Vellum is an LLM engineering platform that addresses a gap in the AI development stack: the tooling between raw model APIs and production deployment. While frameworks like LangChain handle orchestration and platforms like AWS Bedrock handle infrastructure, Vellum focuses on the engineering workflow — helping teams build, test, version, and monitor LLM applications with the same rigor applied to traditional software development.
Founded in 2022, Vellum has positioned itself as the platform for teams who have moved past prototyping and are dealing with the hard problems of production AI: inconsistent outputs, prompt regression, quality monitoring, and iterating on live applications without breaking what works.
Key Features#
Workflow Builder Vellum's workflow editor is a node-based canvas for building multi-step LLM pipelines. Nodes include LLM prompt nodes, code execution nodes, conditional branches, API call nodes, and subworkflow references. The builder targets developers — it's less visual-friendly than Flowise but more powerful for complex logic and better integrated with Vellum's testing and deployment features.
Prompt Management and Versioning Vellum treats prompts as first-class versioned artifacts. Every change to a prompt creates a new version with a full diff, rollback capability, and the ability to A/B test competing versions against production traffic. This discipline around prompt versioning is a critical differentiator for teams maintaining production AI applications.
Evaluation Suites Vellum's evaluation system lets you define test cases — input/expected output pairs — and run them automatically whenever you change a prompt or workflow. Custom evaluators (using LLMs as judges, regex patterns, or custom Python functions) score outputs on dimensions like accuracy, tone, and completeness. Running evals before deploying changes prevents prompt regressions from reaching users.
Deployment and API Management Vellum exposes prompts and workflows as versioned API endpoints. You can promote specific versions to production, canary-deploy new versions to a subset of traffic, and roll back instantly if quality drops. This deployment model brings software engineering best practices to AI application management.
Monitoring and Observability The production monitoring dashboard tracks usage, latency, token costs, and custom quality metrics across deployed workflows. You can inspect individual execution traces, tag problematic outputs for review, and create curated datasets from production traffic for future evaluation.
Document and Knowledge Management Vellum includes document upload, embedding, and retrieval capabilities for RAG-based applications. While not as deep as dedicated vector database tooling, the integrated knowledge management reduces the number of systems teams need to manage for straightforward RAG use cases.
Pricing#
- Starter: Free — access to core features, limited workflow runs, community support
- Pro (~$499/month): Full workflow capabilities, unlimited prompt versions, evaluation suites, production monitoring
- Growth (~$1,499/month): Higher execution limits, priority support, SSO, audit logs
- Enterprise: Custom pricing with SLA, dedicated support, data residency options
Vellum's pricing targets engineering teams at companies with production AI products, not individual developers. The Starter plan is genuinely usable for evaluation and prototyping; the paid plans are for production workloads.
Who It's For#
Vellum is the right choice for:
- Product engineering teams at AI-powered startups or scaleups with production LLM applications
- AI platform engineers responsible for the reliability and quality of LLM applications used by customers
- ML engineers who need structured tooling for prompt iteration, evaluation, and deployment
- Teams scaling from prototype to production who need the engineering practices to prevent regressions as they iterate
It is not the right fit for non-technical teams wanting no-code agent builders, for individual developers building personal projects (cost is prohibitive), or for teams whose primary need is infrastructure (Vellum is application-layer tooling, not cloud infrastructure).
Strengths#
Prompt lifecycle management. Vellum's versioning, A/B testing, and rollback capabilities bring software engineering discipline to prompt management. This is rare and genuinely valuable for teams maintaining production applications.
Evaluation-first philosophy. The emphasis on running systematic tests before deploying prompt changes addresses one of the most common production failure modes in LLM applications — unintended regressions from prompt edits.
Unified platform. Having workflow building, prompt management, evaluation, deployment, and monitoring in one product reduces the integration overhead of stitching together separate tools (e.g., LangChain + LangSmith + custom deployment scripts).
Developer-oriented UX. Unlike some AI platforms that optimize for the demo rather than the workflow, Vellum's interface reflects the needs of engineers working with production systems daily.
Limitations#
Price point excludes early-stage teams. The $499/month Pro plan is reasonable for a funded startup or enterprise team but excludes solo developers and early-stage projects that could benefit from the tooling.
Not a full MLOps platform. Vellum covers the LLM application layer well but doesn't address model training, fine-tuning infrastructure, or data pipeline management. Teams needing full-stack MLOps must supplement Vellum with other tooling.
Smaller community and ecosystem. Compared to LangChain or open-source alternatives, Vellum has a smaller community, fewer public tutorials, and less third-party integration documentation.
Related Resources#
Explore the full AI Agent Tools Directory for developer-oriented platform options.
Related profiles: LangChain for the underlying framework and LangGraph for graph-based agent orchestration.
Comparisons: Vellum vs LangSmith: LLM Observability and Engineering Platform Comparison and Vellum vs Weights and Biases: AI Experiment Tracking Comparison.
For implementation context, see LLM Application Development Best Practices: Prompt Engineering and Evaluation and AI Agent Quality Assurance: Testing and Evaluation Strategies.