Vellum AI
A low-code LLM workflow builder with prompt versioning evaluations and multi-environment deployment — built for teams that want to ship AI features without deep engineering overhead.
Vellum is an interesting case in the prompt management category. It's built specifically to lower the engineering overhead to build AI features and provide non-technical and semi-technical teams with a visual interface for building and deploying and testing LLM flows. They have a drag-and-drop workflow builder that can support multi-step prompt chains, agent flows, and conditional logic without having everyone need to write code. Vellum features a versioning system that records every change and can roll back and is deployable to any environment—production, staging, or development. You can A/B test prompts against each other without going through an engineering sprint to do it. The evaluation system allows you to run structured tests against versions of prompts based on custom metrics to ensure that your changes increase rather than degrade quality. In reviews, the consistent feedback we've seen is that Vellum will tremendously speed up the process from prompt ideation to shipped product, particularly for teams who used to file an engineering ticket for every small change. I'll give it one fair critique: pricing has been inconsistent across reviews. The "Pro" plan at $25/mo is publicly listed, but the "Business" plan doesn't show pricing and requires going through a sales call—it's possible Vellum's product messaging will evolve. We recommend Vellum for product teams and AI engineers who want to rapidly build and iterate complex LLM workflows and bring non-technical stakeholders into that process effectively, bridging a gap left in pure engineering platforms (like LangSmith) or by toolsets that lack the complexity (like PromptLayer).
- Category: Prompt Tools
- Pricing: Freemium
- Rating: 4.5 / 5 (0 reviews)
- Platforms: Web
Key features
- Visual workflow builder — Drag-and-drop environment for building multi-step LLM prompt chains agent flows and conditional logic without writing code for each component
- Prompt versioning — Track every prompt change with full history rollback capability and environment-specific deployment to development staging and production
- Multi-environment deployment — Deploy specific prompt versions to different environments with controlled release and rollback for production AI applications
- Evaluation framework — Define custom metrics and run structured tests against prompt versions to measure performance changes before deploying to production
- A/B testing — Compare prompt versions against each other in production with traffic splitting and metric tracking to identify which version performs better
- Collaboration tools — Product managers domain experts and engineers work in the same visual environment without requiring engineering-only workflows
- API and SDK integration — Connect deployed prompts to applications via API and language-specific SDKs for production integration
- Sandbox testing — Test prompts and workflows in an isolated sandbox environment before deploying changes to any production environment
Pros & Cons
Pros
- Low-code visual environment enables non-technical product managers and domain experts to participate in prompt iteration without engineering bottlenecks
- Multi-environment deployment with rollback gives engineering teams confidence when shipping prompt changes to production AI features
- A/B testing built into the platform removes the need for custom instrumentation to compare prompt versions against real production traffic
- Reviews consistently cite significant acceleration in time from prompt idea to shipped feature compared to manual engineering workflows
- Free plan available for initial exploration and the Pro plan at $25 per month is accessible for smaller teams starting with AI feature development
Cons
- Pricing transparency has been inconsistent with Business plan pricing requiring a sales conversation and some reviewers noting evolving product positioning
- Less suitable for pure research or exploration workflows where the structured environment and deployment overhead adds unnecessary complexity
- Smaller community and fewer public tutorials than LangSmith which makes finding workflow examples and best practices harder for new users
- Advanced features like custom evaluation metrics may require more technical setup than the low-code framing suggests for non-engineering users