Vellum AI is a collaborative platform for building, evaluating, and deploying prompt-based features using large language models. Built with software and product teams in mind, Vellum provides a centralized workspace to manage prompts, test performance across LLMs, monitor production usage, and ship improvements with confidence.
Instead of manually copy-pasting prompts into playgrounds or juggling multiple scripts, teams can use Vellum to version prompts, test them across model providers, track quality metrics, and roll out changes in a controlled, production-safe way. Vellum supports models from OpenAI, Anthropic, Cohere, Azure OpenAI, and more.
The platform is used by engineering, product, and data science teams to build LLM-based features for customer support, content generation, RAG pipelines, and more.
Features
Side-by-Side Prompt Evaluation
Test how different LLMs respond to the same prompt using structured comparison tools and feedback workflows.
Prompt Versioning and Change Management
Track iterations of prompts with clear version histories and rollback options to avoid regressions.
LLM Provider Abstraction
Use a unified interface to test and deploy across OpenAI, Anthropic, Cohere, Azure, and custom model providers.
Batch Testing
Evaluate prompts at scale using representative test datasets to measure performance before deployment.
A/B Testing in Production
Run experiments on different prompt versions in real applications to validate changes with real users.
Monitoring and Observability
Track latency, token usage, error rates, and other metrics across models and prompts in production.
Prompt Repositories
Organize prompts in workspaces, assign metadata, and control access across teams for better collaboration.
Prompt Chaining Support
Build workflows that involve multiple prompt steps (e.g., classification followed by generation).
Ground Truth Evaluation
Benchmark prompt outputs against labeled datasets using custom scoring functions and human feedback loops.
Security and Compliance
Role-based access control, audit logs, and SOC 2 compliance help support enterprise-grade security needs.
How It Works
Vellum simplifies LLMOps through a three-step workflow that unifies prompt development and deployment:
Author & Test Prompts
Use Vellum’s editor to write and test prompts across multiple providers. Define inputs and outputs, run comparisons, and tag good vs bad responses.Batch & Evaluation
Import test datasets (JSON or CSV) and evaluate your prompt logic at scale. Measure quality through automated metrics or manual review.Deploy & Monitor
Once validated, deploy the prompt via the Vellum API or SDK. Monitor usage, latency, and token costs in real time. Run A/B tests to optimize performance in production.
This modular, feedback-driven loop enables iterative improvement while maintaining full visibility and control.
Use Cases
Customer Support Automation
Fine-tune and test LLM prompts that generate helpful, safe, and on-brand support responses across different customer queries.
Content Generation
Test and deploy LLM prompts for creating blog posts, product descriptions, and emails using consistent tone and formatting.
Internal Knowledge Tools
Build and optimize retrieval-augmented generation (RAG) systems with prompts that summarize or extract key info from internal documents.
Classification and Tagging
Use prompts to categorize content, extract structured data, or route messages based on intent.
Chatbot Development
Test conversational flows and refine prompt logic for AI assistants with side-by-side comparison and live A/B testing.
Regulated Industries
Ensure model outputs meet legal, compliance, and tone guidelines through evaluation pipelines and monitoring.
Pricing
Vellum offers usage-based pricing and access tiers based on team size and requirements. While exact pricing is not publicly detailed on the website, their model includes:
Free Plan (for Testing and Evaluation)
Limited number of test runs
Access to prompt editor and evaluations
Ideal for small projects or initial trials
Team Plan (Contact Sales)
Unlimited test cases and API calls
Batch testing and version control
Team collaboration features
Email and chat support
Enterprise Plan (Custom Pricing)
Advanced observability and audit trails
SSO and role-based access
Custom integrations
Priority support and SLAs
SOC 2 and compliance features
To get exact pricing or book a demo, visit https://www.vellum.ai
Strengths
Purpose-Built for LLMOps
Vellum is tailored specifically for prompt lifecycle management, making it more focused than generic model evaluation tools.
Multi-Model Flexibility
Supports prompt testing and deployment across multiple LLM providers, enabling easy experimentation and provider switching.
Integrated Evaluation
Allows teams to measure prompt quality at scale using real data and human feedback, improving reliability.
Production Safety
Versioning, A/B testing, and monitoring ensure that changes to prompts don’t break real-world use cases.
Collaborative Workspace
Teams can work together in a shared UI with full audit history and access control, enabling better cross-functional alignment.
Strong Developer Experience
Includes a clean API, detailed docs, and integrations to plug into existing workflows.
Drawbacks
Not Ideal for Non-Technical Users
While intuitive for developers and product managers, Vellum may require some familiarity with LLM concepts to use effectively.
Focused on Prompt Management
Does not offer full-stack app building or hosting—designed to work alongside your existing infrastructure.
No Built-In Model Training
Vellum is not a training platform; it assumes you’re working with existing LLM APIs.
Pricing Not Transparent
Lack of publicly listed pricing may be a hurdle for individual developers or early-stage startups.
Still Evolving
As LLMOps is a new field, some advanced features (like plugin support or in-editor RAG design) may still be under development.
Comparison with Other Tools
Vellum AI vs OpenAI Playground
OpenAI Playground is good for quick tests. Vellum offers structured workflows, version control, and multi-provider support.
Vellum AI vs PromptLayer
PromptLayer offers prompt observability. Vellum expands on that with side-by-side testing, A/B testing, and version management.
Vellum AI vs LangChain
LangChain is a framework for chaining prompts and tools. Vellum complements it with prompt evaluation, monitoring, and deployment capabilities.
Vellum AI vs Weights & Biases
W&B tracks ML experiments. Vellum is specific to LLM workflows and prompt-centric use cases, providing better tooling for prompt testing.
Vellum AI vs Replit or Notion AI
Replit and Notion AI offer general-purpose AI integrations. Vellum is built for teams creating and managing LLM-driven features.
Customer Reviews and Testimonials
While Vellum AI is still expanding in visibility, early feedback from engineering and product teams has been positive:
“Vellum has saved us countless hours iterating on prompts. We can test across models and ship changes with confidence.”
— Senior Engineer, B2B SaaS Company
“It’s like Git for prompts. We finally have a way to version and test before pushing to production.”
— Product Manager, AI Startup
“Our support chatbot used to break every week. Now we can monitor and refine prompts continuously with Vellum.”
— Head of Engineering, Customer Experience Platform
“The side-by-side comparisons helped us understand which LLM provider performed better for our use case.”
— Data Scientist, Fintech Company
Conclusion
Vellum AI is a game-changing tool for any team building LLM-powered products. With its focus on prompt management, evaluation, and deployment, it provides the infrastructure needed to scale AI features without sacrificing quality, control, or speed.
Whether you’re building a chatbot, automating support, or experimenting with content generation, Vellum offers the prompt infrastructure layer your team needs to iterate faster and deploy more reliably.
To learn more or request a demo, visit https://www.vellum.ai
