Vellum

Overview

Vellum AI is a development platform designed to help teams build, test, and deploy applications powered by large language models (LLMs). According to its official website, Vellum is positioned as “Your Personal Intelligence,” emphasizing its role in enabling users to create intelligent, AI-driven solutions that align with their specific workflows. The platform abstracts away much of the complexity of working with multiple LLM providers, offering a unified environment for prompt experimentation, model selection, and production deployment.

At its core, Vellum AI provides a comprehensive suite of tools for managing the entire lifecycle of LLM-powered applications. Users can design and version prompts, compare outputs across different models side by side, and run automated evaluations to ensure quality and reliability. The platform also supports the construction of multi-step workflows and orchestration chains, allowing developers to combine model calls with business logic. These capabilities are essential for building robust AI features such as retrieval-augmented generation (RAG), fine-tuning pipelines, and complex agent-based systems.

From an operational standpoint, Vellum AI offers deployment and monitoring features that help teams ship models to production with confidence. It integrates with popular model providers and cloud infrastructure, enabling smooth transitions from experimentation to real-world use. The platform is designed for teams that need to maintain high standards of accuracy, traceability, and cost efficiency across their AI projects. Whether you are a solo developer or a large enterprise, Vellum AI aims to streamline the path from idea to production.

In summary, Vellum AI is a practical solution for anyone looking to harness the power of LLMs. It combines prompt engineering, evaluation, orchestration, and deployment into one cohesive platform. While the official site highlights the concept of “personal intelligence,” the underlying technology is a full-fledged AI development environment suitable for a wide range of use cases, from conversational assistants to knowledge management systems.

Core capabilities

Prompt Management

Create, version, and refine prompts in a centralized workspace with full history tracking.

Model Comparison

Run side-by-side evaluations of different LLMs to select the best performer for your task.

Automated Evaluation

Define test suites and use objective metrics to automatically assess output quality and consistency.

Workflow Orchestration

Build multi-step AI workflows that connect model calls with data processing and external APIs.

Deployment Tooling

Publish models to production with minimal friction, including API endpoints and integration hooks.

Monitoring and Analytics

Track live performance, latency, token usage, and cost to optimize operations.

RAG Integration

Support for retrieval-augmented generation pipelines that ground responses in your own knowledge base.

Fine-tuning Support

Prepare datasets and fine-tune models to improve accuracy on domain-specific tasks.

Use cases

Building conversational assistants and chatbots

Automating content generation for marketing or documentation

Developing retrieval-augmented question-answering systems

Creating code generation and code review tools

Constructing data extraction and summarization pipelines

Powering intelligent search and knowledge management

Rapidly prototyping and A/B testing LLM-based features