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Predibase: Private Cloud for Fine-Tuning & Serving ML Models

Fine-tune and deploy Large Language Models (LLMs) efficiently with Predibase. Leverage open-source models and powerful tools for AI development and data analysis.

Fine-tune and deploy Large Language Models (LLMs) efficiently with Predibase. Leverage open-source models and powerful tools for AI development and data analysis.

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Updated on April 19, 2025 (3 months ago)

Accelerating AI Development with Predibase: Your Platform for Large Language Models

Large language models (LLMs) are revolutionizing AI development, but fine-tuning and deploying them can be complex. Predibase is a powerful developer platform designed to simplify this process, empowering engineers to efficiently train, fine-tune, and serve any open-source ML model or LLM within their private cloud. Utilizing the innovative Lorax infrastructure, Predibase leverages techniques like quantization and low-rank adaptation for optimized performance and cost-effectiveness. Developers can seamlessly deploy multiple fine-tuned adapters on a single GPU, accelerating time-to-market for AI projects across diverse applications such as customer service automation, document generation, and sentiment analysis. With its user-friendly interface and robust features, Predibase democratizes access to cutting-edge LLM technology, enabling developers of all skill levels to build and deploy sophisticated AI solutions.

Pricing

Predibase offers a tiered pricing structure for its LLM platform. The "Developer" tier starts at $25 with free credits and includes access to shared serverless inference, basic fine-tuning capabilities, and 2 concurrent training jobs. For more advanced users, the "Enterprise" tier provides unlimited best-in-class fine-tuning, dedicated servers, pay-as-you-go pricing, volume discounts, guaranteed instances for scaling, and additional support. Predibase also offers private serverless inference with a variety of GPU options priced hourly and shared serverless inference that is free up to 1 million tokens per day. Fine-tuning costs vary based on model size, dataset size, epochs, and fine-tuning method (Turbo Lora, Turbo), starting at $3.00 per million tokens. Key Points: Tiered Pricing: Developer ($25) and Enterprise tiers available. Shared Serverless Inference: Free up to 1M tokens/day. Private Serverless Inference: Hourly pricing based on GPU type. Fine-tuning Costs: $3.00 per million tokens (minimum). Enterprise Features: Volume discounts, dedicated servers, guaranteed instances, and additional support.

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