Vellum

Operator's take

Most teams discover the same problem around the same point: the AI demo works great, and then the moment you try to ship it, everything gets complicated. Prompt changes break things in ways you didn't anticipate. You don't know which model is actually performing better. QA is a vibes check. Vellum is built for exactly that inflection point — the moment your team is serious about AI but hasn't yet figured out how to operate it like a real product.

The visual workflow builder is the entry point: connect RAG pipelines, branching logic, and tool integrations without writing infrastructure code. But the more interesting piece is evaluation. Vellum lets you run AI workflows against datasets and custom metrics before you deploy — so when you update a prompt, you actually know whether the change made things better or worse across a representative set of scenarios, not just the three examples you were looking at. That discipline is what separates teams that build reliable AI products from teams that are perpetually firefighting.

The honest tradeoff: Vellum is aimed at product teams and data science teams shipping real AI features, not at solo operators building light automation. If your use case is connecting a few tools together or summarizing weekly reports, this is more infrastructure than you need. Pricing is tiered with an Enterprise plan (SOC 2 Type 2 and HIPAA compliant, with configurable data retention) — but no public pricing page exists for the orchestration product, so plan names, quotas, and dollar amounts need to come straight from Vellum; any specific figures you see quoted elsewhere can't be confirmed against anything live today. And you'll still need to build your own UI layer; Vellum handles orchestration and observability, not the front-end.

What it's good at

• Visual workflow and agent builder — construct multi-step LLM pipelines and agentic systems with drag-and-drop, including branching logic, RAG, and tool integrations, without writing orchestration code.
• Code-first option alongside the visual builder — a Python and TypeScript Workflows SDK lets developers define workflows, nodes, and control flow in code (with a dedicated code-first quickstart), so teams that want source-controlled orchestration aren't forced into the visual editor.
• Automated evaluation — test workflows against custom metrics and diverse datasets before deployment, so model or prompt changes get validated rather than just guessed.
• Prompt management and versioning — centralize prompts with version control, run A/B comparisons across model and prompt combinations, and track what's live.
• Observability dashboard — real-time monitoring of success rates, latency, and error patterns across deployed workflows; find what's breaking and why.
• Flexible deployment via API/SDK — push workflows to production through a REST API or SDKs; integrate Vellum-powered features into existing apps without a full redeploy cycle.
• Handles the prototype-to-production jump — the combination of evaluation, versioning, and monitoring is specifically designed for teams that have a working demo and need it to hold up in the real world.

What it's not

• Not for solo operators or light automation — the feature set and pricing ladder assume a product team or data science team; if you just need to wire up a few tools, look at simpler orchestration options.
• Not a front-end or app builder — Vellum handles the AI pipeline side; you still need to build the user-facing layer yourself.
• Not self-hosted infrastructure — the orchestration platform is hosted SaaS (AES-256 encryption at rest, TLS in transit, RBAC, optional HMAC auth); no self-hosted or on-prem option appears in the docs, so teams with strict data-residency or air-gap requirements will need to confirm what Enterprise accommodations exist before assuming a fit.