Business Software for Kubernetes

The Ark evidence management system streamlines the entire process of receiving, tracking, and securing evidence throughout its entire lifecycle. As a web-based platform, it eliminates the need for an application and provides user-friendly functionalities, including customizable templates that facilitate efficient metadata collection and seamless uploads of external files, making it well-suited for managing diverse types of evidence. Ark is compatible with leading Digital Evidence Management Suites (DEMS) and can work seamlessly with Davidhorn’s backend or other preferred systems. It can be deployed in both cloud and on-premises settings, supporting environments like Microsoft, Linux, or Kubernetes. Prioritizing security, Ark adheres to regulations such as MoPi, PACE, and GDPR, and incorporates features like digital fingerprinting and comprehensive audit trails. Additionally, Ark equips users with advanced tools designed to enhance efficiency, including speech-to-text AI that can generate draft transcripts in over 25 languages, while also enabling the option to livestream interviews, ultimately leading to reduced travel expenses. This innovative system not only improves evidence management but also helps organizations maintain high standards of accountability and transparency.

Enhance and launch AI models using Simplismart's ultra-fast inference engine. Seamlessly connect with major cloud platforms like AWS, Azure, GCP, and others for straightforward, scalable, and budget-friendly deployment options. Easily import open-source models from widely-used online repositories or utilize your personalized custom model. You can opt to utilize your own cloud resources or allow Simplismart to manage your model hosting. With Simplismart, you can go beyond just deploying AI models; you have the capability to train, deploy, and monitor any machine learning model, achieving improved inference speeds while minimizing costs. Import any dataset for quick fine-tuning of both open-source and custom models. Efficiently conduct multiple training experiments in parallel to enhance your workflow, and deploy any model on our endpoints or within your own VPC or on-premises to experience superior performance at reduced costs. The process of streamlined and user-friendly deployment is now achievable. You can also track GPU usage and monitor all your node clusters from a single dashboard, enabling you to identify any resource limitations or model inefficiencies promptly. This comprehensive approach to AI model management ensures that you can maximize your operational efficiency and effectiveness.

Enhanced high-definition APM visibility through real user monitoring, synthetic monitoring, and OpenTelemetry offers a solution that is scalable, user-friendly, and simplifies the integration of insights from end users, applications, networks, and the cloud-native space. The rise of microservices within containerized environments on dynamic cloud infrastructures has resulted in a highly transient and distributed landscape at an unprecedented scale. Traditional methods of enhancing APM, which rely on sampled transactions, partial traces, and aggregate metrics, have become ineffective, as legacy APM solutions struggle to identify the reasons behind slow or stalling critical business applications. The Riverbed platform provides cohesive visibility across the contemporary application landscape, ensuring ease of deployment and management, while facilitating quicker resolution of even the most challenging performance issues. Riverbed APM is thoroughly designed for the cloud-native environment, offering extensive monitoring and observability for transactions that operate on the latest cloud and application infrastructures, ultimately enhancing operational efficiency and user experience. This comprehensive approach not only addresses current performance challenges but also positions organizations to adapt to future technological advancements seamlessly.

A test execution is defined by a combination of a testing tool, a repository of tests, and specific arguments or parameters. You can utilize the out-of-the-box workflows provided by Testkube for various testing tools or create a personalized workflow tailored to your needs. It's possible to group related or unrelated test execution workflows into comprehensive suites for better organization. Additionally, you can establish a searchable repository filled with test executions that are ready to run. Proactive triggers allow you to initiate test executions in response to CI/CD events, on a manual basis, or according to a predefined schedule. Furthermore, you can set up your system to respond to Kubernetes infrastructure events, like deployments, to automatically launch test executions. With Kubernetes’s inherent scalability, you have the option to increase the number of concurrent test executions to meet demand. You can also take advantage of advanced scaling options to enhance load tests, allowing you to expand from a single execution to as many as 1,000. By parallelizing functional tests, you can significantly reduce the time required for completion. All logs and artifacts generated during the execution of test tools are stored in a centralized repository, making them easily accessible. Moreover, the system includes features for quickly identifying errors by highlighting keywords associated with failures in the logs. In addition, utilizing various filtering options can enhance your ability to sift through logs efficiently, ensuring a smoother debugging process.

