About Vellum
Vellum is an AI product development platform (LLMOps) that helps engineering and product teams design AI workflows, evaluate them with datasets and custom metrics, deploy and version safely, monitor in production, and route requests across models for resilience. Founded in 2023 and backed by Y Combinator, Vellum raised a $20M Series A in 2025 to scale enterprise AI development.
Serving over 150 companies - from bleeding-edge startups to household names like Swisscom, Redfin, and Drata - Vellum combines a visual workflow canvas with Python/TypeScript SDKs so product managers can prototype alongside engineers using the same underlying logic.
Products & Services
Visual graph canvas to connect prompt nodes, tools/APIs, control flow, guardrails, and sub-workflows with support for loops, branching, and parallelism.
Intuitive prompt editor with comparison modes, A/B testing across models, version control, and one-click deployment to production endpoints.
Out-of-the-box metrics, LLM-based evaluations, and custom metrics via Python or TypeScript. Test against datasets before deploying to production.
One-click API endpoints with automatic version control, safe rollbacks, and release management for production LLM applications.
Full trace visualization, usage tracking, latency monitoring, and debugging console for production AI applications.
Built-in document retrieval, chunking, and embedding for RAG pipelines. Knowledge base supporting semantic search across your documents.
Vellum Integrations
Vellum integrates with major LLM providers and infrastructure tools across the AI stack:
OpenAI
Anthropic
Google AI
Cohere
Azure OpenAI
Fireworks
Perplexity
Cerebras
AWS Marketplace
GitHub
Customers & Case Studies
Top Customers
Redfin
Drata
Swisscom
Headspace
DeepScribe
Rentgrata
Left Field Labs
Rely Health
Customer Success Stories
Long-term customer developing high-impact AI solutions, prototyping fast and improving cross-team collaboration with Vellum workflows.
Rolled out “Ask Redfin” to millions of users across 14 markets, using thousands of test cases to evaluate their conversational agent.
Builds and secures 7,000+ isolated knowledge bases to drive compliant GRC automation across tenants.
Cut clinician note iteration time by 20–40% using feedback loops and regression testing for accuracy.
Made Vellum a core part of their AI platform, giving Swiss banks and governments a secure way to build AI applications.
Went from multi-engineer, multi-month builds to deploying healthcare workflows in days with voice agents and smart triage.
Case Studies by Industry
Pain Points & Solutions
Visual workflow builder and prompt-to-agent scaffolding let teams go from idea to production-ready AI applications in days instead of months. One EdTech company made development 10x faster.
Built-in evaluation framework with dataset testing, regression checks, and custom metrics ensures AI accuracy before deploying to production. DeepScribe cut note iteration time by 20–40%.
Product managers prototype in the visual interface while engineers work with the same logic in Python/TypeScript SDKs, bridging the gap between technical and non-technical team members.
Version control, safe rollbacks, and release management let teams deploy with confidence. One-click API endpoints with automatic versioning reduce deployment friction.
Model-agnostic platform supports OpenAI, Anthropic, Google, Cohere, and more. Teams can swap or route across models without rewriting application logic.
SOC 2 Type II, HIPAA compliance, VPC deployments, and BAAs make Vellum accessible to healthcare, finance, and government organizations like Swisscom.
How Vellum Looks on AI Platforms
Vellum's score is calculated based on: website structure and schema markup, content accessibility for LLMs, clarity of product/service descriptions, FAQ coverage and structured data, integration documentation, and pricing transparency. As a smaller, developer-focused platform, Vellum scores well on technical documentation but has room to grow in broader content coverage and brand visibility.
How accessible is Vellum?
Vellum's website provides strong technical documentation, an open-source LLM leaderboard that draws organic traffic, detailed blog posts on LLM development topics, and clear product descriptions. Their docs site (docs.vellum.ai) covers security, data privacy, and API references. However, as an earlier-stage company, overall content breadth is more limited compared to larger platforms.
How easy is it for LLMs to understand Vellum's mission?
Vellum's mission is well-communicated: help teams build, evaluate, and deploy production-ready AI applications with rigor and speed. The website consistently reinforces this with customer stories, product walkthroughs, and technical content that LLMs can parse effectively. Their blog and leaderboard content rank well in AI-related search queries.
Competitive Landscape
How Vellum differentiates in head-to-head matchups:
| Competitor | What Differentiates Vellum | How Vellum is Better |
|---|---|---|
| LangChain | Visual low-code builder + enterprise governance | Non-engineers can build alongside developers; no framework lock-in |
| Langfuse | All-in-one platform vs. observability-only | Combines orchestration, evals, and deployment in one tool instead of multiple |
| LangSmith | Model-agnostic and framework-independent | Works with any LLM provider; not tied to LangChain ecosystem |
| Weights & Biases | Purpose-built for LLM apps, not general ML | Workflow orchestration and production deployment built-in |
| Braintrust | Visual workflow builder and RAG support | Broader feature set beyond just evaluation and tracing |
| Humanloop | Enterprise compliance (SOC 2, HIPAA, VPC) | Stronger security posture for regulated industries |
| PromptLayer | Full lifecycle platform vs. prompt management | Goes beyond prompts to orchestration, eval, and monitoring |
Pricing
Startup
free forever
50 builder credits/month, 1 user seat, hosted agent apps, debugging console, 20-document knowledge base.
Pro
per month
5,000 daily prompt executions, 250 daily workflow executions, multiple seats, advanced evaluations.
Enterprise
annual billing
Custom credit bundles, VPC deployments, dedicated Slack support, onboarding, DPAs, BAAs, and custom contracts.
Security & Compliance
Vellum provides enterprise-grade security with SOC 2 Type II attestation and HIPAA compliance. All data is encrypted using AES-256 GCM encryption at rest and in transit. Enterprise customers can deploy in virtual private clouds and sign Business Associate Agreements for handling protected health information. Role-based access control and audit logs support governance requirements.
Strengths & Top Pros
- ✅ Visual workflow builder lets non-engineers prototype while developers use Python/TypeScript SDKs
- ✅ Model-agnostic: supports OpenAI, Anthropic, Google, Cohere, Azure, and more with easy switching
- ✅ Built-in evaluation framework with dataset testing and regression checks before production deployment
- ✅ Enterprise-ready: SOC 2 Type II, HIPAA, VPC deployments, and BAAs for regulated industries
- ✅ Y Combinator backed with $24.5M in funding and a responsive, customer-centric support team
- ✅ Real customer results: 10x faster development (EdTech), 20–40% faster clinical notes (DeepScribe)
- ✅ Free tier available for startups to get started with no cost barrier to entry
What People Say About Vellum
What Does Reddit Have to Say About Vellum
Reddit sentiment toward Vellum is generally positive among AI/ML practitioners who value the visual workflow builder and evaluation tools. Some users note the interface has a learning curve (described as "Excel meets TensorFlow"), but most agree the platform delivers real value once mastered. Developers appreciate the model-agnostic approach and responsive support team. Discussions often compare Vellum favorably to LangChain for production use cases.
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💬 Vellum AI for LLM development - anyone tried it?
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💬 Vellum vs LangChain for production LLM apps
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💬 Best LLMOps platforms for production - Vellum, Langfuse, or build your own?
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💬 Has anyone used Vellum's workflow builder for complex AI agents?
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💬 Evaluating LLM platforms for our startup - Vellum vs Humanloop vs LangSmith
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LangChain
Langfuse
Weights & Biases
Braintrust
Humanloop
PromptLayer
LangSmith