Rivet vs. MLflow

Rivet and MLflow are two AI development platforms designed for different purposes. Rivet provides a no-code visual programming interface, making it easier for teams to build AI workflows without extensive coding, while MLflow focuses on managing the machine learning lifecycle, offering tools for experiment tracking and model deployment. While both platforms have their advantages, they also come with limitations that may require additional tools and integrations to achieve a fully functional AI development pipeline.

For teams looking for a more complete and scalable solution, there is another option to consider. Sandgarden combines the strengths of both Rivet and MLflow while addressing their gaps, offering a more flexible and integrated approach to AI development. This comparison will examine how Rivet and MLflow compare while also exploring how an alternative like Sandgarden can provide a more advanced and efficient solution.

Rivet’s AI workflow creation compared to MLflow’s AI model lifecycle management.

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

MLflow

At its core MLflow is a tool for systematically tracking experiments and facilitating the reproduction of high quality results. It also provides an observability suite for performance monitoring. Together, they help businesses quickly filter out noise and focus on implementing the most reliable ML models and LLM-based workflows.

Along with these features, MLflow has recently rolled out a prompt management UI where users can create and refine prompts without diving deep into code. This democratizes the process of prompt generation, facilitating its use across the organization. The platform continually evolves through contributions from its OSS community, and is supplemented by solid documentation.

That said, MLflow is not without its drawbacks:

Limited ability to move workloads to production
Slow to adapt to new models and functionalities
Limited scalability for large-scale operations

View more MLflow 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 MLflow serve different functions in the AI development space, but both come with limitations that can hinder scalability and efficiency. Rivet’s no-code visual programming approach makes it accessible for beginners, but it lacks the depth needed for complex AI workflows, including advanced version control, security, and analytics. MLflow, on the other hand, is a widely used platform for model lifecycle management, providing tools for tracking experiments and deploying models. However, it does not offer built-in prompt management, structured logging, or enterprise-grade security, requiring additional integrations to fill these gaps.

Sandgarden outperforms both by delivering a fully integrated AI development environment that eliminates the need for third-party solutions. Unlike Rivet and MLflow, Sandgarden combines structured prompt management, real-time analytics, and end-to-end security within a single, scalable platform. With flexible deployment options, API-first architecture, and enterprise-level encryption, Sandgarden ensures that AI teams can seamlessly build, test, and deploy models without workflow fragmentation. For businesses looking for a robust, all-in-one AI development solution, Sandgarden is the superior choice.