Examine the causes of failures right after the testing phase concludes. Create straightforward and easy-to-read reports for your teams. Utilize machine learning-driven auto-analyzers to delve into the reasons behind the failures. Consolidate test outcomes from different platforms, frameworks, and programming languages while delivering actionable insights. Employing machine learning algorithms helps to uncover patterns in the test data, identify the underlying causes of failures, and forecast future testing outcomes. Support the manual examination of test logs and emerging failure patterns from the latest test runs. Enable automated decision-making processes for release pipelines by adhering to defined testing criteria and outcomes. Present test results in a clear format that facilitates monitoring of trends, recognition of patterns, generation of insights, and informed business choices. Regularly assess your product's health and automate release decisions with Quality Gates to enhance efficiency and reliability. This approach not only streamlines the testing process but also significantly contributes to improving overall product quality.

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.

Red Hat Advanced Cluster Management for Kubernetes allows users to oversee clusters and applications through a centralized interface, complete with integrated security policies. By enhancing the capabilities of Red Hat OpenShift, it facilitates the deployment of applications, the management of multiple clusters, and the implementation of policies across numerous clusters at scale. This solution guarantees compliance, tracks usage, and maintains uniformity across deployments. Included with Red Hat OpenShift Platform Plus, it provides an extensive array of powerful tools designed to secure, protect, and manage applications effectively. Users can operate from any environment where Red Hat OpenShift is available and can manage any Kubernetes cluster within their ecosystem. The self-service provisioning feature accelerates application development pipelines, enabling swift deployment of both legacy and cloud-native applications across various distributed clusters. Additionally, self-service cluster deployment empowers IT departments by automating the application delivery process, allowing them to focus on higher-level strategic initiatives. As a result, organizations can achieve greater efficiency and agility in their IT operations.

In summary, OKD represents a highly opinionated version of Kubernetes. At its core, Kubernetes consists of various software and architectural patterns designed to manage applications on a large scale. While we incorporate some features directly into Kubernetes through modifications, the majority of our enhancements come from "preinstalling" a wide array of software components known as Operators into the deployed cluster. These Operators manage the over 100 essential elements of our platform, including OS upgrades, web consoles, monitoring tools, and image-building functionalities. OKD is versatile and suitable for deployment across various environments, from cloud infrastructures to on-premise hardware and edge computing scenarios. The installation process is automated for certain platforms, like AWS, while also allowing for customization in other environments, such as bare metal or lab settings. OKD embraces best practices in development and technology, making it an excellent platform for technologists and students alike to explore, innovate, and engage with the broader cloud ecosystem. Furthermore, as an open-source project, it encourages community contributions and collaboration, fostering a rich environment for learning and growth.

Enhancing application performance becomes effortless when you streamline resources throughout your data centers and cloud environments using a single software solution. Discover the impact of your application and infrastructure dependencies on workload efficiency through comprehensive visibility. Utilize AI-driven analytics and tailored resource suggestions to proactively tackle potential issues before they disrupt your operations. Achieve cost reductions, automate processes, and enhance application resource management across your IT landscape. Our instantaneous decision-making engine designed for hybrid cloud setups empowers you to manage everything from a unified interface. Implement resource recommendations automatically at your preferred times. When integrated with Cisco AppDynamics, you can merge real-time insights into business performance and user experience with automated infrastructure management. Additionally, gain deeper insights by connecting with external APM tools like Dynatrace and New Relic. Ensure optimal performance for applications and workloads deployed on AWS while maximizing resource efficiency and minimizing downtime.

