Compare Evidently AI vs. Gantry

An open-source platform for monitoring machine learning models offers robust observability features. It allows users to evaluate, test, and oversee models throughout their journey from validation to deployment. Catering to a range of data types, from tabular formats to natural language processing and large language models, it is designed with both data scientists and ML engineers in mind. This tool provides everything necessary for the reliable operation of ML systems in a production environment. You can begin with straightforward ad hoc checks and progressively expand to a comprehensive monitoring solution. All functionalities are integrated into a single platform, featuring a uniform API and consistent metrics. The design prioritizes usability, aesthetics, and the ability to share insights easily. Users gain an in-depth perspective on data quality and model performance, facilitating exploration and troubleshooting. Setting up takes just a minute, allowing for immediate testing prior to deployment, validation in live environments, and checks during each model update. The platform also eliminates the hassle of manual configuration by automatically generating test scenarios based on a reference dataset. It enables users to keep an eye on every facet of their data, models, and testing outcomes. By proactively identifying and addressing issues with production models, it ensures sustained optimal performance and fosters ongoing enhancements. Additionally, the tool's versatility makes it suitable for teams of any size, enabling collaborative efforts in maintaining high-quality ML systems.

Gain a comprehensive understanding of your model's efficacy by logging both inputs and outputs while enhancing them with relevant metadata and user insights. This approach allows you to truly assess your model's functionality and identify areas that require refinement. Keep an eye out for errors and pinpoint underperforming user segments and scenarios that may need attention. The most effective models leverage user-generated data; therefore, systematically collect atypical or low-performing instances to enhance your model through retraining. Rather than sifting through countless outputs following adjustments to your prompts or models, adopt a programmatic evaluation of your LLM-driven applications. Rapidly identify and address performance issues by monitoring new deployments in real-time and effortlessly updating the version of your application that users engage with. Establish connections between your self-hosted or third-party models and your current data repositories for seamless integration. Handle enterprise-scale data effortlessly with our serverless streaming data flow engine, designed for efficiency and scalability. Moreover, Gantry adheres to SOC-2 standards and incorporates robust enterprise-grade authentication features to ensure data security and integrity. This dedication to compliance and security solidifies trust with users while optimizing performance.