RapidMiner AI Hub

Basic Information

• Model: RapidMiner AI Hub (formerly known as RapidMiner Server and Altair AI Hub).
• Version: The latest stable version is 2026.0. Other recent versions include 2025.1, 2025.0, 2024.1, 2024.0, 10.3, 10.2, 10.1, and 10.0.
• Release Date: Specific release dates for individual versions are not provided, but the latest stable version, 2026.0, is current.
• Supported Operating Systems: Linux (64-bit), Windows Server (2008 R2, 2012, 2012 R2, 2016, 2019), Windows 10 (64-bit), Windows 11 (64-bit), macOS 13 (Ventura), and macOS 14 (Sonoma).
• Latest Stable Version: 2026.0.
• End of Support Date: Not explicitly specified in the provided information.
• End of Life Date: Not explicitly specified in the provided information.
• Auto-update Expiration Date: Not explicitly specified in the provided information.
• License Type: Primarily uses Altair Unit Licensing, which became the default from AI Hub version 10.1. It also supports traditional RapidMiner Licenses. Academic and student editions may have specific licensing terms.
• Deployment Model: Supports on-premise, cloud (via AWS and Microsoft Azure images), or hybrid deployments. It leverages Docker-compose or Kubernetes for orchestration.

Technical Requirements

• RAM:
  • Minimum for Server with attached Job Agent: 8GB.
  • Recommended for Server and each Job Agent: 32GB to 1TB.
  • Minimum for Real-Time Scoring: 2GB.
  • Recommended for Real-Time Scoring: 2GB.
  • Server application itself requires a minimum of 2GB. Job Agents typically allocate 128MB to 256MB RAM, with job containers handling process execution.
• Processor:
  • Minimum for Server with attached Job Agent: Dual core 2GHz.
  • Recommended for Server and each Job Agent: Quad core 3GHz or faster.
  • Minimum for Real-Time Scoring: Single core 2GHz.
  • Recommended for Real-Time Scoring: Quad core 3GHz or faster.
• Storage:
  • Minimum for Server repository: 10GB free disk space.
  • Recommended for Server repository: Sufficient free disk space to accommodate user data.
  • Minimum for Real-Time Scoring: 1GB free disk space.
  • The filesystem must support UTF-8.
• Display: A browser resolution of 1024x768 or higher is required for the server user interface.
• Ports:
  • Default web interface and RapidMiner Studio connection: 8080 (configurable).
  • Internal communication: 5672.
  • HTTPS (recommended for public services): 443.
  • Database ports vary (e.g., Oracle 1521).
• Operating System: Linux (64-bit), Windows Server (2008 R2, 2012, 2012 R2, 2016, 2019), Windows 10/11 (64-bit), macOS 13 (Ventura), macOS 14 (Sonoma).

Analysis of Technical Requirements: RapidMiner AI Hub offers a scalable architecture designed to accommodate a wide range of enterprise needs. The system requirements are flexible, allowing for minimal installations for development or testing and robust configurations for large-scale production environments. RAM is a critical resource, directly impacting the size of datasets that can be processed. The platform's ability to leverage Job Agents and Job Containers facilitates distributed processing, enabling efficient handling of computationally intensive tasks.

Support & Compatibility

• Latest Version: 2026.0.
• OS Support: Compatible with 64-bit versions of Linux, Windows 10/11, macOS 13 (Ventura), macOS 14 (Sonoma), and Windows Server editions (2008 R2, 2012, 2012 R2, 2016, 2019).
• End of Support Date: Not explicitly specified in the provided information.
• Localization: Not explicitly detailed, but as an enterprise solution, it is expected to support various international deployments.
• Available Drivers: Ships with the Oracle driver. Users may need to provide JDBC drivers for other database systems. It supports connections to a wide array of databases including PostgreSQL, Oracle, Microsoft SQL Server, MySQL, Snowflake, Teradata, HP Vertica, IBM Netezza, and NoSQL databases like MongoDB, Cassandra, and Apache Solr.

Analysis of Overall Support & Compatibility Status: RapidMiner AI Hub demonstrates strong compatibility across major operating systems and a broad spectrum of database technologies, making it suitable for diverse enterprise IT infrastructures. Its deployment flexibility, including Docker and Kubernetes support, further enhances its adaptability. The platform's continuous updates, with version 2026.0 being the latest, indicate ongoing development and support from the vendor.

