MLflow vs. Vellum

MLflow and Vellum are two AI development platforms with different areas of focus. MLflow is designed for managing the machine learning lifecycle, providing tools for experiment tracking, model versioning, and deployment. Vellum, on the other hand, specializes in prompt management, making it easier for teams to refine and optimize AI-generated content. While both platforms serve important functions, they also have limitations that may require additional tools or integrations to create a more complete AI workflow.

For teams seeking a more unified and scalable solution, there is another platform worth considering. Sandgarden extends beyond the capabilities of both MLflow and Vellum, offering a more comprehensive approach to AI development. This comparison will explore how MLflow and Vellum compare while also introducing an alternative that delivers greater flexibility, efficiency, and long-term scalability.

MLflow’s AI model tracking compared to Vellum’s low-code prompt 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

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

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

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

MLflow and Vellum offer valuable capabilities in AI development, but each has critical limitations that prevent them from serving as a complete solution. MLflow is a strong choice for managing the machine learning lifecycle, including model tracking and deployment, but it lacks structured prompt management, comprehensive analytics, and enterprise-level security. Vellum, on the other hand, is designed to simplify prompt engineering, making it accessible for teams working with AI models, but it does not provide robust evaluation, logging, or scalable deployment options. Both platforms require additional integrations to bridge these gaps, leading to inefficiencies in AI workflows.

Where these platforms fall short, Sandgarden excels. Instead of forcing teams to rely on multiple tools, Sandgarden integrates structured prompt management, real-time analytics, version control, and security features into a single, powerful platform. Unlike MLflow and Vellum, it offers full encryption, access control, and deployment flexibility, ensuring teams can build, test, and scale AI applications without unnecessary complexity. For organizations seeking a streamlined, enterprise-grade AI development environment, Sandgarden is the clear leader.