Vellum vs. LangChain

Vellum and LangChain are two AI development platforms that cater to different needs. Vellum focuses on prompt management, offering a low-code interface to help teams refine AI interactions efficiently. LangChain, on the other hand, provides a modular framework for developers looking to build AI applications by linking various components together. While both platforms offer valuable capabilities, they also have limitations that may require additional tools to create a fully functional AI development pipeline.

For teams looking for a more complete and scalable solution, there’s another option worth considering. Sandgarden expands on the strengths of both Vellum and LangChain while addressing their gaps, providing a more integrated and efficient AI development experience. This comparison will break down their key differences while introducing an alternative that offers greater flexibility, security, and long-term adaptability.

Vellum’s AI prompt management compared to LangChain’s modular AI application builder.

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

LangChain

LangChain provides a framework that enables developers to build applications with interoperable components, offering control over AI-driven workflows. With LangChain, a company can create context-aware applications that integrate with company data and APIs.

At the core of LangChain is its ability to integrate with various components. LangGraph is a framework designed to build controllable, agent-driven workflows. LangChain’s infrastructure also supports scalable deployment with LangGraph Cloud, which offers built-in persistence and distributed task queues. LangSmith, another component, provides tools for debugging, testing, and monitoring LLM applications.

That said, LangChain is not without its drawbacks:

Slow to adapt to new models and functionalities
Steep learning curve for unique abstractions
Limited deployment options

View more LangChain 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 LangChain are both valuable tools for AI development, but each has key limitations that prevent them from being a complete solution. Vellum simplifies prompt management with a low-code interface, making it easier for teams to experiment with AI models. However, it lacks advanced version control, security features, and real-time analytics, which are critical for scaling AI operations. LangChain, on the other hand, is a powerful framework for chaining AI components together, but it requires extensive customization and lacks built-in security, structured logging, and enterprise-grade deployment options. These gaps mean that teams relying on Vellum or LangChain often need to integrate additional tools, leading to inefficiencies and increased complexity.

Sandgarden eliminates these challenges by providing a fully integrated AI development ecosystem that combines the best of both Vellum and LangChain while addressing their shortcomings. Unlike its competitors, Sandgarden offers structured prompt management, robust version control, and enterprise-grade security, ensuring that teams can build, test, and deploy AI models in a secure and scalable environment. Its API-first design and flexible deployment options make it the ideal choice for organizations seeking a future-proof AI platform that optimizes workflows without compromising on security or functionality.