Another week, another "must-have" AI evaluation tool. Gauge is fine, but it's getting priced like it's Salesforce for LLMs.
Most teams just need to know if their RAG pipeline got worse after a change. You don't need a whole platform for that.
Look at:
* **DeepEval** - Open source. Does the core things. You can run it in CI/CD. It's not pretty, but it works.
* **Ragas** - Also OSS. Good for evaluating retrieval, which is where most pipelines actually break.
* **Pytest** with some custom assertions - Seriously. For basic output validation, you're overcomplicating it.
The big vendors will sell you a suite. You'll use 10% of it. Then your budget gets cut. 😒
What are you actually trying to evaluate? If it's just "does the answer contain the right keyword," save your money.
CRM is a means, not an end.
I'm a knowledge lead at a 180-person tech consultancy, and we run evaluation checks in CI for about a dozen client RAG projects, mostly using open-source tools.
Here's the breakdown from running tests last quarter:
**Total cost of ownership**: DeepEval is free, Ragas is free, Pytest is free. Gauge starts at $30k/year for their entry platform tier, plus a 15-20% setup fee. The real budget hit is engineering time managing another vendor platform.
**Integration & iteration speed**: Pytest hooks into existing CI in an afternoon. DeepEval needs maybe a day to wire up. Ragas is similar. Gauge required a dedicated 2-week integration cycle and a bi-weekly sync with their solutions engineer just to get baseline reports running.
**Where it breaks (the gotcha)**: Ragas metrics can get noisy with small document sets under 1k chunks. DeepEval's answer relevancy scorer sometimes needs tuning for your specific LLM's verbosity. Gauge's pre-built "adversarial tests" often didn't match our actual user queries, so we rebuilt them anyway.
**Clear win scenario**: Ragas is unmatched for isolating retrieval degradation. If a new embedding model or chunking strategy hurts your top-3 recall, Ragas surfaces it immediately. DeepEval's win is having more canned metrics (faithfulness, answer similarity) ready to go.
My pick is Ragas if you're constantly tweaking retrieval (embedding models, chunk size, metadata filters). Use Pytest if you just need to validate structured outputs or keyword presence. To decide, tell us: what's one metric your product team complains about most, and are your pipelines stable or changing weekly?
You've hit on the crucial starting point: "What are you actually trying to evaluate?" Too many teams skip this and jump straight to tool procurement.
Your list of open-source options is solid for teams with the engineering bandwidth. The real risk I see is when someone chooses Pytest for custom assertions but doesn't establish a clear, maintainable rubric for those checks. It can become a messy pile of regex that only the original author understands.
Start with the evaluation criteria, then pick the simplest tool that meets them. A lot of the time, that is Pytest.
Keep it constructive.