Question: I need a platform that helps me build and deploy trustworthy AI models with robust testing and evaluation tools.

If you need a foundation to develop and deploy AI models you can trust, Openlayer is a good option. It lets you develop, deploy and manage high-quality AI models, in particular large language models (LLMs). It offers automated testing, monitoring and alerts, versioning and tracking, and security compliance to ensure models are deployed correctly. It's geared for data scientists, ML engineers and product managers, with a free plan with limited features and a custom plan with more advanced features.

Another option is HoneyHive, a foundation geared for teams building GenAI applications. It offers a unified environment for collaboration, testing and evaluation. HoneyHive offers automated CI testing, production pipeline monitoring, dataset curation and distributed tracing with OpenTelemetry. It's good for use cases like debugging, online evaluation and benchmarking, and it offers integrations with common GPU clouds. It's good for developers and teams, with a free Developer plan and an Enterprise plan for bigger needs.

For a tool geared specifically to ensuring high-quality LLM applications, Deepchecks automates evaluation and monitoring. It can spot problems like hallucinations and bias, and it offers a "Golden Set" approach to create a rich ground truth for LLMs. With features for debugging and version comparison, Deepchecks is good for developers and teams who want to ensure their LLM-based software works as intended from development to deployment. The company offers several pricing tiers, including a free Open-Source option.

Last, Humanloop is designed to make it easier to develop and optimize LLM applications. It's a playground for developers and product managers to iterate on AI features, with tools for prompt management, evaluation and monitoring. Humanloop supports common LLM providers and offers SDKs for easy integration. It's good for rapid prototyping and for enterprise-scale deployment, and it's been adopted by some high-profile companies to improve efficiency and collaboration for AI development.