Vellum vs. Stack AI

Vellum and Stack AI serve different roles in AI development, each catering to specific needs. Vellum focuses on prompt management, providing a low-code solution for refining AI-generated interactions. Stack AI, on the other hand, is designed for integrating AI into business operations, streamlining automation and workflow efficiency. While both platforms offer valuable capabilities, they also come with limitations that may require additional integrations to achieve a more complete AI solution.

Sandgarden presents a stronger alternative by combining the strengths of both Vellum and Stack AI while addressing their shortcomings. It offers a more comprehensive AI development environment that enhances flexibility, scalability, and security. This comparison will explore how Vellum and Stack AI compare while introducing a solution that better supports long-term AI growth and efficiency.

Vellum’s AI prompt refinement versus Stack AI’s business process automation tools.

Feature Comparison

Prompt Management

LLM Evaluation

Version Control

Analytics

Tracing

Metrics

Logging

API First

Self-Hosted

On-Prem Deployment

Dedicated Infrastructure

Access Control

SSO

Data Encryption

Vellum

Vellum offers a visual interface to generate AI workflows simply without extensive experience with LLMs. This allows engineering and product teams to effectively collaborate on delivering AI solutions for various business needs.

Vellum excels in simplifying the basic processes for working with LLMs. Prompt engineering, semantic search, prompt chaining, and RAG are basic tools useful to any business looking to experiment with AI. Ease of use is augmented by thorough documentation and tutorials, further enabling users of various abilities to contribute to a company’s AI initiatives.

That said, Vellum is not without its drawbacks:

Less capable with complex implementations
Limited flexibility and control over underlying infrastructure
Hosted deployment options only

View more Vellum alternatives

Stack AI

Stack AI offers a UI that allows users to generate AI agents with simple drag-and-drop functionality. This lets technical and non-technical users alike deliver AI solutions for various business needs. The platform’s low-code approach democratizes AI development, facilitating its use across the organization.

Stack AI’s strength is its extensive library of pre-built templates based on common use cases. Whether it’s a chatbot, back office automation, or a basic RAG tool, AI based solutions are just clicks away. The platform is augmented by a responsive support team, further enabling users of various abilities to contribute to a company’s AI initiatives.

That said, Stack AI is not without its drawbacks:

Reliance on pre-built templates restricts use cases
Limited customization hinders specialization
Limited scalability for large-scale operations

View more Stack AI alternatives

Sandgarden

Sandgarden provides production-ready infrastructure by automatically crafting the pipeline of tools and processes needed to experiment with AI. This helps businesses move from test to production without figuring out how to deploy, monitor, and scale the stack.

With Sandgarden you get an enterprise AI runtime engine that lets you stand up a test, refine and iterate, all in support of determining how to accelerate your business processes quickly. Time to value is their ethos and as such the platform is freely available to try without going through a sales process.

Conclusion

Vellum and Stack AI each provide valuable tools for AI development, but they lack the depth and flexibility needed for a complete, scalable solution. Vellum focuses on simplifying prompt management with a low-code interface, making it accessible for teams experimenting with AI-driven workflows. However, it falls short in areas like advanced analytics, version control, and enterprise-grade security, limiting its usability for teams that require more control over their development pipeline. Stack AI, on the other hand, is designed for integrating AI into business processes but does not offer the structured prompt management, detailed logging, or real-time model evaluation necessary for a robust AI infrastructure.

Unlike these two platforms, Sandgarden provides a fully unified development environment that allows teams to manage AI models efficiently while ensuring security and scalability. Its built-in analytics and structured logging give teams greater insight into model performance, while enterprise-grade encryption and flexible deployment options provide a secure and seamless experience. With an API-first approach and a feature set that eliminates the need for external integrations, Sandgarden offers the most comprehensive and future-proof solution for AI development.