Openlayer: AI Testing, Observability & Governance Platform

Openlayer is a unified platform built for enterprises that need to rigorously test, monitor, and govern their AI systems throughout the entire lifecycle—from early ML prototypes to production-grade LLM applications. Rather than treating evaluation as a one-off task, Openlayer embeds continuous testing into your workflow, giving teams a shared workspace to hunt down bugs in models and datasets, track changes through versioned commits, and validate performance before and after deployment. Beyond evaluation, Openlayer doubles as a real-time observability layer, letting engineers trace production requests, monitor inference logs, and annotate outputs with feedback as issues arise. This closes the loop between debugging and live performance, making it easier to catch regressions and drift before they impact users. Built-in governance tooling also helps AI teams demonstrate compliance and maintain oversight as models scale. Designed to slot into existing engineering stacks, Openlayer connects natively with Git for version control, offers SDKs for multiple programming languages, and supports virtually every major LLM provider. Its REST API and CLI make it fully customizable, so ML engineers, data scientists, and MLOps teams can automate testing pipelines and integrate governance checks without disrupting their current tools.

Start by connecting Openlayer to your existing stack using Git, the provided SDKs, or the REST API. Create a project and define tests to evaluate your ML models or LLMs against key quality and performance criteria. Once live, use the observability tools to monitor production requests and trace system behavior in real time. Annotate flagged requests with feedback to guide debugging, commit new model or dataset versions as issues are resolved, and collaborate with teammates in a shared workspace to keep everyone aligned on model health and governance status.