Openlayer is a platform designed for teams building AI and large language model (LLM) applications. It provides tools to test, evaluate, and monitor AI models and pipelines throughout the development lifecycle. Users can run automated evaluations against custom or pre-built tests, track model performance over time, and detect regressions or quality issues before and after deployment. The platform supports integration into CI/CD workflows, enabling teams to gate deployments based on evaluation results. In production, Openlayer monitors live traffic, surfaces failures, and provides visibility into how models behave with real user inputs. It is aimed at AI engineers, ML engineers, and product teams who need structured quality assurance processes for LLM-based features and applications. The platform supports a range of evaluation types including hallucination detection, toxicity, relevance, and custom metrics defined by the user.
Target audience and deployment
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- API
Key features
Use cases
- Evaluate LLM outputs automatically
- Monitor AI models in production
- Gate deployments with CI/CD testing
- Debug model and pipeline failures
- Track model performance over time
Best for
- AI Engineers who need to systematically test and evaluate LLM applications before deployment
- ML Engineers who need to monitor model quality and detect regressions in production
- Product Teams who need visibility into how AI features perform with real users
Integrations
Developer
GitHub, GitLab
AI models included
OpenAI, Anthropic, Azure OpenAI, Cohere, Mistral
Other
LangChain, LlamaIndex