Vellum
LLM development platform for prompt engineering, testing, and deployment.
Description
Short Intro: Vellum is a unified LLM development platform founded in January 2023 by Akash Sharma, Sidd Seethepalli, and Noa Flaherty, three engineers who built production LLM applications with GPT-3 at Dover starting in mid-2020, two years before ChatGPT. Coming out of Y Combinator's Winter 2023 batch and backed by $29.5M including a $20M Series A led by Leaders Fund in July 2025, Vellum gives engineering and product teams a shared environment to version prompts, orchestrate multi-step workflows, run evaluations, and monitor production LLM behavior without scattering that logic across application code.
Key Capabilities:
Prompt editor with version control and GitHub-style release management
Side-by-side model comparison across OpenAI, Anthropic, and other major providers
Workflow builder for chaining multiple LLM calls, logic, and data sources
Evaluation framework with quantitative metrics and custom scoring
Out-of-the-box RAG without additional backend infrastructure
Semantic search for injecting company-specific context into prompts
Stable API interface decoupling prompts from application code
Production monitoring and observability for deployed LLM workflows
No-code LLM builder for non-technical team members
Few-shot example management within the prompt editor
Deployment and rollback with performance monitoring for edge case detection
Python and TypeScript SDKs for programmatic integration
Alternative tools
- OpenAI Playground
Browser-based prompt iteration environment for the OpenAI API.
- PromptPerfect
Automated prompt optimization tool for text and image models — shutting down September 2026.
- PromptHub
Git-style version control and collaboration platform for LLM prompts.
- Agenta
MIT-licensed LLMOps platform for prompt engineering, evaluation, and tracing.
- Sourcegraph Cody
Enterprise AI coding assistant with multi-repository context retrieval
- Arize Phoenix
Trace every step your LLM agent takes, from prompt to response
