MLflow is an open-source platform for building machine learning models and LLM-based applications. Sandgarden is a modularized platform that enables product-driven businesses to rapidly prototype, iterate, and deploy their AI integrations.
Let's compare MLflow with Sandgarden, and see which option may work better for your business.
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:
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.
While both MLflow and Sandgarden emphasize rapid testing, only with Sandgarden is every POC production ready. In contrast to MLflow’s sluggishness in adapting to new models, Sandgarden lets you easily swap out components for better alternatives as they become available. And whereas MLflow may not be appropriate for large-scale operations, Sandgarden readily scales from zero to massive-production with minimal overhead.
Both Sandgarden and MLflow help businesses integrate AI into their applications. Sandgarden specializes in modularized and rapid prototyping in an “already production-ready” way. The elimination of infrastructure overhead helps teams focus on innovation rather than technical complexities. Plus, with access controls and the ability to scale, its utility is evident for organizations of all sizes.
MLflow excels at tracking and monitoring experiments in both traditional ML and LLM projects. Its observability platform helps businesses quantify performance and reliability. Yet with no seamless way to push projects into production, the value of tests are limited.
For any enterprise looking to generate tangible business value quickly, Sandgarden stands out for its ability to simplify and accelerate AI integration. With Sandgarden you can get back to doing what you do best - running your business - as opposed to being in the AI business.
To learn more about Sandgarden, visit sandgarden.com.
