Vellum

Vellum is a platform for building and deploying production AI workflows. Where most frameworks hand you a set of primitives and let you assemble something, Vellum gives you a full environment: a visual workflow designer, prompt management with versioning, built-in evals, deployment controls, and team collaboration tools. The platform targets engineering teams that need to move fast but can't afford to rebuild infrastructure every time they ship a new workflow. The tradeoff is real: Vellum is opinionated, relatively expensive, and asks for adoption rather than integration. Whether that's a fair deal depends entirely on what your team would have built otherwise.

Most teams building AI features in 2026 have already learned the hard way that gluing an LLM to your product is the easy part. The hard part is managing what happens to your prompts after you ship, testing whether a new model version breaks existing workflows, giving non-technical colleagues a way to review outputs without setting up their own dev environment, and tracking down why a specific user's request produced a bad result three days ago. That operational surface is unglamorous and expensive to build, and it's exactly where Vellum has positioned itself.

Vellum is a platform for teams that want to skip building AI infrastructure and get straight to shipping workflows. It bundles a visual workflow designer, prompt management with versioning, a built-in evaluation suite, and production deployment controls into a single environment. The pitch is that the engineering hours you'd spend assembling those capabilities from separate tools or building them in-house cost more than the platform fee. That argument holds in more cases than skeptics expect.

This review is for teams evaluating whether Vellum's opinionated platform approach is the right fit, or whether a code-first framework like LangGraph covers enough ground to not need it.

Visual workflow designer

The centerpiece of Vellum is a drag-and-drop workflow builder that lets you connect nodes representing LLM calls, search queries, code execution, conditional logic, and external API calls. You design the workflow visually, configure each node, and Vellum handles the execution infrastructure underneath.

The designer works well for the workflows it was designed for. Linear pipelines with a retrieval step followed by a generation step are fast to build. Branching on model output, adding error handling, or running parallel branches is supported through dedicated node types. The canvas gives non-developers enough visibility to participate in workflow design conversations, which matters more than it sounds for teams where product managers or ML engineers are involved in prompt decisions.

The ceiling shows up when workflows need genuinely custom behavior that Vellum's node types don't cover. Code execution nodes provide an escape hatch for arbitrary Python, but you're still constrained by what the platform allows to run. Teams coming from LangGraph will notice that expressing complex graph topologies in Vellum's GUI is less flexible than doing it in code, even if the visual representation makes simpler cases easier.

The SDK path partially addresses this. Vellum provides Python and TypeScript SDKs that let you define workflows in code and sync them to the UI for visualization and team review. That bidirectional sync is genuinely useful: developers write code, teammates review the graph view, and the canonical definition stays in version control.

Prompt management and versioning

Prompt management is where Vellum does some of its best work. The platform stores every version of every prompt, lets you tag releases, roll back to previous versions, and compare two versions side by side in the Sandbox before pushing anything to production.

The workflow for iterating on a production prompt is more deliberate than editing a string in code. You open the prompt in the Sandbox, run it against a set of test cases, check the outputs, tag the version when you're satisfied, and promote it. That process forces a review step that purely code-based workflows often skip, and the diff view between versions makes it easy to see exactly what changed.

Teams that have had a production prompt break silently after an edit tend to appreciate this enough to pay for it. The version history also makes auditing straightforward: if a customer reports a bad output from last Tuesday, you can pull up which prompt version was active at that time and replay the request against it.

The system handles multi-model setups reasonably well. You can maintain separate prompt variants for different models and switch the active variant at the deployment level without touching the workflow definition.

Built-in evals and testing

Vellum ships a full evaluation suite rather than requiring teams to wire one up externally. The platform provides dozens of premade metrics covering areas like response quality, factual accuracy, and format adherence, alongside custom metric creation for domain-specific needs.

Two evaluation modes cover different stages of development. Offline evals run a batch of test cases through a workflow version before you promote it, which serves as a gate between development and deployment. Online evals run continuously against live traffic, so you get signal on production performance over time rather than only at release time.

The eval tooling integrates with the prompt versioning system, so you can compare eval scores across prompt versions to see whether a change improved things. That feedback loop between iteration and measurement is the kind of thing that teams running separate eval harnesses have to build themselves, and it's a genuine time saver.

The limitation is that the eval framework is opinionated. If your quality bar requires highly custom evaluation logic or depends on tooling you've already built internally, adapting it to Vellum's eval system may cost more integration time than it saves. Teams with mature eval pipelines in tools like LangSmith or Weights and Biases will want to check what migration looks like before committing.

Production deployment

Vellum treats deployment as a first-class concern rather than an afterthought. The platform lets you deploy specific workflow versions, run A/B tests between variants, and manage rollouts without touching application code. For teams that iterate frequently on prompts or workflow logic, that release management layer removes a lot of coordination overhead.

