Question: What are some options for deploying AI applications at scale, both in the cloud and on-premise?

Anyscale is a full-on system for building, deploying and scaling AI applications. It spans multiple clouds and on-premises environments, with features like workload scheduling, heterogeneous node control and optimized resource allocation. The system is based on the open-source Ray framework and supports a variety of AI models, including LLMs and custom generative AI models. Customers get a 50% discount on spot instances and a free tier with flexible pricing.

Abacus.AI is an all-purpose AI system for building and running large-scale AI agents and systems. It includes products like ChatLLM for building end-to-end RAG systems, AI Agents for automating complex workflows, and heavy-duty predictive and analytical tools. It's geared for enterprise use with high availability, governance and compliance features, and can handle real-time forecasting and anomaly detection.

If you need high-performance and energy-efficient AI compute, check out Groq. Its LPU Inference Engine is designed for high-speed compute for efficient AI model inference and can be deployed in the cloud or on-premises. The system is tuned for generative AI models and helps customers automate AI processing workflows, making it a good fit for customers who need fast and efficient AI inference.

Another option is LLMStack, an open-source system that lets developers build AI apps using pre-trained language models from big companies like OpenAI. It offers no-code abilities, vector databases for efficient data storage and multi-tenancy controls. LLMStack can run in the cloud or on-premises, and is good for tasks like building chatbots and AI assistants.