Enterprise platform for building, testing, deploying, and monitoring LLM-powered applications with prompt engineering, evaluation pipelines, and workflow orchestration.
Enterprise platform for building, testing, deploying, and monitoring LLM-powered applications with prompt engineering, evaluation pipelines, and workflow orchestration.
Vellum is a freemium AI development platform in the LLM ops category that enables engineering and product teams to build, evaluate, and deploy production-grade AI applications, with pricing starting free on the Develop tier and scaling to the Scale tier for production workloads and custom Enterprise pricing for compliance-heavy organizations. It is designed for mid-size to enterprise engineering teams that need a structured workflow around prompt engineering, evaluation, and deployment rather than ad hoc scripting.
The platform's core value proposition centers on three pillars: a visual workflow editor for building multi-step LLM pipelines without orchestration boilerplate, an automated evaluation and regression testing framework that catches quality issues before they reach production, and a model-agnostic architecture supporting over 50 LLMs across providers like OpenAI, Anthropic, Google, Cohere, and Meta. This combination means teams can iterate on prompts, swap models, and measure output quality within a single platform rather than stitching together separate tools for each concern.
Vellum also provides an integrated retrieval-augmented generation (RAG) pipeline that handles document ingestion, chunking, embedding, and semantic search, eliminating the need for teams to separately manage vector databases and processing infrastructure. For production deployment, the platform offers versioned API endpoints with zero-downtime rollouts, A/B testing for prompt variants, real-time monitoring dashboards tracking latency and cost, and instant rollback capabilities.
On the compliance and collaboration front, Vellum is SOC 2 Type II certified and provides SSO, role-based access controls, approval workflows, and audit trails—features that have made it a fit for regulated industries including fintech, healthcare, and legal tech. The platform supports shared workspaces where product managers, data scientists, and engineers can collaborate on prompt optimization using the visual editor without requiring code deployments.
As of early 2026, Vellum reports over 200 enterprise customers and processes millions of LLM API calls monthly through its gateway. The platform has been adopted by teams at companies ranging from Series A startups to Fortune 500 enterprises, with particular traction in financial services and healthcare where auditability requirements make ad hoc prompt management impractical. Independent reviews on G2 rate the platform 4.6 out of 5 stars based on user feedback highlighting the evaluation framework and workflow builder as standout features.
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A drag-and-drop canvas for building multi-step LLM pipelines with branching logic, conditional routing, tool use, and RAG integration. Teams can chain prompts, API calls, and code execution nodes without writing orchestration boilerplate. The editor supports real-time collaboration and version history, making it practical for teams iterating on complex pipelines. Each node can be individually tested and debugged, and the visual representation helps non-engineering stakeholders understand and contribute to pipeline design.
Provides automated evaluation pipelines that let teams define custom scoring functions, use LLM-as-judge evaluators, and run side-by-side comparisons of prompt variants. Regression tests can be triggered on every prompt change to catch quality degradations before deployment. The framework tracks quality metrics over time, giving teams quantitative evidence for prompt optimization decisions. This evaluation-driven approach reduces the risk of shipping degraded outputs and provides an audit trail of quality measurements across releases.
Supports 50+ LLMs from providers including OpenAI, Anthropic, Google, Cohere, Meta, and various open-source models through a unified API layer. Teams can swap models at the configuration level without code changes, enabling cost optimization and risk mitigation against provider outages. This also simplifies benchmarking new models against existing production prompts, allowing teams to evaluate whether newer or cheaper models meet their quality thresholds before committing to a migration.
Handles the full retrieval-augmented generation workflow: document ingestion from multiple formats, configurable chunking strategies, embedding generation, and semantic search indexing. Teams can build and iterate on knowledge-base-powered applications directly within the platform rather than stitching together separate vector databases and processing pipelines. The integrated approach means retrieval parameters can be tuned alongside prompt parameters in a unified evaluation loop, leading to faster iteration on RAG quality.
Offers versioned API endpoints with zero-downtime rollouts, A/B testing for prompt variants, and real-time dashboards tracking latency, cost, error rates, and output quality. Rollback capabilities let teams instantly revert to previous prompt or workflow versions if issues arise in production, reducing the blast radius of changes to LLM-powered features. The monitoring layer integrates with the evaluation framework, so teams can set alerts on quality metric thresholds and catch production degradation early.
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In early 2026, Vellum expanded its model gateway to support over 50 LLMs including the latest models from major providers. The platform introduced enhanced workflow editor features with improved real-time collaboration, more granular evaluation metrics for regression testing, and expanded RAG pipeline capabilities with additional document format support. Enterprise customers gained access to improved audit trail reporting and more flexible role-based access control configurations.
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