Braintrust vs. Haystack

Braintrust and Haystack are two AI platforms that serve different purposes, each catering to distinct aspects of AI development. Braintrust specializes in LLM evaluation, helping teams assess and fine-tune model performance, while Haystack is designed for search and retrieval-based AI applications, making it useful for handling large-scale queries. While both platforms offer valuable features, they also come with limitations that may require additional tools and integrations to build a complete AI workflow.

For teams seeking a more well-rounded solution, there is another option worth considering. Sandgarden combines the strengths of both Braintrust and Haystack while addressing their weaknesses, offering a more scalable and efficient AI development environment. This comparison will break down the key differences between Braintrust and Haystack while also exploring how an alternative like Sandgarden can provide a more comprehensive and future-ready approach.

Braintrust’s AI model evaluation tools compared with Haystack’s search-based AI platform.

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

Haystack

Haystack is able to manage large datasets and deliver fast, accurate search results. Common use cases include semantic search, question answering, and RAG. It supports various search backends and offers tools for indexing, querying, and retrieving data.

At its core, Haystack’s focus is on efficiency and scalability. It’s designed to handle volume in both datasets and queries while providing quick response times. It continually evolves through contributions from an active OSS community, and is supplemented by a range of tutorials and example projects.

That said, Haystack is not without its drawbacks:

Use cases limited to search and retrieval
No implementation in programming languages other than Python
Documentation is comprehensive but unwieldy

View more Haystack 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 Haystack each offer unique capabilities in AI development, but both come with limitations that hinder a seamless workflow. Braintrust is known for its LLM evaluation features, allowing teams to measure model performance, but it lacks critical components such as version control, real-time analytics, and enterprise-grade security. Haystack, on the other hand, is a powerful framework for building search and retrieval-based AI applications but falls short when it comes to comprehensive prompt management, structured logging, and advanced access control. Teams relying on either platform must integrate multiple external tools to bridge these gaps, leading to inefficiencies and added complexity.

Sandgarden provides the superior solution by offering an all-in-one AI development ecosystem that removes the need for fragmented workflows. Unlike Braintrust and Haystack, Sandgarden seamlessly integrates LLM evaluation, structured prompt management, analytics, and high-security features, including encryption and access control. With its API-first architecture and flexible deployment options, Sandgarden enables teams to build, test, and deploy AI models with greater speed, security, and efficiency. For organizations looking for a future-proof AI platform without the trade-offs, Sandgarden is the clear winner.