LLM development platform for prompt engineering, evaluation, workflow orchestration, and deployment of production AI applications. Helps engineering teams build, test, and ship LLM-powered features with version control and observability.
An LLM development platform that provides prompt engineering, evaluation, workflow building, and deployment tools for teams building production AI applications.
Vellum is a freemium LLM development platform — free for up to 100,000 monthly prompt executions, with Pro plans starting at $89/seat/month — that helps engineering teams build, test, evaluate, and deploy production AI applications with confidence. Used by over 200 companies including teams at Fortune 500 enterprises, Vellum has processed more than 500 million LLM API calls through its platform and supports integration with 50+ foundation models across providers like OpenAI, Anthropic, Google, Cohere, and Meta.
The platform provides a complete toolkit spanning prompt engineering with side-by-side model comparison, automated evaluation pipelines for regression testing across prompt versions, a visual workflow builder for chaining LLM calls and agentic pipelines, and version-controlled deployment management with rollback capabilities. Vellum's evaluation framework has been used to run over 10 million automated test executions, helping teams catch regressions before they reach production.
Teams start in the prompt engineering playground, where they can compare outputs across models from multiple providers, iterating on prompts with full version history. Once prompts perform well in testing, Vellum's evaluation framework lets teams define quantitative test suites that automatically score outputs using custom metrics, LLM-as-judge configurations, or deterministic checks — catching regressions before they reach users.
The workflow builder enables teams to construct complex multi-step AI pipelines visually — chaining LLM calls, adding conditional logic, integrating tool use, and building retrieval-augmented generation (RAG) flows with document management and search indexes. Over 15,000 workflows have been deployed through the platform. These workflows deploy through Vellum's managed infrastructure with environment management (staging and production), A/B testing, and monitoring.
In production, Vellum provides observability into latency, cost, and quality metrics, giving teams visibility into how their AI features perform with real users. The platform integrates into existing CI/CD pipelines via REST API and SDKs for Python and TypeScript, with the Python SDK averaging over 50,000 weekly downloads on PyPI. Multi-user workspaces with role-based access control support collaboration across engineering teams of all sizes.
Vellum is SOC 2 Type II certified and offers HIPAA compliance on enterprise plans, making it suitable for regulated industries including healthcare and financial services. The platform does not train on customer data and acts as a passthrough to model providers, addressing common enterprise data privacy concerns.
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Vellum's playground lets teams compare prompt outputs across multiple LLM providers (OpenAI, Anthropic, Google, and others) side by side. Every prompt iteration is version-controlled, allowing teams to track changes over time and revert when needed. Variables and templates make it easy to test prompts against diverse inputs systematically rather than ad hoc.
The evaluation system allows teams to define test suites with quantitative scoring criteria, then automatically run these suites against prompt changes to catch regressions before deployment. This shifts LLM quality assurance from manual spot-checking to systematic, repeatable testing integrated into the development workflow.
Vellum's workflow builder provides a visual interface for constructing complex multi-step AI pipelines. Teams can chain LLM calls, add conditional branching logic, integrate external tool use, and build retrieval-augmented generation flows — all without writing orchestration code. Workflows can be deployed and versioned like prompts.
Prompts and workflows deploy through Vellum's managed infrastructure with environment separation (staging, production), rollback capabilities, and A/B testing support. This gives teams the same deployment safety practices they use for application code, applied to their AI components.
Vellum provides document upload, indexing, and search capabilities for teams building retrieval-augmented generation applications. Teams can manage their knowledge bases, configure search parameters, and test retrieval quality alongside prompt performance in a unified platform.
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Vellum continues to develop its LLM development platform with enhancements to the workflow builder, evaluation framework, and deployment management capabilities. The platform supports the latest models from major providers and has expanded its enterprise compliance and security features.
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