Ludwig serves as a low-code platform specifically designed for the development of tailored AI models, including large language models (LLMs) and various deep neural networks. With Ludwig, creating custom models becomes a straightforward task; you only need a simple declarative YAML configuration file to train an advanced LLM using your own data. It offers comprehensive support for learning across multiple tasks and modalities. The framework includes thorough configuration validation to identify invalid parameter combinations and avert potential runtime errors. Engineered for scalability and performance, it features automatic batch size determination, distributed training capabilities (including DDP and DeepSpeed), parameter-efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and the ability to handle larger-than-memory datasets. Users enjoy expert-level control, allowing them to manage every aspect of their models, including activation functions. Additionally, Ludwig facilitates hyperparameter optimization, offers insights into explainability, and provides detailed metric visualizations. Its modular and extensible architecture enables users to experiment with various model designs, tasks, features, and modalities with minimal adjustments in the configuration, making it feel like a set of building blocks for deep learning innovations. Ultimately, Ludwig empowers developers to push the boundaries of AI model creation while maintaining ease of use.

Our cloud solutions deliver equivalent quality and quantity while costing less than half of traditional options, making them an intelligent choice for businesses aiming to enhance their financial efficiency. If your CI/CD processes are being hampered by exorbitant expenses, allow your developers to experience the lightning-fast capabilities of SonicInfra, enabling them to roll out features and resolve bugs with greater speed. Your engineers can leverage CI/CD without the burden of spending extensive time fine-tuning auto-scaling, as no specialized cloud engineer is required to manage costs effectively. Our infrastructure is optimized for high-volume computing needs, like CI/CD, where achieving high throughput is prioritized over minimizing latency. SonicInfra can seamlessly integrate with your CI/CD provider using just a single line of code, facilitating an effortless shift away from costly cloud settings. With our team's extensive experience across various tools and platforms, we are ready to assist you in maximizing your CI/CD efficiency. We are committed to ensuring that your transition is not only smooth but also beneficial to your overall operational performance.

Revamp banking functions across various sectors, including retail, corporate, small and medium enterprises, Islamic banking, microfinance, and distinct financial entities. Oracle FLEXCUBE empowers banks to initiate their digital transformation journeys, ensuring they remain relevant, competitive, and compliant amidst the rapidly changing landscape of the banking sector. It enables the launch and management of a broad spectrum of customized products and services, facilitating efficient growth and expansion. Banks can swiftly adjust their operations to accommodate shifting customer expectations, business needs, and regulatory demands. Furthermore, the platform allows for seamless collaboration within ecosystems to create and enhance innovative financial products and services. It ensures operational efficiency at a large scale while providing flexibility in deployment and operations. The onboarding process for new customers is made simple, and personalized product offerings can be provided effortlessly. Additionally, it accelerates the decision-making process for lending products, reducing the time required for approvals and decisions. Moreover, the corporate lending lifecycle is streamlined, covering everything from origination to the management of credit, collateral, syndication, and beyond, thereby enhancing overall operational effectiveness. This comprehensive approach ultimately positions banks to thrive in an increasingly competitive market.

Prevasio is a cloud security platform powered by AI that delivers extensive visibility, automatic threat detection, and strong defense for cloud-based applications. It facilitates the automatic discovery and mapping of cloud infrastructure, pinpointing resources and illustrating their role in powering applications, thereby offering unmatched visibility along with actionable insights. The platform’s agentless Cloud-Native Application Protection Platform (CNAPP) covers the entire CI/CD pipeline through to runtime, ensuring a seamless and effective approach to security management. By assessing risks according to their potential impact on business applications and their severity, Prevasio enables organizations to concentrate on the most critical vulnerabilities. Furthermore, it enhances cloud compliance by continuously monitoring assets, ensuring compliance with industry standards and regulations. Additionally, Prevasio's Infrastructure-as-Code (IaC) scanning allows for the early identification of vulnerabilities during the development cycle, safeguarding cloud infrastructure prior to its construction. This proactive approach not only streamlines security processes but also fosters a culture of security-first development within organizations.

