances infrastructure management through capabilities like automated backups, failover options, CI/CD integration, and comprehensive observability. Zerops.io adapts effortlessly to the evolving requirements of your project, guaranteeing maximum performance and cost-effectiveness throughout the development lifecycle, while also accommodating microservices and intricate architectures. It is particularly beneficial for developers seeking a combination of flexibility, scalability, and robust automation without the hassle of complex setups. This ensures a streamlined experience that empowers developers to focus on innovation rather than infrastructure.

Amazon SageMaker HyperPod is a specialized and robust computing infrastructure designed to streamline and speed up the creation of extensive AI and machine learning models by managing distributed training, fine-tuning, and inference across numerous clusters equipped with hundreds or thousands of accelerators, such as GPUs and AWS Trainium chips. By alleviating the burdens associated with developing and overseeing machine learning infrastructure, it provides persistent clusters capable of automatically identifying and rectifying hardware malfunctions, resuming workloads seamlessly, and optimizing checkpointing to minimize the risk of interruptions — thus facilitating uninterrupted training sessions that can last for months. Furthermore, HyperPod features centralized resource governance, allowing administrators to establish priorities, quotas, and task-preemption rules to ensure that computing resources are allocated effectively among various tasks and teams, which maximizes utilization and decreases idle time. It also includes support for “recipes” and pre-configured settings, enabling rapid fine-tuning or customization of foundational models, such as Llama. This innovative infrastructure not only enhances efficiency but also empowers data scientists to focus more on developing their models rather than managing the underlying technology.

NVIDIA Base Command Manager provides rapid deployment and comprehensive management for diverse AI and high-performance computing clusters, whether at the edge, within data centers, or across multi- and hybrid-cloud settings. This platform automates the setup and management of clusters, accommodating sizes from a few nodes to potentially hundreds of thousands, and is compatible with NVIDIA GPU-accelerated systems as well as other architectures. It facilitates orchestration through Kubernetes, enhancing the efficiency of workload management and resource distribution. With additional tools for monitoring infrastructure and managing workloads, Base Command Manager is tailored for environments that require accelerated computing, making it ideal for a variety of HPC and AI applications. Available alongside NVIDIA DGX systems and within the NVIDIA AI Enterprise software suite, this solution enables the swift construction and administration of high-performance Linux clusters, thereby supporting a range of applications including machine learning and analytics. Through its robust features, Base Command Manager stands out as a key asset for organizations aiming to optimize their computational resources effectively.

Together AI offers a cloud platform purpose-built for developers creating AI-native applications, providing optimized GPU infrastructure for training, fine-tuning, and inference at unprecedented scale. Its environment is engineered to remain stable even as customers push workloads to trillions of tokens, ensuring seamless reliability in production. By continuously improving inference runtime performance and GPU utilization, Together AI delivers a cost-effective foundation for companies building frontier-level AI systems. The platform features a rich model library including open-source, specialized, and multimodal models for chat, image generation, video creation, and coding tasks. Developers can replace closed APIs effortlessly through OpenAI-compatible endpoints. Innovations such as ATLAS, FlashAttention, Flash Decoding, and Mixture of Agents highlight Together AI’s strong research contributions. Instant GPU clusters allow teams to scale from prototypes to distributed workloads in minutes. AI-native companies rely on Together AI to break performance barriers and accelerate time to market.

Exafunction enhances the efficiency of your deep learning inference tasks, achieving up to a tenfold increase in resource utilization and cost savings. This allows you to concentrate on developing your deep learning application rather than juggling cluster management and performance tuning. In many deep learning scenarios, limitations in CPU, I/O, and network capacities can hinder the optimal use of GPU resources. With Exafunction, GPU code is efficiently migrated to high-utilization remote resources, including cost-effective spot instances, while the core logic operates on a low-cost CPU instance. Proven in demanding applications such as large-scale autonomous vehicle simulations, Exafunction handles intricate custom models, guarantees numerical consistency, and effectively manages thousands of GPUs working simultaneously. It is compatible with leading deep learning frameworks and inference runtimes, ensuring that models and dependencies, including custom operators, are meticulously versioned, so you can trust that you're always obtaining accurate results. This comprehensive approach not only enhances performance but also simplifies the deployment process, allowing developers to focus on innovation instead of infrastructure.

