Vellum AI

What is Vellum AI?

Vellum AI is a comprehensive platform designed to accelerate the development and deployment of AI applications, particularly those powered by large language models (LLMs). It bridges the gap between technical and non-technical teams by providing a unified environment for prompt engineering, workflow orchestration, evaluation, and continuous improvement. Vellum supports integration with multiple AI models—open source, closed source, and self-hosted—allowing users to compare and optimize AI performance efficiently. Its low-code interface, combined with code-first SDKs, facilitates collaboration across cross-functional teams, enabling rapid iteration and deployment without frequent full redeployments. Additionally, Vellum offers advanced monitoring and observability tools to track AI decisions, costs, and latency, ensuring reliability and transparency in production.

Key Features

• Collaborative Prompt Engineering

  Visual and code-based tools for defining, editing, and testing prompts across multiple AI models in a shared environment.

• Multi-Model Playground

  Seamlessly compare and switch between different AI providers and models, including open source and proprietary options.

• Workflow Builder

  Create complex AI workflows and agentic systems using a declarative, type-safe framework with both GUI and code interfaces.

• Integrated Evaluation and Testing

  Quantitative testing framework with custom metrics and test case management to rigorously assess AI performance before deployment.

• Continuous Deployment & Monitoring

  Version control and one-click deployment with real-time monitoring of AI inputs, outputs, latency, costs, and decision traces.

• Document Retrieval & RAG Pipelines

  Built-in support for semantic search and retrieval-augmented generation using uploaded documents and vector databases.

Use Cases

• AI-Powered Chatbots : Build, test, and deploy conversational agents that integrate multiple data sources and models for customer support or internal tools.
• Collaborative AI Development : Enable cross-functional teams, including subject matter experts and engineers, to co-create, evaluate, and iterate AI features.
• Document Analysis & Search : Implement semantic search and retrieval systems over large document collections to power knowledge bases and Q&A bots.
• AI Workflow Automation : Design multi-step AI workflows combining business logic, API calls, and dynamic prompts for complex automation tasks.
• Production AI Monitoring : Continuously observe AI system behavior in production to detect issues, debug, and improve model quality and cost efficiency.

FAQs