Vellum

From experiment to production

Vellum gives AI product and engineering teams a shared place to build prompts, compare model outputs, run dataset-based evaluations, and assemble multi-step workflows. The visual editor lets product specialists contribute without taking deployment control away from developers. Once an experiment is ready, it can be exposed through a versioned API, while logs help trace what happened in production.

Where it earns its keep

The platform makes most sense after an application has moved beyond a single prompt and changes need testing before release. A free tier supports initial experimentation; paid plans expand usage, collaboration, and operational controls, while larger deployments require negotiated terms. Costs become harder to predict as execution volume grows, and adopting Vellum adds another layer between application code and model providers.

• Best fit: Teams maintaining customer-facing LLM features.
• Watch for: Workflow limits and provider spending.