Google Cloud Deploy is a comprehensive continuous delivery service that streamlines the deployment of applications to various platforms such as Google Kubernetes Engine (GKE), Cloud Run, and Anthos. Users can easily create delivery pipelines and oversee the flow of releases through different environments, including testing, staging, and production. The service provides convenient one-click promotion and rollback options for releases, which can be accessed through the web console, command-line interface, or API. As a fully managed service, it eliminates the need for users to set up any infrastructure and dynamically scales to enhance both cost-efficiency and performance. Moreover, it integrates smoothly with Google Cloud's Identity and Access Management (IAM), Cloud Logging, and Cloud Audit Logs, creating a cohesive platform for monitoring and managing release candidates throughout an organization. It also allows for seamless connections with widely-used DevOps tools, such as continuous integration systems and ticketing platforms, enhancing collaboration and efficiency within development teams. Overall, Google Cloud Deploy is designed to facilitate a more effective and agile deployment process, making it an invaluable resource for teams looking to optimize their application delivery workflows.

Orchid Security employs a passive listening approach to consistently identify both self-hosted applications, which you oversee, and third-party SaaS applications, offering a thorough inventory of your enterprise's applications alongside critical identity attributes such as multi-factor authentication (MFA) enforcement, the presence of rogue or orphaned accounts, and role-based access control (RBAC) privilege details. By leveraging state-of-the-art AI analytics, Orchid Security automatically evaluates the identity technologies, protocols, and native authentication and authorization processes of each application. The identity controls are then measured against various privacy laws, cybersecurity frameworks, and best practices, including PCI DSS, HIPAA, SOX, GDPR, CMMC, NIST CSF, ISO 27001, and SOC2, in order to identify potential vulnerabilities in your cybersecurity stance and compliance adherence. Not only does Orchid Security provide insights into these vulnerabilities, but it also empowers organizations to swiftly and effectively address these issues without the need for code alterations, thus enhancing overall security posture. This proactive approach ensures that enterprises can maintain compliance while minimizing their risk exposure.

Accelerate your application development and ease database management with the premier hybrid multi-cloud DBaaS that supports Microsoft SQL Server, Oracle, EDB, PostgreSQL, MySQL, MariaDB, and MongoDB. Integrate databases seamlessly into your software development process while minimizing administrative tasks and ensuring optimal performance, resilience, and security for development, testing, and production environments. Provision, clone, and refresh databases effortlessly from development settings, allowing for greater efficiency. Automate administrative functions to maintain consistency with best practices, giving database administrators more time to focus on high-impact activities. Experience dependable performance, scalability, and security across all databases hosted on a hybrid multi-cloud framework. Facilitate collaboration for platform and operations teams by treating databases as code through REST API and Kubernetes integrations. The NDB Operator for Kubernetes empowers developers to provision, clone, and restore databases with minimal code, significantly reducing time-to-market. By leveraging these advanced capabilities, teams can enhance their workflow and drive innovation more effectively than ever before.

The Nutanix Kubernetes Platform (NKP) streamlines platform engineering by minimizing operational challenges and ensuring uniformity across various environments. It offers all the necessary elements for a production-ready Kubernetes setup within a fully integrated, turnkey framework. You can deploy it in public cloud settings, on-premises, or at edge locations, with or without the Nutanix Cloud Infrastructure. The platform is built from upstream CNCF projects that are not only fully integrated and validated but also easily replaceable, preventing vendor lock-in. It simplifies the management of complex microservices while improving observability and security. Additionally, it provides robust multi-cluster management features for your public cloud Kubernetes deployments without necessitating a shift to a different runtime. By harnessing the power of AI, it helps users maximize their Kubernetes experience through anomaly detection paired with root cause analysis, as well as an intelligent chatbot that offers best practices and fosters consistency in operations. This comprehensive approach enables teams to focus more on innovation rather than being bogged down by operational hurdles.

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.

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.

