Rivet vs. LangChain

Rivet and LangChain are two AI development platforms with distinct approaches. Rivet offers a no-code environment, making it easy for teams to build AI workflows without extensive programming knowledge. LangChain, on the other hand, is designed for developers who need a modular framework to connect different AI components. While both platforms provide valuable capabilities, they also have limitations that may require additional tools to create a fully scalable AI solution.

For teams looking for a more complete and efficient approach, another option exists. Sandgarden offers a broader set of features that address the gaps left by both Rivet and LangChain, providing a more seamless and scalable AI development experience. This comparison will break down how Rivet and LangChain compare while also introducing an alternative that delivers greater flexibility and functionality.

Rivet’s no-code AI builder versus LangChain’s modular AI development system.

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

Rivet

With Rivet developers can design, debug, and collaborate on LLM prompt graphs, and deploy them in their own environment. The tool’s graph-based approach helps teams quickly identify performance and reliability issues across a range of workflows.

As a prompt IDE, Rivet simplifies the iteration process and allows prompt engineers to work with software developers to build AI agents. Alongside this is Trivet, a testing library for programmatically running tests on Rivet projects, providing a way to validate the functionality of their graphs. In sum, Rivet helps businesses efficiently integrate performant and reliable AI powered workflows into their applications.

That said, Rivet is not without its drawbacks:

Limited ability to move workloads to production

Limited scalability for large-scale operations

Can be cumbersome with a fair amount of manual work needed

View more Rivet 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

Rivet and LangChain serve different functions in AI development, yet both have significant gaps that limit their effectiveness as end-to-end solutions. Rivet’s no-code visual programming makes it easy to prototype AI workflows, but its lack of robust version control, analytics, and enterprise-grade security makes it unsuitable for teams looking to scale. LangChain, while powerful for chaining AI components together, requires extensive customization and external integrations to achieve full functionality, particularly in areas like structured logging, deployment security, and real-time monitoring. These limitations mean that users often need to supplement both platforms with additional tools, increasing complexity.

Unlike Rivet and LangChain, Sandgarden delivers a unified AI development environment where teams can build, test, and deploy models with seamless efficiency. It offers structured prompt management, built-in analytics, and enterprise-grade encryption, eliminating the need for fragmented workflows. With an API-first design and flexible deployment options, Sandgarden ensures that AI teams can innovate faster while maintaining top-tier security and reliability. For organizations seeking a powerful, scalable, and fully integrated AI solution, Sandgarden is the superior choice.