No expertise required! Our Kubernetes Management platform allows you to create clusters in minutes. Every feature of our platform has been designed to automate DevOps. We ensure that every component is tightly interconnected by building everything from scratch. This allows us to achieve the best performance and reduce complexity. Syself Autopilot supports declarative configurations. This is an approach where configuration files are used to define the desired states of your infrastructure and application. Instead of issuing commands that change the current state, the system will automatically make the necessary adjustments in order to achieve the desired state.

Establish a robust and scalable cloud infrastructure that enables you to exert control and maintain clear organizational structures within your cloud ecosystem. Achieve this by creating distinct organizations, outlining the relationships between users, tenants, and projects, while effectively managing quotas and services. Utilize cloud and virtualization platforms such as Red Hat Enterprise Virtualization, Amazon, and Microsoft Azure for system provisioning, set retirement dates for resources, and scale your environment according to your specific needs. By taking your current operational environment and running various what-if scenarios, you can proactively plan resources and gain ongoing insights into consumption patterns, facilitating detailed chargeback, quotas, and policy development. Enhance your understanding of performance, capacity, and workloads by utilizing SmartState's historical and trend analytics, which provide valuable insights into different facets of your cloud infrastructure. Establish the policy framework for your environment and implement automatic alerts and responses to adapt to changes in your cloud landscape, ensuring continuous optimization and agility. This comprehensive approach not only improves resource management but also fosters a proactive culture of monitoring and adaptability within your organization.

FinOps on nOps We only charge for what we save. Most organizations don’t have the resources to focus on reducing cloud spend. nOps is your ML-powered FinOps team. nOps reduces cloud waste, helps you run workloads on spot instances, automatically manages reservations, and helps optimize your containers. Everything is automated and data-driven.

The Lumen Cloud Application Manager is an innovative tool that streamlines the orchestration of infrastructure, applications, and services across various technologies through a single platform, enhancing your agility, flexibility, and control over application workloads. By unifying the workload management process across different hosting environments, our centralized platform enables you to gain clear insights into your operational status. This leads to quicker response times and more informed decision-making. You can manage the modeling, deployment, and orchestration of the entire application lifecycle seamlessly. Utilize our interactive visualization tools to scale, migrate, and update your applications with greater efficiency. Additionally, our solution simplifies the management of your hybrid IT environment, resulting in accelerated application delivery, reduced costs, and complete visibility of your progress, empowering you to make strategic choices for the future—all backed by the support of a dedicated technical account manager ensuring your success. This comprehensive approach fosters a proactive standpoint in managing your IT resources effectively.

Design, oversee, operate, and enhance high-performance computing (HPC) and large-scale compute clusters seamlessly. Implement comprehensive clusters and additional resources, encompassing task schedulers, computational virtual machines, storage solutions, networking capabilities, and caching systems. Tailor and refine clusters with sophisticated policy and governance tools, which include cost management, integration with Active Directory, as well as monitoring and reporting functionalities. Utilize your existing job scheduler and applications without any necessary changes. Empower administrators with complete authority over job execution permissions for users, in addition to determining the locations and associated costs for running jobs. Benefit from integrated autoscaling and proven reference architectures suitable for diverse HPC workloads across various sectors. CycleCloud accommodates any job scheduler or software environment, whether it's proprietary, in-house solutions or open-source, third-party, and commercial software. As your requirements for resources shift and grow, your cluster must adapt accordingly. With scheduler-aware autoscaling, you can ensure that your resources align perfectly with your workload needs while remaining flexible to future changes. This adaptability is crucial for maintaining efficiency and performance in a rapidly evolving technological landscape.

DataNimbus, an AI-powered platform, streamlines payments and accelerates AI implementation through innovative solutions. DataNimbus improves scalability and governance by seamlessly integrating Databricks components such as Spark, Unity Catalog and ML Ops. Its offerings include a designer, a marketplace of reusable connectors and blocks for machine learning, and agile APIs. All are designed to simplify workflows while driving data-driven innovation.

Streamline those repetitive, time-consuming, and error-prone tasks related to cloud management through automation. The Azure Automation service enables you to concentrate on activities that contribute real value to your business. It minimizes errors and enhances efficiency, ultimately leading to reduced operational expenses. You can seamlessly update Windows and Linux systems within hybrid environments while keeping track of update compliance across Azure, on-premises, and various other cloud platforms. Additionally, you can schedule deployments to ensure updates are installed within a designated maintenance window. Authoring and managing PowerShell configurations, importing configuration scripts, and generating node configurations can all be accomplished in the cloud. Furthermore, Azure Configuration Management allows for the monitoring and automatic updating of machine configurations across both physical and virtual systems, whether they operate on Windows or Linux, in the cloud or on-premises, ensuring seamless management across diverse environments. This comprehensive approach not only enhances operational agility but also drives innovation within your organization.

