Alternatives guide

Best Vellum Alternatives

Updated Sep 14, 2026 3 alternatives compared

Vellum

The one you're replacing Freemium

AI product and engineering teams reach for Vellum to test, evaluate, deploy, and monitor LLM features without building the entire operating layer themselves.

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The essentials

All 3 at a glance

Vellum included for comparison — click any alternative to jump to its full breakdown below.

Alternative Pricing License Built for Founded Best for
Vellum Current $0.00Freemium Proprietary Startups Small Business 2023 The one you're replacing
Dify AI $59.00Freemium Open source Startups Enterprise 2023 Self-hosted LLM application development
Stack AI $0.00Freemium Proprietary Startups Enterprise 2022 Enterprise AI assistants and workflows
Relevance AI $19.00Freemium Proprietary Small Business Enterprise 2020 No-code business AI agents
The shortlist

Every alternative, ranked

Filter by pricing model or license — every entry is one we’d consider a real replacement.

Showing 3 alternatives

Stack AI

Freemium Proprietary

Turning company data and repetitive processes into working AI assistants is the job Stack AI handles best, using a visual builder that can still support production deployments.

Build visual workflows Grounded knowledge bases Deploy endpoints Evaluation controls
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Relevance AI

Freemium Proprietary

Operations, sales, and support teams reach for Relevance AI to build task-focused AI agents without developing an entire orchestration system from scratch.

Build task agents Coordinate agent teams Connect business systems Ground agent responses
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Before you switch

Should you replace Vellum?

Why people stay 5

  • Side-by-side model testing makes prompt and provider changes easier to judge against identical cases.
  • Evaluation datasets turn subjective prompt iteration into repeatable checks before release.
  • Hosted endpoints let teams update LLM behavior without redeploying application code each time.
  • The visual canvas gives product specialists a practical way to contribute to application logic.
  • Production traces identify the configuration and model behind an individual output.

Why people switch 4

  • The extra platform layer can feel excessive for products built around one or two straightforward prompts.
  • Visual workflow definitions are less natural to review and merge than code stored in Git.
  • Execution volume and underlying model usage can make total costs difficult to forecast.
  • Advanced governance, support, and scale requirements push teams toward higher-priced or negotiated plans.

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