An advanced data intelligence and ML observability platform for enterprise teams, specializing in the evaluation, debugging, and monitoring of large language models (LLMs) and other generative AI applications from development to production.
Galileo is a comprehensive platform designed for machine learning teams developing applications with large language models and unstructured data. It provides a suite of tools to rapidly evaluate, debug, and monitor models throughout their entire lifecycle. The platform excels at automatically identifying and surfacing critical issues like data quality errors, model hallucinations, prompt failures, and performance regressions. Its key value proposition is accelerating the path to production for reliable GenAI products by providing actionable insights to fix problems in both datasets and model responses. Galileo serves data scientists, ML engineers, and product teams within enterprises who need to build and maintain high-quality AI systems with confidence.
Enterprise ML Teams, Data Scientists, Machine Learning Engineers, and LLM/GenAI Developers building applications with unstructured data.
Based on 0 reviews
2021
San Francisco, USA
Community
For individuals and small teams getting started. Includes up to 10k records/month, 1 user, community support, and access to core evaluation and observability features.
Free
Scale
For growing teams and production use cases. Includes custom usage limits, multiple users, dedicated support, and advanced collaboration features.
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Enterprise
For large organizations with security and scale requirements. Features unlimited usage, private cloud deployment (VPC), SSO, premium support, and custom onboarding.
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A leading ML observability platform that offers broader support for both structured and unstructured data, making it a strong choice for companies with diverse model portfolios.
A direct competitor focused on AI observability and data monitoring, often chosen for its strong open-source component (LangKit) for LLM monitoring.
An AI Observability platform known for its deep focus on model explainability (XAI) and fairness, which can be critical for teams in highly regulated industries.
More of a full MLOps platform, W&B is a popular alternative for teams who want an all-in-one solution for experiment tracking, model registry, and workflow automation, rather than a specialized evaluation tool.
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