Provides a collaborative platform for building, deploying, and monitoring large language model (LLM) applications, integrating tools for experimentation, evaluation, and regression testing. It streamlines AI development by enabling rapid iteration, fine-grained release management, and production-level observability, reducing deployment timelines and improving system reliability.
Funding
$20M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
+3Founders
Product
Problem
Developing and deploying large language model (LLM) applications requires extensive experimentation, evaluation, and monitoring, leading to slow iteration cycles and increased deployment timelines. Existing AI development workflows often lack the systematic testing, version control, and observability found in traditional software development.
Solution
Vellum is a collaborative platform designed to streamline the entire lifecycle of LLM application development, from initial experimentation to production deployment and ongoing monitoring. The platform provides tools for rapid iteration on prompts, models, architectures, and retrieval-augmented generation (RAG) techniques. It facilitates data-driven experimentation and regression testing to optimize quality, cost, and latency. Vellum also offers detailed logging, end-user feedback capture, and fine-grained release management, enabling teams to implement software development best practices for AI.
Target Audience
Vellum is designed for AI developers, product managers, and startup founders building and deploying LLM-powered applications, as well as enterprise teams seeking to scale their AI development efforts.
Features
- Integrated development environment (IDE) for building AI workflows, from basic prompts to complex agentic systems
- Experimentation tools for rapidly testing different prompts, models, and RAG configurations
- Evaluation framework for applying test-driven development to optimize AI system performance
- Deployment capabilities with detailed logs and end-user feedback capture
- Monitoring tools for measuring production quality and detecting issues
- Version control, CI/CD, and observability features for AI applications
- TypeScript SDK and React chatbox plugin for rapid integration
- Role-based access control