Originally created by Uber, Horovod aims to simplify and accelerate the process of distributed deep learning, significantly reducing model training durations from several days or weeks to mere hours or even minutes. By utilizing Horovod, users can effortlessly scale their existing training scripts to leverage the power of hundreds of GPUs with just a few lines of Python code. It offers flexibility for deployment, as it can be installed on local servers or seamlessly operated in various cloud environments such as AWS, Azure, and Databricks. In addition, Horovod is compatible with Apache Spark, allowing a cohesive integration of data processing and model training into one streamlined pipeline. Once set up, the infrastructure provided by Horovod supports model training across any framework, facilitating easy transitions between TensorFlow, PyTorch, MXNet, and potential future frameworks as the landscape of machine learning technologies continues to progress. This adaptability ensures that users can keep pace with the rapid advancements in the field without being locked into a single technology.

Stacktape is a user-friendly cloud framework that eliminates the need for DevOps, making it both robust and accessible. It enables users to develop, deploy, and manage applications seamlessly on AWS, requiring 98% less configuration and no prior DevOps or cloud expertise. Unlike other platforms, Stacktape allows for the deployment of both serverless applications based on AWS Lambda and traditional container-based applications. It boasts support for over 20 infrastructure components, including SQL databases, load balancers, MongoDB Atlas clusters, batch jobs, Kafka topics, Redis clusters, and more. In addition to managing infrastructure, Stacktape simplifies source code packaging, deployment processes, and facilitates both local and remote development. The framework is complemented by a Visual Studio Code extension and a graphical user interface for local development, enhancing user experience. As an Infrastructure as Code (IaaC) solution, Stacktape significantly reduces configuration complexity; for instance, a typical production-grade REST API requires only around 30 lines of configuration, contrasting sharply with the 600-800 lines needed for CloudFormation or Terraform. Furthermore, deploying applications can be accomplished with a single command, whether from a local machine or through a CI/CD pipeline, making the process as streamlined as possible. This ease of use allows developers to focus more on building features rather than managing infrastructure intricacies.

Oracle Cloud Infrastructure (OCI) Resource Manager is a service managed by Oracle that streamlines the deployment and management of various resources within the Oracle Cloud Infrastructure ecosystem. In contrast to other cloud providers' Infrastructure-as-Code (IaC) solutions, this service utilizes Terraform, an open-source standard recognized widely in the industry, enabling DevOps professionals to create and implement their infrastructure across diverse environments. By employing IaC principles, developers can achieve consistent and repeatable deployment of configurations, which significantly boosts overall productivity. Additionally, for compliance and auditing purposes, Resource Manager meticulously records user-driven changes to the infrastructure along with corresponding timestamps. Delve into a specific architecture and Terraform configuration designed for leveraging Oracle Autonomous Data Warehouse alongside Oracle Analytics Cloud to enhance data management efficiency and effectiveness in your projects. This integration not only streamlines data handling but also empowers businesses to derive actionable insights from their data assets.

Azure Arc serves as a strategic bridge between on-premises, edge, and multicloud infrastructures, empowering organizations to unify management under a single Azure framework. It supports hybrid operations by bringing Azure security, compliance, and governance services to workloads no matter where they run. Through Arc, enterprises can streamline VMware lifecycle management, deploy Kubernetes clusters, and extend Azure data services directly into their datacenters. It also enhances modernization by enabling containerized apps, SQL Server management, and Windows Server deployments with new cloud-driven capabilities. Developers can maintain familiar workflows with GitHub and Visual Studio Code while benefiting from consistent APIs and centralized observability via Azure Monitor. Security is reinforced by Microsoft’s 34,000 security engineers and over 100 compliance certifications, making Arc suitable for regulated industries. With pricing that includes core inventory at no cost and paid add-ons like advanced security and monitoring, it scales to fit enterprise needs. Customer success stories from companies like Chevron, LALIGA, and Coles demonstrate how Azure Arc accelerates innovation and simplifies operations in real-world scenarios.

