Vellum

AI product development platform for prompt engineering, testing, and LLM deployment

Vellum is a New York-based AI product development platform that provides the tooling layer engineering teams need to build reliable LLM-powered features and deploy them to production with confidence — covering prompt management, evaluation frameworks, model comparison, workflow building, and production monitoring. It addresses a specific gap: moving from a working LLM prototype to a production-grade feature that behaves consistently, can be updated safely, and can be monitored over time. Vellum targets engineering teams at SMBs and mid-market companies building AI features inside products — not the AI/ML researchers building foundation models, but the product engineers shipping customer-facing AI capabilities. It has raised $5M+ in seed funding with backing from Susa Ventures and others.

Compliance

GDPR SOC 2

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Key Features

Prompt management and versioning: A centralized system for writing, versioning, testing, and deploying prompts — allowing teams to update production prompts without code deploys and track which version of a prompt is live.
LLM evaluation framework: Structured evaluation harnesses for testing prompt and model changes against defined datasets before pushing to production — measuring output quality, regression, and behavioral changes systematically.
Model comparison and A/B testing: Side-by-side comparison of outputs from different models and prompt variants on the same inputs, helping teams select the best model-prompt combination for each use case.
AI workflow builder: A visual tool for chaining LLM calls, retrieval steps, and logic into multi-step AI workflows that can be built and iterated on without code.
Production monitoring: Logging and observability for live LLM calls in production, tracking quality scores, latency, and cost over time — surfacing regressions before they compound.
Multi-model integration: Connects to all major LLM providers, allowing teams to work across models from a single interface.

Use Cases

For engineering teams managing multiple LLM features: A product engineering team uses Vellum to manage prompts, run evaluations, and monitor outputs across ten different AI features in a single platform rather than maintaining scattered scripts and spreadsheets.
For teams migrating models or prompts safely: An engineering team switching from one LLM provider to another uses Vellum’s evaluation harness to confirm the new model matches or exceeds existing quality on production-representative test cases before cutover.
For product teams iterating on prompts without code deploys: A PM or AI product engineer uses Vellum’s prompt management layer to test and deploy prompt changes directly, without needing an engineering sprint for each iteration.

Pricing MODELS

Freemium

Pricing Summary

Freemium model. Free tier for individuals and small teams. Starter at $200/month. Enterprise is custom-quoted with expanded usage, SSO, and dedicated support.