This company offers a Kubernetes extension that simplifies machine learning operations by orchestrating model training and deployment across multiple cloud environments. Their platform provides custom resources for managing models, servers, datasets, and notebooks, enabling organizations to scale their ML initiatives.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations face challenges in deploying and managing AI models, particularly large language models (LLMs), within their own environments due to concerns around data privacy, security, vendor lock-in, and the complexity of integrating with existing systems. The rapidly evolving landscape of open-source AI models also makes it difficult to quickly adopt and scale new technologies.
Solution
Substratus AI Engine is a Kubernetes-based platform that enables enterprises to deploy, manage, and scale AI models within their own infrastructure, ensuring data sovereignty and control. The platform supports a wide range of open-source models, including DeepSeek, Llama, and Mistral, and provides secure API connectors for integrating with internal systems and data warehouses. Substratus offers sandboxed execution environments with granular permission controls, allowing organizations to run AI agents securely while maintaining compliance with internal policies. By leveraging the open-source KubeAI project, Substratus simplifies the deployment and management of LLMs, speech processing, and vector embeddings, optimizing performance and resource utilization across various hardware configurations.
Target Audience
The primary target audience includes enterprises and security-conscious startups seeking to deploy and manage AI models within their own environments while maintaining data privacy, security, and control.
Features
- Support for deploying and managing leading open-source models like DeepSeek, Llama, and Mistral
- High-performance inference optimized for various hardware configurations (NVIDIA, AMD GPUs, Google TPUs, and CPUs)
- Secure API connectors for integrating AI models with enterprise systems, databases, and data warehouses
- Sandboxed execution environments with granular permission controls and audit logging for secure AI agent execution
- Built-in chat UI and integration options for existing user interfaces
- Pluggable AI policy enforcement for implementing organizational AI governance
- Optimized LLM routing for increased throughput and reduced response times
- Model caching for quickly responding to spikes in load
- Dynamic adapters for swapping model adapters on the fly for specialized tasks