Efficiently manage multicluster environments for Azure Kubernetes Service (AKS) that involve tasks such as workload distribution, north-south traffic load balancing for incoming requests to various clusters, and coordinated upgrades across different clusters. The fleet cluster offers a centralized management system for overseeing all your clusters on a large scale. A dedicated hub cluster manages the upgrades and the configuration of your Kubernetes clusters seamlessly. Through Kubernetes configuration propagation, you can apply policies and overrides to distribute resources across the fleet's member clusters effectively. The north-south load balancer regulates the movement of traffic among workloads situated in multiple member clusters within the fleet. You can group various Azure Kubernetes Service (AKS) clusters to streamline workflows involving Kubernetes configuration propagation and networking across multiple clusters. Furthermore, the fleet system necessitates a hub Kubernetes cluster to maintain configurations related to placement policies and multicluster networking, thereby enhancing operational efficiency and simplifying management tasks. This approach not only optimizes resource usage but also helps in maintaining consistency and reliability across all clusters involved.

IBM Analytics Engine offers a unique architecture for Hadoop clusters by separating the compute and storage components. Rather than relying on a fixed cluster with nodes that serve both purposes, this engine enables users to utilize an object storage layer, such as IBM Cloud Object Storage, and to dynamically create computing clusters as needed. This decoupling enhances the flexibility, scalability, and ease of maintenance of big data analytics platforms. Built on a stack that complies with ODPi and equipped with cutting-edge data science tools, it integrates seamlessly with the larger Apache Hadoop and Apache Spark ecosystems. Users can define clusters tailored to their specific application needs, selecting the suitable software package, version, and cluster size. They have the option to utilize the clusters for as long as necessary and terminate them immediately after job completion. Additionally, users can configure these clusters with third-party analytics libraries and packages, and leverage IBM Cloud services, including machine learning, to deploy their workloads effectively. This approach allows for a more responsive and efficient handling of data processing tasks.

Spot Ocean empowers users to harness the advantages of Kubernetes while alleviating concerns about infrastructure management, all while offering enhanced cluster visibility and significantly lower expenses. A crucial inquiry is how to effectively utilize containers without incurring the operational burdens tied to overseeing the underlying virtual machines, while simultaneously capitalizing on the financial benefits of Spot Instances and multi-cloud strategies. To address this challenge, Spot Ocean is designed to operate within a "Serverless" framework, effectively managing containers by providing an abstraction layer over virtual machines, which facilitates the deployment of Kubernetes clusters without the need for VM management. Moreover, Ocean leverages various compute purchasing strategies, including Reserved and Spot instance pricing, and seamlessly transitions to On-Demand instances as required, achieving an impressive 80% reduction in infrastructure expenditures. As a Serverless Compute Engine, Spot Ocean streamlines the processes of provisioning, auto-scaling, and managing worker nodes within Kubernetes clusters, allowing developers to focus on building applications rather than managing infrastructure. This innovative approach not only enhances operational efficiency but also enables organizations to optimize their cloud spending while maintaining robust performance and scalability.

Our AI can choose from hundreds of database tuning knobs. It will take care of all the details so you can get your life back. You can monitor your database performance and adjust your configuration to meet changing workloads. OtterTune is designed for cloud databases and doesn't access any user data. Instead, it fine-tunes your DBaaS operations. OtterTune is more than a frontend to a few tools. It's a revolutionary machine-learning and machine training platform that allows the database backend to run in the best possible way, both now and in the future. OtterTune actively tunes your database using machine learning. Select a target objective to optimize (e.g. throughput, latency or cost) and OtterTune will automatically update 100+ configuration settings to help you achieve that target. Don't be surprised if there are outages or performance drops. OtterTune monitors and collects metrics about your workload.

Cluster.dev serves as the ultimate management solution for cloud-native infrastructures, seamlessly integrating the diverse capabilities of your existing infrastructure tools. It empowers teams to develop platform-as-a-service environments tailored to their specific needs, while providing the ability to customize both projects and infrastructures. By consolidating all changes and the overall status of your infrastructure in one location—your Git repository—you can monitor everything effortlessly. This platform eliminates the need for tedious manual runbooks and complex CI/CD processes, ensuring that you maintain a clear overview of your infrastructure. With Cluster.dev, you can confidently implement infrastructure changes, knowing that your tools will function as anticipated. Every element within Cluster.dev, from scripts to Kubernetes manifests, is tracked and maintained with precision. The platform ensures security and independence from external vendors, allowing you to transmit secrets to third-party applications even when they do not support your secret storage solution. Additionally, the remarkable template engine of Cluster.dev offers unparalleled customization options for all aspects of your infrastructure, enabling you to tailor configurations, scripts, manifests, and Terraform code to meet your unique requirements. In an ever-evolving technological landscape, having such a versatile tool is invaluable for maintaining control and flexibility.