Vellum vs. Haystack

Vellum and Haystack cater to different areas of AI development, each offering specialized functionality. Vellum provides a low-code solution for prompt management, helping teams refine and optimize AI-generated responses. Haystack, meanwhile, is designed for search and retrieval-based AI applications, making it a strong option for handling large-scale queries and document indexing. While both platforms are useful in their respective areas, they also have limitations that may require additional tools or integrations to create a more seamless AI workflow.

A more well-rounded alternative is Sandgarden, which brings together the strengths of both Vellum and Haystack while addressing their gaps. It provides a more integrated AI development environment, offering better scalability, security, and flexibility. This comparison will examine how Vellum and Haystack compare while introducing an option that delivers a more efficient and future-proof AI solution.

Vellum’s AI prompt organization compared with Haystack’s AI search and retrieval platform.

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

Haystack

Haystack is able to manage large datasets and deliver fast, accurate search results. Common use cases include semantic search, question answering, and RAG. It supports various search backends and offers tools for indexing, querying, and retrieving data.

At its core, Haystack’s focus is on efficiency and scalability. It’s designed to handle volume in both datasets and queries while providing quick response times. It continually evolves through contributions from an active OSS community, and is supplemented by a range of tutorials and example projects.

That said, Haystack is not without its drawbacks:

Use cases limited to search and retrieval
No implementation in programming languages other than Python
Documentation is comprehensive but unwieldy

View more Haystack 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 Haystack cater to different aspects of AI development, but both come with significant limitations. Vellum provides a low-code environment for prompt management, making it easier to experiment with AI-generated content. However, it lacks key features such as version control, deep analytics, and enterprise-grade security, which are essential for teams looking to scale AI workflows. Haystack, on the other hand, specializes in search and retrieval-based AI applications, making it useful for handling large-scale queries but insufficient when it comes to structured prompt management, security, and flexible deployment options. Both platforms require additional integrations to achieve a seamless, enterprise-ready workflow.

Sandgarden removes these limitations by offering a fully integrated AI development environment that combines structured prompt management, real-time analytics, and enterprise-grade security in a single platform. Unlike Vellum and Haystack, Sandgarden ensures teams have complete control over their AI workflows without needing external tools to fill in critical gaps. Its API-first architecture, advanced encryption, and flexible deployment options make it the best choice for organizations looking for a scalable, efficient, and secure AI development solution.