If you're looking for Forefront replacement, Predibase is another option worth considering. It lets developers customize and deploy large language models (LLMs) with a low cost and high performance. With support for state-of-the-art methods like quantization and low-rank adaptation, you can fine-tune models for specific tasks like classification, information extraction and code generation. The service also includes a low-cost serving infrastructure, free serverless inference for up to 1 million tokens per day, and enterprise-level security with SOC-2 compliance.
Another option is Airtrain AI, a no-code compute service for data teams that need to run big data pipelines. It's geared for data teams, but it also has a powerful suite of tools for big language models, including an LLM Playground for trying out more than 27 open-source and proprietary models. With features like AI Scoring for testing models with your own data and task descriptions, and a Community Support system, Airtrain AI can make LLMs more accessible and affordable, letting you quickly test, fine-tune and deploy your own custom AI models.
If you need a DevOps platform, check out Keywords AI. It's designed to be a one-stop shop for the entire lifecycle of AI software, with the ability to handle hundreds of concurrent calls without a performance penalty and integration with OpenAI APIs. With tools for rapid development, visualization, logging, performance monitoring and data collection, Keywords AI is geared for AI startups, letting developers focus on products, not infrastructure.
Each of these services has its own strengths for fine-tuning and deploying open-source language models, so they're all good Forefront alternatives.