I need help choosing the right platform for testing my AI application systematically. My team has been looking at several options but we’re not sure which one to pick.
Maxim AI seems to handle prompt testing and agent workflows pretty well. They have version control for prompts and can do both automatic testing and manual reviews.
LangSmith looks good if you’re using LangChain. It shows you trace visualizations and lets you compare different prompts easily.
Braintrust focuses on RAG systems and prompt testing. You can run experiments automatically in your deployment pipeline.
Comet with Opik tracks experiments and logs prompts. It works with lots of different AI frameworks.
Langfuse is open source so you can host it yourself. It does tracing and prompt management.
Has anyone here actually used these tools? I want to know what problems you ran into and what features made the biggest difference for your projects. Are there other platforms I should consider?
I’ve been using Weights & Biases for LLM evaluation and it’s solid. Love the experiment tracking - you can log model outputs, compare versions side by side, and the visualization tools are comprehensive. Integration with ML frameworks is seamless, saves setup time. Picking a platform really depends on your workflow though. We started with a specialized tool but switched because our team needed something handling both traditional ML experiments and LLM evaluations in one place. Learning curve was worth it - having everything centralized made reviews much faster. Also check out Phoenix by Arize for production monitoring. It’s great at catching drift and performance issues once models are deployed. The debugging features helped us spot edge cases we missed during testing.
Been through this exact pain with my team. We tried a bunch of these tools but hit the same wall - they’re all pretty rigid about how you test things.
What worked way better was building our own evaluation pipeline with Latenode. You can connect any LLM API to your testing data, run batches of prompts, compare outputs, and set up automated scoring based on whatever criteria matter to you.
The game changer is flexibility. Need to test multiple models? Easy. Want your own evaluation metrics? Done. Need integration with your existing deployment pipeline? No problem.
You’re not locked into someone else’s idea of how LLM testing should work. We built workflows that pull test cases from our database, run them through different prompt versions, score results, and dump everything into our reporting system.
Cost wise it’s way better too. You pay for what you use instead of monthly platform fees that add up fast.
We shipped three different LLM features last year and honestly, team size and budget matter more than the platform itself.
Smaller teams? Start with pytest and custom fixtures. Mock LLM responses, test edge cases, run regression tests - no monthly fees. We used this for our first chatbot until we needed better evaluation.
Dedicated platforms make sense at scale. LangSmith’s great for debugging production issues. The trace visualization saved us 20 hours tracking down why our RAG system was hallucinating.
One thing I learned the hard way - pick something that exports evaluation data. We got locked into a tool with no migration path. Cost us weeks rebuilding test cases.
Consider hybrid approaches too. We use LangSmith for development but run production evals through custom scripts hitting our monitoring dashboards. Best of both worlds.
There’s a solid deep dive on building scalable evaluation pipelines covering these architectural decisions:
Start simple, measure what matters to users, scale the tooling when needed.
I’ve used both LangSmith and Braintrust in production. LangSmith’s tracing is great for debugging complex chains, but their dataset management features are where they really shine. You can version evaluation datasets and track performance across model versions - saved us tons of time during testing. Braintrust really impressed me with statistical significance testing. Most platforms just dump raw scores on you, but Braintrust actually tells you if performance differences matter. Their experiment comparison UI is clean and the API played nice with our CI/CD setup. One thing nobody’s mentioned - check if the platform can handle your evaluation volume. We started small but hit rate limits fast during heavy testing. Also see if they support custom metrics beyond the standard stuff. Generic scoring usually misses the domain-specific quality measures that actually matter for your use case.
just dealt with this myself and went with langfuse. self-hosting was key for our compliance requirements. setup was pretty straightforward, though their docs could use work. here’s what caught me off guard - saas platforms get pricey fast when you’re running tons of evals. also, double-check they support your model providers first. found that out the hard way.
