Vellum

Vellum

⭐ 4.5

Vellum is a development platform for building, testing, and deploying LLM applications with prompt engineering and production monitoring.

Screenshots

Vellum screenshot

About Vellum

Vellum is a comprehensive development platform designed to streamline the creation and deployment of Large Language Model applications. It provides a unified workspace where teams can engineer prompts, manage versions, run tests, and monitor performance across all major LLM providers. The platform eliminates vendor lock-in by supporting OpenAI, Anthropic, Google, and other leading models, allowing developers to select the best solution for each use case. The platform excels at collaboration and iteration. Teams can compare prompts and models side-by-side, test variations at scale, and track changes through built-in version control. Vellum's no-code LLM builder enables non-technical users to create sophisticated applications including chatbots, Q&A systems, document analyzers, and intent classifiers without writing code, while experienced developers benefit from programmatic access and workflow automation. Production readiness is a core strength. Vellum handles deployment, manages LLM chains, and provides detailed observability into how applications perform in production. Users can incorporate proprietary data as context, evaluate outputs systematically through test suites, and gain insights into feature effectiveness through monitoring dashboards. This end-to-end approach—from prompt experimentation to production insights—reduces time-to-market and improves application quality.

Pros

👍 Multi-provider LLM support without vendor lock-in 👍 No-code builder enables non-technical users to create applications 👍 Comprehensive production monitoring and observability features 👍 Collaborative prompt testing and version control workflows 👍 Fast deployment with semantic search and document integration

Cons

👎 Learning curve may exist for advanced workflow automation features 👎 Pricing structure not detailed for different team sizes 👎 Limited information on fine-tuning capabilities and customization depth