Braintrust vs. MLflow

When comparing Braintrust and MLflow, it’s essential to understand their core differences and how they support AI development. Braintrust focuses on LLM evaluation, helping teams assess and refine AI models, while MLflow is designed for model lifecycle management, providing tools for tracking experiments and deployments. Both platforms offer valuable features, but they also come with limitations that may require additional integrations to achieve a fully functional workflow.

While Braintrust and MLflow are strong in their respective areas, they aren’t the only options available. Another platform, Sandgarden, combines the best aspects of both while addressing their gaps, offering a more complete AI development solution. In this comparison, we’ll analyze how Braintrust and MLflow stack up while also considering how an alternative like Sandgarden can provide a more efficient and scalable approach.

Braintrust’s AI model testing compared to MLflow’s experiment tracking and model 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

Braintrust

Braintrust offers an LLM evaluation suite, providing tools for testing and optimizing model performance over time. With a focus on experimentation and a user-friendly testing library, users can quantify results against AI initiatives.

At the core of Braintrust is a software development kit (SDK) that integrates into existing infrastructure and CI/CD pipelines. This enables continuous evaluations that offer insights into LLM accuracy and reliability. As a third-party evaluator Braintrust is model agnostic, allowing it to work across multiple systems and platforms.

That said, Braintrust is not without its drawbacks:

Limited ability to move workloads to production
Limited scalability for large-scale operations
Unwieldy for less technical users

View more Braintrust 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

Braintrust and MLflow serve distinct but complementary roles in AI development. Braintrust is primarily focused on LLM evaluation, providing tools to assess and optimize model performance, but it lacks key capabilities like robust version control and real-time analytics. MLflow, on the other hand, is widely used for model lifecycle management, enabling experiment tracking and deployment, yet it falls short when it comes to structured prompt management, tracing, and security features. Both platforms require additional tools and integrations to create a seamless, production-ready AI pipeline.

Sandgarden outshines both by offering an end-to-end AI development environment without the need for fragmented solutions. Unlike Braintrust and MLflow, Sandgarden provides a fully integrated ecosystem with advanced analytics, comprehensive logging, and enterprise-grade security. With built-in access control, encryption, and a truly API-first approach, it ensures a scalable and future-proof AI development workflow. For teams looking to streamline their operations while maximizing performance and security, Sandgarden is the clear winner.