If February had a theme, it would be velocity. At kluster.ai, we spent the shortest month of the year laying track for the future of AI, and the numbers, partnerships, and community momentum speak for themselves.

If February had a theme, it would be velocity. At kluster.ai, we spent the shortest month of the year laying track for the future of AI, and the numbers, partnerships, and community momentum speak for themselves.

Enterprises face precision challenges with LLMs in specialized tasks, leading to RAG and fine-tuning. RAG retrieves external data for real-time relevance but needs quality sources and maintenance. Fine-tuning embeds domain knowledge, offering consistency but requires periodic updates. Choice depends on data needs and complexity.

Open weights models allow developers to access and modify the underlying parameters of AI, enabling customization and adaptation. Restricted weights models, controlled by providers like OpenAI, limit access and customization.

The benefits of batch inference over real-time inference for large language models

Launch of fine-tuning capabilities on kluster.ai

Transparent reasoning meets state of the art performance

Transform your AI workflows with dynamic scaling, cutting-edge models, and game-changing cost efficiency.