A/B testing at the workflow level is one of the more distinctive features. You can split traffic between two workflow variants, run both in production simultaneously, and compare performance metrics before deciding which to promote. Most teams building this capability themselves spend weeks on it; Vellum ships it in the base platform.

The monitoring side integrates with Datadog and supports webhook-based event streams, so you can route production telemetry to whatever observability stack your team already uses. Cost tracking per workflow run is built in, which matters when you're running high-volume production workloads and need to understand where spend is going.

Self-hosted deployment is available for teams with data residency or regulatory requirements. That option adds operational complexity, but it means Vellum isn't automatically off the table for organizations that can't send data to a third-party hosted service.

Team collaboration

Vellum's design assumes multiple people are involved in AI workflow development, not just the engineer who built the first prototype. The platform provides role-based access control, review workflows for prompt changes, and shared workspaces where technical and non-technical teammates can inspect the same workflow.

The practical value shows up in teams where product managers write initial prompt drafts, engineers refine them, and ML engineers own the evaluation criteria. Vellum gives all three roles a place to work without everyone needing to read Python source files or understand the underlying framework.

The collaboration features also extend to external stakeholders. If your organization has compliance or legal review requirements for AI outputs, Vellum's review workflow can be configured to require approvals before a prompt version goes live. That kind of audit trail is hard to fake with a code-first framework and usually requires custom tooling to implement properly.

Who reaches for Vellum

The typical Vellum customer is a product team that has already shipped a first AI feature, hit the limits of what a quick prototype handles, and decided that building prompt management and deployment infrastructure themselves is not a good use of engineering time. The platform makes most sense for teams with more than one person touching AI workflows, at least some non-technical stakeholders who need visibility into what the system is doing, and a product that needs to iterate on prompts regularly.

Teams at the very early stage, where a single developer is still figuring out whether an AI feature is worth building, will find the price hard to justify. Open-source alternatives like LangGraph or even lighter workflow tools like Gumloop cover more ground than you'd expect at zero cost, and the infrastructure debt only becomes a real burden once you're operating at volume.

The other group that fits Vellum well is enterprises with data requirements that push them toward a self-hosted deployment model. The self-hosted option is unusual for a platform at this price point, and it opens Vellum to regulated industries that can't use fully managed AI tooling.

Where the platform lock-in shows up

Platform lock-in is the honest concern with Vellum, and it's worth naming directly. Workflows defined in Vellum's GUI don't export to an open format that another framework can consume. Prompt versions are stored in Vellum's system, not in your repository. Eval results live in Vellum's database. The SDK helps by letting you define workflows in code, but the execution environment is still Vellum's platform.

This matters in two ways. First, if Vellum raises prices significantly or changes the product direction, migrating is a real project rather than a configuration change. Second, if you've built complex workflows in the UI, the path to a code-first framework like LangGraph involves rebuilding rather than exporting.

The counter-argument is that lock-in is the wrong frame. Teams using Vercel to host a Next.js app, or Supabase for their database, accept similar dependencies because the time savings justify them. Whether the same logic applies to Vellum comes down to the specific team and the specific alternative: how much would you actually spend building prompt management, versioning, and evals yourself, and does the monthly fee come out ahead? For teams that have done that math honestly, Vellum often wins.

Vellum vs. LangChain and LangGraph

LangChain and LangGraph are libraries that give you primitives to build with. Vellum is a platform that gives you an environment to operate in. The comparison is less about which tool has better APIs and more about whether you want to own the infrastructure layer.

LangGraph is the best open-source alternative for teams that need stateful, production-grade workflow orchestration and are willing to build observability, prompt management, and deployment tooling around it. The framework itself is excellent. The surrounding work is real and non-trivial.

Vellum's answer to that is that most teams underestimate how much time the surrounding work takes. If the internal estimate for building a prompt management system is one sprint and it turns into three months of maintenance, the platform fee becomes a different conversation.

For teams that want a managed AI workflow environment and aren't building something that requires deep customization at the framework level, Vellum is a serious option. For teams where control and flexibility matter more than operational convenience, the open-source path with LangGraph is probably the right choice. The two are not trying to be the same thing, and the honest answer to "which should I use" is almost always a question about your team's priorities rather than a verdict on the technology.

If you're evaluating AI coding agents as a separate use case, the best AI agent for coding covers that space in more detail.

Key features

• Visual drag-and-drop workflow designer with no-code and code-first paths
• Prompt management with versioning, tagging, and team review workflows
• Built-in evaluation suite with premade and custom metrics
• Production deployment with A/B testing and release management
• RAG support with search nodes and document ingestion
• Python, TypeScript, and Go SDK for code-first workflow definition
• Datadog and webhook integrations for monitoring
• Role-based access control and self-hosted deployment option