Confident AI
The cloud platform built on DeepEval, the pytest-compatible LLM testing framework
Confident AI is profiled here as a Evaluation tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Confident AI is a San Francisco-based company founded in 2024 by Jeffrey Ip and Kritin Vongthongsri, backed by Y Combinator. The company builds two products: DeepEval, an Apache 2.0 Python framework with 15,000+ GitHub stars that runs LLM unit tests using pytest-compatible syntax, and Confident AI, the cloud platform that adds team collaboration, dataset management, production monitoring, and dashboards on top of it. The relationship between the two mirrors Next.js and Vercel: DeepEval runs locally or in CI without a Confident AI account, and the platform extends it for teams that need cross-functional visibility beyond what a local test runner provides.
Key Capabilities
Pytest-native LLM test runner: DeepEval integrates directly into existing pytest workflows, letting backend engineers write LLM test cases using the same patterns they already use for software unit tests, with CI/CD blocking on metric thresholds
Comprehensive evaluation metric library: Pre-built metrics for RAG pipelines, AI agents, and chatbots run against any LLM provider using LLM-as-judge, statistical methods, or local NLP models, with natural language explanations attached to each score
Production tracing and monitoring: An SDK and OpenTelemetry integration capture every LLM call, tool call, and agent step in production, with alerting on quality degradation and cost and latency tracking per trace
DeepTeam red teaming framework: A separate Apache 2.0 library with 1,700+ GitHub stars that applies adversarial penetration testing techniques to LLM systems, with an MCP server for running red team scans directly from Cursor or Claude Code
No-code eval workflows: Product managers, QA teams, and domain experts connect to an LLM application over HTTP and run evaluation workflows without writing code, removing the engineering bottleneck from prompt quality decisions
Self-hosted enterprise deployment: The full Confident AI platform deploys into a customer's own VPC or on-premise infrastructure on the Enterprise plan, keeping trace data and evaluation results within the customer's network
Alternative tools
- Gentrace
Testing and evaluation for generative AI applications
- HELM
Reproducible, multi-scenario benchmarking of foundation models
- lm-evaluation-harness
Standard framework for benchmarking language models
- garak
Vulnerability scanner for large language models
- DeepChecks
Validate ML models, LLM applications, and AI agent decisions across every development stage
- Evidently AI
Evaluate, test, and monitor traditional ML models and LLM applications from one framework