Cloudfleet provides a Kubernetes experience that spans from datacenters to the cloud and edge, ensuring it meets its intended purpose. With just-in-time infrastructure, automated updates, and sophisticated permissions management, users can effortlessly oversee their clusters through a unified interface. As a comprehensive multi-cloud and hybrid Kubernetes solution, Cloudfleet streamlines the setup of your infrastructure by enabling automatic server provisioning across both on-premises settings and a dozen different cloud service providers, enhancing efficiency and flexibility for your operations. This approach not only minimizes the complexity of managing diverse environments but also empowers users to focus more on their core objectives.

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.

Token Security presents an innovative strategy tailored for the booming era of Non-Human Identities (NHI), emphasizing a machine-first approach to identity security. In today's digital landscape, identities are omnipresent and often unmanaged; they manifest as machines, applications, services, and workloads, continuously generated by various sources throughout the day. The intricate and sluggish nature of managing these identities has resulted in an attack surface that organizations find difficult to navigate. Rather than concentrating solely on human identities, Token prioritizes the resources being accessed, swiftly revealing who accesses which resources, identifying vulnerabilities, and ensuring security without disrupting operations. Furthermore, Token adeptly identifies all identities across cloud environments, seamlessly integrating intricate components such as Kubernetes, databases, servers, and containers, thereby consolidating relevant identity data into a cohesive perspective. This comprehensive approach not only enhances security but also simplifies the management of identities within increasingly complex infrastructures.

O-mega stands out as the first-ever productivity platform tailored specifically for multi-agent teams, empowering organizations to create AI agents that can operate independently. These intelligent agents are engineered to perform actions safely and judiciously, understanding the appropriate tools and conditions necessary for task completion. They work seamlessly across various processes, departments, roles, and levels of authorization, all while maintaining an awareness of the organization's mission, guidelines, and industry regulations. O-mega provides universal connectivity for agents to engage with any platform, API, web browser, or legacy system, such as Slack, GitHub, Dropbox, Google, Microsoft, AWS, Shopify, Salesforce, Stripe, WordPress, LinkedIn, Twitter, YouTube, Discord, Apple, WhatsApp, and many others. This extensive connectivity facilitates the automation of a wide range of business processes through agentic process automation, enabling AI agents to manage tasks that include writing and publishing content, processing invoices, onboarding new team members, and creating weekly financial summaries. Ultimately, O-mega redefines efficiency by allowing organizations to leverage AI for streamlined operations and enhanced productivity.

Kubiya is an innovative internal developer platform powered by AI that utilizes conversational technology to optimize DevOps workflows. By seamlessly connecting with existing tools and platforms, it enables developers to engage with their systems through natural language, significantly decreasing the time required for automation and boosting overall productivity. This platform features AI-driven assistants that can efficiently manage routine functions such as overseeing Jira queues, provisioning infrastructure, and applying just-in-time cloud permissions, thereby empowering engineering teams to concentrate on more strategic tasks. With its agentic-native architecture, Kubiya guarantees reliable and secure operations, adhering to rigorous enterprise security standards and compliance with corporate policies. Additionally, the platform integrates effortlessly into communication tools, including Slack and Microsoft Teams, offering a user-friendly conversational interface for task delegation and automation. As a result, Kubiya not only enhances efficiency but also fosters a more collaborative environment for development teams.

ZEST Security presents an innovative platform for risk resolution that harnesses the power of AI to transform cloud risk remediation specifically for security teams. In contrast to conventional security measures that simply point out vulnerabilities, ZEST takes a proactive approach by linking the appropriate team to the necessary solutions, thus shortening the duration from vulnerability identification to effective remediation. This comprehensive platform ensures full remediation coverage by juxtaposing the intended DevOps configuration with the current cloud runtime environment, facilitating effortless detection and addressing of risks in both managed and unmanaged cloud settings. With automated root cause analysis, the platform accurately identifies the source of issues, down to specific assets and lines of code, enabling teams to tackle multiple challenges with minimal adjustments. Furthermore, the AI-driven risk resolution pathways significantly shorten the mean time to remediation and remove the need for manual triage through the application of dynamic remediation techniques. As a result, security teams can respond to threats more swiftly and efficiently than ever before.