Incredible Pricing

Yes. RagMetrics was built for benchmarking large language models. You can run identical tasks across multiple LLMs, compare their outputs side by side, and score them for reasoning quality, hallucination risk, citation reliability, and output robustness.

Yes. RagMetrics provides a powerful API for programmatically scoring and comparing LLM outputs. Use it to integrate hallucination detection, prompt testing, and model benchmarking directly into your GenAI pipeline.

RagMetrics can be deployed in multiple ways, including as a fully managed SaaS solution, inside your private cloud environment (like AWS, Azure, or GCP), or on-premises for organizations that require maximum control and compliance.

Running an experiment is simple. You connect your LLM or retrieval-augmented generation (RAG) pipeline—such as Claude, GPT-4, Gemini, or your own model—define the task you're solving, upload a labeled dataset or test prompts, select your scoring criteria like hallucination rate or retrieval accuracy, and then run the experiment through the dashboard or API.

To run an evaluation, you’ll need access to your LLM’s API key, the endpoint URL or model pipeline, a dataset or labeled test inputs, a clear task description, and a definition of success for that task. You can also include your own scoring criteria or subject matter expertise.

RagMetrics is model-agnostic and supports any public, private, or open-source LLM. You can paste your custom endpoint, evaluate outputs from models like Mistral, Llama 3, or DeepSeek, and compare results to popular models like GPT-4, Claude, and Gemini using the same scoring framework.