Security Status

• Security Features: Includes robust authentication, authorization, data encryption, and auditing capabilities. It offers role-based access control and fine-grained permissions for shared repositories, models, and processes.
• Known Vulnerabilities: No specific known vulnerabilities were identified in the provided information.
• Blacklist Status: Not applicable.
• Certifications: No specific security certifications were explicitly mentioned.
• Encryption Support: Recommends and supports HTTPS for encrypted network traffic. It facilitates TLS offloading and the use of custom Certificate Authority (CA) certificates. Plain HTTP is not supported from AI Hub version 2025.0.
• Authentication Methods: Utilizes an integrated Identity Provider (KeyCloak) for centralized user management, Single Sign-On (SSO), and Two-Factor Authentication (2FA). It supports Federated Identity with SAML v2.0 or OAuth2, and integrates with LDAP servers like Active Directory for user federation. The Real-Time Scoring Agent specifically supports Basic Auth and OAuth2.
• General Recommendations: Users are advised to change the default admin password, configure strong password policies, enable mandatory 2FA, and always use HTTPS for public-facing services.

Analysis on the Overall Security Rating: RapidMiner AI Hub provides a comprehensive security framework designed for enterprise environments. Its reliance on industry-standard authentication protocols (SAML, OAuth2, LDAP) and an integrated Identity Provider ensures secure access and management. The emphasis on HTTPS encryption and fine-grained access controls contributes to a strong security posture, crucial for handling sensitive data and AI models.

Performance & Benchmarks

• Benchmark Scores: Specific benchmark scores were not found in the provided information.
• Real-world Performance Metrics: Designed for scalable execution through Job Agents and Job Containers. It offers virtually unlimited scalability by allowing the addition of more Job Agents and machines to the environment. The Real-Time Scoring Agent is optimized for very low-latency predictions, delivering actionable intelligence in real-time.
• Power Consumption: No specific data on power consumption was found.
• Carbon Footprint: No specific data on carbon footprint was found.
• Comparison with Similar Assets: Direct comparisons with similar assets were not provided in the search results. However, as an AI and machine learning platform, it competes with other data science and MLOps solutions.

Analysis of the Overall Performance Status: RapidMiner AI Hub is engineered for high performance and scalability, particularly in the context of AI and machine learning model deployment and real-time inference. Its distributed architecture allows for efficient resource utilization and expansion, making it suitable for demanding data science workloads. While specific benchmark figures are not available, its design principles prioritize the ability to handle large datasets and complex analytical processes effectively.

User Reviews & Feedback

User feedback highlights RapidMiner AI Hub's strengths in facilitating collaboration and efficient deployment of AI/ML models. It is praised for its shared repository, process version management, and the reusability of templates, which streamline team workflows. The platform's ability to easily deploy models, schedule workflows, and create web applications, including interactive dashboards, is highly valued. Its integration capabilities with third-party tools and its real-time scoring agent are also noted as significant advantages. The flexible licensing and diverse deployment options (on-premise, cloud, hybrid) contribute to its appeal. The visual workflow designer and support for both code-optional and code-friendly approaches make it accessible to a wide range of users, from domain experts to experienced coders.

While specific weaknesses are not extensively detailed in recent feedback, older discussions sometimes point to potential memory issues if the system is not adequately provisioned with RAM, especially when dealing with very large datasets.

Recommended use cases for RapidMiner AI Hub include the orchestration, management, and deployment of AI and machine learning models, collaborative data science projects, sharing resources, executing workflows, and integrating models into existing systems. It is particularly effective for automated data science, predictive modeling, and specific applications like customer churn analysis, sentiment analysis, and optimizing operational processes.

Summary

RapidMiner AI Hub, now part of Altair's portfolio, is a robust and scalable enterprise platform designed for the orchestration, management, and deployment of artificial intelligence and machine learning models. Its key strengths lie in its comprehensive security framework, flexible deployment options, and strong collaborative features. The platform supports a wide array of operating systems and database technologies, ensuring broad compatibility within diverse IT environments. Its architecture, leveraging Job Agents and Job Containers, is built for high performance and scalability, capable of handling demanding data science workloads and delivering real-time predictions. The integrated Identity Provider, support for industry-standard authentication protocols (SAML, OAuth2, LDAP), and emphasis on HTTPS encryption provide a secure foundation for sensitive data and AI assets.

While the platform excels in its core functionalities and offers extensive features for model deployment and collaboration, specific benchmark data and detailed information on power consumption or carbon footprint are not readily available in public documentation. Potential weaknesses, particularly concerning memory management, can arise if the underlying infrastructure is not adequately provisioned for large-scale data processing. However, the platform's design allows for significant scaling of resources to mitigate such issues.

RapidMiner AI Hub is highly recommended for organizations seeking an end-to-end solution for their data science and MLOps initiatives. It is particularly well-suited for teams requiring a collaborative environment, automated model deployment, and real-time scoring capabilities across various deployment models (on-premise, cloud, hybrid). Its user-friendly interface, catering to both code-free and code-friendly users, makes advanced AI accessible to a broader audience within an enterprise.

Please note: The information provided is based on publicly available data and may vary depending on specific device configurations. For up-to-date information, please consult official manufacturer resources.