What is Confident AI?

Confident AI is an evaluation platform designed for assessing large language models (LLMs). It enables companies to benchmark and unit test LLM applications, including chatbots and retrieval-augmented generation (RAG) systems. The platform allows easy generation, management, and sharing of evaluation datasets and test cases, centralizing testing processes to enhance efficiency.

With over 12 custom metrics and automatic regression tracking, users can ensure LLMs operate as expected. The tool facilitates A/B testing to identify optimal configurations and offers detailed monitoring to streamline workflows, thereby saving significant time for development teams.

Confident AI details

Company: Confident AI, Inc.
Jurisdiction: California
Data residency: United States, Singapore
Stated compliance: SOC 2, HIPAA

Compliance as stated in Confident AI's own published documentation.

Confident AI tech specs

Works with: OpenAI / GPT
Cloud: AWS Google Cloud Azure
Integrations: Slack Discord Microsoft Teams GitHub

Confident AI pricing

Free trial

Free forever $0

Starter $24.99 per user/mo

Premium custom

Auto-renewal: Yes

Trial, refund and renewal terms as stated in Confident AI's own published documentation.

Verify on the official pricing page.

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Confident AI's key features

Benchmarking LLM applications
Generation and management of evaluation datasets
Custom metrics for performance assessment
A/B testing capability
Automatic regression tracking

Confident AI use cases

Efficiently benchmark large language models for your enterprise's chatbot applications using Confident AI's pre-built evaluation datasets, ensuring optimal performance and user satisfaction.
Streamline the testing process for retrieval-augmented generation systems by leveraging Confident AI's centralized management tools, allowing teams to easily generate and share test cases without redundancy.
Conduct A/B testing to identify the best configurations for LLMs while utilizing the platform's detailed monitoring capabilities, significantly reducing development time and enhancing model reliability.