Engineering teams are increasingly deploying multiple large language models (LLMs) in production, with nearly 70% of surveyed ML practitioners having already done so. Fine-tuning LLMs for specific tasks is identified as a cost-effective deployment strategy that requires access to advanced management tools. A major update to the Predibase platform now allows teams to manage LLM deployments through an enhanced user interface, offering features such as dedicated and serverless deployment options. Serverless deployments are cost-efficient for experimentation, as they charge per token and eliminate the need for idle GPU management. As usage scales, dedicated deployments become more appealing. The platform's new Deployments page offers a comprehensive view of all deployments, streamlining the monitoring and management process by displaying essential details like status and model context. The update also simplifies the creation of new deployments and provides detailed event histories and logs for better operational insight. Predibase aims to be the central hub for LLM management, offering enhanced visibility and control to facilitate rapid AI development and deployment. Users can start fine-tuning and serving models for free with an initial credit offering.