I’ve been looking into different platforms for testing and monitoring AI applications lately. Since there are so many options out there, I wanted to share what I found after comparing several popular tools. Testing AI systems properly is really important if you want them to work well in real applications.
Maxim AI - This one is built specifically for testing LLM applications and chatbots. You can run both automatic tests and have humans review outputs. It also handles prompt versions and lets you compare different approaches side by side. What I liked is that you can test before launching and keep monitoring after your app goes live.
Langfuse - This is an open source tool that tracks what your LLM is doing. It shows detailed logs of requests, counts tokens, and saves prompts. Good for developers but the testing features are pretty basic.
Braintrust - Focuses on creating test datasets that you can reuse. Good for running the same tests repeatedly to check if performance drops. Missing some features like advanced prompt management though.
Vellum - Combines prompt editing with testing tools. You can run A/B tests and work with team members easily. The prompt editor is solid but evaluation features are lighter than dedicated testing platforms.
Langsmith - Made for people using LangChain. Great for debugging chains and agents. More focused on developers than QA teams.
Comet - Known for traditional ML experiment tracking. Added LLM support recently but evaluation features are still developing.
Arize Phoenix - Open source monitoring library. Good at tracing model behavior but you need to build evaluations yourself.
LangWatch - Lightweight monitoring tool that’s easy to set up. Basic evaluation features compared to specialized platforms.
Anyone have experience with these tools or recommendations for other options?
Been through this recently and went with a hybrid setup. Started with Langsmith since we use LangChain heavily, but the QA features sucked for production. Great for debugging though - saved us tons of time on complex chains. Now we’re moving to Maxim AI for proper testing while keeping Langsmith for dev work. Yeah, it’s more expensive running both, but the human evaluation workflows already caught stuff our automated tests missed. Also, Vellum’s collaboration tools are solid if you’ve got non-tech people reviewing prompts.
Dealt with this same issue when we scaled our AI features last year. Tested about half these tools you mentioned.
Biggest pain was fragmentation - one tool for testing, another for monitoring, trying to connect dev and prod issues. Maxim AI fixed that for us. The continuous monitoring sold me since we could track model performance over time with real user data.
Learned this the hard way - don’t skip human review. Our automated tests missed context issues that only surfaced when real people used the system. Those edge cases would’ve been production disasters.
Langfuse works if you’re budget-tight, but you’ll outgrow it once things get complex. We still use it for lightweight projects.
Braintrust handled regression testing well, but yeah, prompt management sucks. If you’re iterating prompts constantly, it’s a workflow nightmare.
Video covers some free tools worth checking if you’re starting out. Sometimes simple beats overengineered testing setups.
i gave langfuse a shot – tracking was ok, but testing felt kinda limited. maxim ai seems like a solid option though, it wraps both testing and monitoring nicely. appreciate the info!
We’ve been using Braintrust for 6 months now. The reusable datasets are handy, but yeah, those prompt management limitations you mentioned are real. Had to build our own versioning system on top, which was a pain. Just started checking out Maxim AI too. The integrated approach looks solid - handling pre-launch testing and production monitoring in one place would beat juggling multiple tools. That human review feature caught my eye for spotting edge cases that automated tests miss. How’s Maxim’s pricing compared to running separate testing and monitoring solutions?
