Premai: Building and Scaling Private AI Models

Premai is an AI platform designed for teams and enterprises to train, fine-tune, and deploy private, task-specific large language models (LLMs) with speed, accuracy, and full control. It enables users to create custom LLMs optimized for specific workflows, outperforming general-purpose models in efficiency and cost. Premai supports a wide range of open-source models including Llama, Qwen, and GPT-OSS-20B, and offers powerful features such as model compression, fine-tuning, evaluation, and deployment across multiple environments. With end-to-end isolation and SOC 2 certification, Premai ensures data security and model ownership, allowing users to export and self-host models without vendor lock-in.

Key Features:

• Custom Model Creation: Build specialized LLMs in under 30 minutes.
• Fine-Tuning: Supports LoRA, QLoRA, full fine-tuning, GRPO, and DPO.
• Model Compression: Reduce model size by up to 70% and inference costs by up to 80%.
• Data Preparation & Generation: Tools for dataset creation and management.
• Evaluation: Custom evaluations using natural language and LLM-as-Judge evaluators.
• Deployment Options: Serverless inference, dedicated GPU endpoints, VPC, and on-prem hosting.
• Model Ownership: Full control and exportability of model weights.
• Security & Compliance: SOC 2 certified with 100% data isolation and no data leakage.
• Scalability: Supports enterprise-grade workloads with dedicated GPUs and engineering support.
• No Vendor Lock-in: Models can be deployed on any cloud or infrastructure.

Pricing: Premai offers a freemium model with usage-based pricing for developers and a comprehensive enterprise plan. Developer pricing includes $0.010 per datapoint for data generation and evaluation, with fine-tuning credits and inference priced per token. The enterprise plan includes unlimited experiments, dedicated resources, and full support.

Conclusion: Premai is a powerful, secure, and scalable platform for building private AI models with full ownership and control, making it ideal for enterprises and developers seeking high performance and cost efficiency in AI deployment.