Alright, so the team was about to sign another $2k/month contract for one of those third-party "LLM evaluation platforms." You know the type: slick UI, five new AI buzzwords in the tagline, and a pricing page that requires a finance degree to parse.
I told them to hold off. The core thing we needed was a way to run our custom evaluation logic against our LangSmith datasets and track the results over time. Turns out, you can do that without the middleman markup.
We already had everything we were paying for elsewhere:
* Datasets in LangSmith (our "ground truth," such as it is)
* A bunch of Python functions for our specific compliance checks (hallucination detection against our internal docs, structured output validation, sensitive data leakage scans)
* The LangSmith SDK
The "harness" is basically a glorified script. It:
- Pulls a dataset from LangSmith.
- Runs our current prompt chain against each example.
- Applies our custom eval functions to the outputs.
- Pushes the results back to LangSmith as feedback scores.
Now every experiment run or prompt change automatically gets evaluated against our entire test suite. The graphs and comparisons are all right there in LangSmith, same as if we'd used their built-in evaluators.
The savings are obvious, but the real win is control. Those third-party platforms never quite fit our edge cases—like checking if a support response correctly withholds internal architecture details (SOC2, CCM, you know the drill). Their regex-based "compliance" checks were laughably easy to bypass.
So we're down $2k/month, and our evals actually test what we need. The whole "best-in-breed" evaluation platform market feels a bit like a racket now. Maybe just build the thing.
kj
It's not secure, it's just not exploited yet.
Exactly. This is the same pattern as overpriced integration platforms versus a solid middleware core. They're selling you a UI wrapper around APIs you already own.
The trick is making sure that script runs reliably in your pipeline, not just on someone's laptop. We run ours as a scheduled job in our existing workflow engine. It pushes scores to LangSmith and fails the deployment gate if hallucination scores dip below threshold.
What are you using to trigger runs? Is it manual or automated?
Integration is not a project, it's a lifestyle.
Love this approach, and user278's follow-up about automation is key. That shift from a "glorified script" to a scheduled pipeline is where the real reliability comes in.
One caveat from our moderation days: make sure those custom eval functions are version-controlled alongside the prompt chains they're checking. It's a classic pitfall to have a scoring change that looks like a model regression, but actually it's just the test that got stricter. LangSmith's tagging helps, but linking the code commit to the run is a lifesaver.
Raise the signal, lower the noise.