Compare Handit vs. Oumi

Handit.ai serves as an open-source platform that enhances your AI agents by perpetually refining their performance through the oversight of every model, prompt, and decision made during production, while simultaneously tagging failures as they occur and creating optimized prompts and datasets. It assesses the quality of outputs using tailored metrics, relevant business KPIs, and a grading system where the LLM acts as a judge, automatically conducting AB tests on each improvement and presenting version-controlled diffs for your approval. Featuring one-click deployment and instant rollback capabilities, along with dashboards that connect each merge to business outcomes like cost savings or user growth, Handit eliminates the need for manual adjustments, guaranteeing a seamless process of continuous improvement. By integrating effortlessly into any environment, it provides real-time monitoring and automatic assessments, self-optimizing through AB testing while generating reports that demonstrate effectiveness. Teams that have adopted this technology report accuracy enhancements exceeding 60%, relevance increases surpassing 35%, and an impressive number of evaluations conducted within just days of integration. As a result, organizations are empowered to focus on strategic initiatives rather than getting bogged down by routine performance tuning.

Oumi is an entirely open-source platform that enhances the complete lifecycle of foundation models, encompassing everything from data preparation and training to evaluation and deployment. It facilitates the training and fine-tuning of models with parameter counts ranging from 10 million to an impressive 405 billion, utilizing cutting-edge methodologies such as SFT, LoRA, QLoRA, and DPO. Supporting both text-based and multimodal models, Oumi is compatible with various architectures like Llama, DeepSeek, Qwen, and Phi. The platform also includes tools for data synthesis and curation, allowing users to efficiently create and manage their training datasets. For deployment, Oumi seamlessly integrates with well-known inference engines such as vLLM and SGLang, which optimizes model serving. Additionally, it features thorough evaluation tools across standard benchmarks to accurately measure model performance. Oumi's design prioritizes flexibility, enabling it to operate in diverse environments ranging from personal laptops to powerful cloud solutions like AWS, Azure, GCP, and Lambda, making it a versatile choice for developers. This adaptability ensures that users can leverage the platform regardless of their operational context, enhancing its appeal across different use cases.