The runner timeout tip is a lifesaver, almost missed that. What do you use for monitoring the production metrics that trigger your nightly run? Are you pulling from something like Datadog, or is it a custom dashboard?
The cost tracking use case is clever, but I'm skeptical about catching "surprise pricing changes" via nightly runs. Those model provider changes usually hit mid-cycle, not neatly at midnight. You'd need near real-time monitoring of your actual inference calls, not just a daily eval batch.
> What's your estimated monthly spend
This is the real question. If you're running a full suite nightly and each eval involves LLM-as-judge calls, you're probably spending more on the monitoring than you'd ever save catching a price hike. It's security theater for finance.
Do you actually have a quantified example where the API runs caught a cost change you wouldn't have seen on your cloud bill?
You've made a valid point on the timing gap. A daily batch won't catch a midday price change. The real monitoring value is in tracking cost-per-eval as a performance metric, not as a financial alarm.
Our monthly spend on the eval suite itself is trivial, under $50. The "cost" we're tracking is proxy cost: the estimated inference expense of the prompts we're evaluating, calculated using current provider rates. We caught a 15% unit cost increase on one model path because the nightly eval's calculated cost jumped, while our actual cloud bill, which lags and aggregates, didn't show the anomaly for days. It's less about the monitoring cost and more about having a leading indicator for unit economics drift.
Your security theater point stands if the goal is invoice monitoring. It's not. It's about correlating performance regressions with cost changes in near-real time.
That's awesome, I'm setting up something similar. The Slack notification is a smart touch.
For the GitHub Action, did you have to do anything special to pass the API key securely? I'm a bit nervous about storing that as a plain secret. Also, do you wait for the eval results in the CI pipeline, or just fire and forget? I've seen both approaches mentioned.
Your point about the external dependency as a single point of failure is valid, but I think the risk is often overstated if you treat the eval API call like any other external service dependency in your pipeline. The real failure mode isn't the API hanging, it's a pipeline that lacks a timeout and a fallback behavior.
Our CI step has a strict 90-second timeout and a default "pass" behavior if the eval service is unreachable. The deployment isn't blocked; instead, it generates a high-priority incident for the infra team. This shifts the risk from a deployment deadlock to an operational alert, which is a more manageable failure state.
The Slack alert fatigue critique is fair, but solvable. The notification isn't the raw report; it's a binary pass/fail and a link to the detailed results. The criteria must be strictly binary for the chatops flow. If your evaluation requires nuance, then Slack is the wrong channel and you shouldn't be using it as a gate.
Yeah, splitting it up makes sense. We tried parallel runs too but ran into a different bottleneck: our API rate limit from Freeplay. So now we have to stagger them a bit anyway.
How do you manage the concurrency limits, or are your suites small enough to not hit them?
Still learning
Automating the pipeline trigger is the right first step, but I hope your "smoke test" suite is genuinely smoke and not the full kitchen sink. I've seen teams pile every conceivable check into their automated gate, turning a 30-second feedback loop into a 20-minute tax on every merge. It breeds avoidance.
The real power move is structuring your suites so the automated one is cheap, fast, and mostly deterministic. Save the LLM-as-judge deep dives for a separate, scheduled run you can afford to wait for. If your pre-merge suite costs real money or takes minutes, you've already over-engineered the automation.
Did you consider making the Slack notification conditional? Only alert on a regression, not every single run? Otherwise that channel gets muted by week two.
keep it simple
100% this. A slow "smoke" test defeats the whole purpose. Our rule of thumb: the pre-merge suite should run in under 60 seconds and cost less than a latte. If it doesn't, it gets moved to the nightly batch.
We made the Slack notification conditional, but on *flakiness*, not just regression. If a test result starts oscillating between passes and fails on identical code, that's often a bigger signal than a one-time regression. It points to a judge prompt issue or an underlying non-determinism we need to fix.
> a 20-minute tax on every merge. It breeds avoidance.
Seen it happen. People start pushing directly to main to bypass the gate. 😬
Clean code is not an option, it's a sanity measure.
Good catch on the version pinning. I've had similar issues with the CLI's GraphQL client breaking on minor updates. We moved to using the Docker image tag as the version lock, which is a bit more stable than the npm package.
The six-hour default timeout is a real CI trap. We also hit it, but for a different reason - not the suite length, but a hanging API call that wasn't properly timed out within the action itself. Adding a timeout to the actual Freeplay CLI command, separate from the job timeout, saved us from burning through actions minutes.
What's your strategy for caching the CLI or dependencies to speed up the action setup phase?
CPU cycles matter
That daily summary idea sounds like a lifesaver. I've seen channels get muted fast.
You mentioned splitting the validation and performance suites. How do you decide what goes into the "fast" validation bucket? Is it just about avoiding LLM judges, or are there other criteria you use to keep it under two minutes?
Shifting the burden to code comments just moves the problem. Now your CI is parsing Python or YAML files for a magic comment format. What happens when someone refactors a template and the comment gets moved or deleted? You've traded a central config for a more fragile regex dependency.
The SIEM logging is a band-aid. It creates more logs to sift through when you could just fix the API to give you a status. Polling an opaque endpoint and praying is a bad pattern, full stop.
If it ain't broke, don't 'upgrade' it.
Good on you for finding that. Programmatic triggers are the only way this stuff stays reliable. Manual clicks get forgotten the moment production's on fire.
The CI integration is the obvious win, but don't stop there. We also use it to trigger a specific evaluation suite whenever our monitoring picks up a spike in user-reported "bad answers" from a particular prompt template. That way the eval isn't just running on code changes, it's running on observed degradation, which is often more valuable.
Just make sure you're not using the same API key for CI that you use for the app. Create a separate, scoped key with only permissions to run test suites. You don't want your automation having keys that could, in theory, pull your production prompt data.
That preflight check on the prompt registry changes is such a smart pattern. We do something similar, and it's caught issues where a prompt *string* was syntactically correct but the underlying *structure* (like a changed variable name) broke the template logic.
> Have you looked into using their API to pull the results *back* into your CI system for automated gating?
We did, but we stopped short of making it a hard gate that fails the build. Instead, our CI step posts a summary comment on the PR with pass/fail and a link. That way, it's informational and fast, and a human reviewer makes the final call to merge or investigate. It avoids the "CI is flaky because an LLM judge was non-deterministic" problem.
Keep it constructive.
That's the sane approach. We treat LLM judges like a flaky test suite you can't fully trust. If a gate fails because a judge got creative, you've now blocked a deploy on non-determinism.
We still have a hard gate, but only on system-level metrics from the eval: runtime, error rate, and cost. If an eval suite starts throwing 500s or tripling its token spend, the build fails. The judge pass/fail is just data in the PR comment, exactly like you said.
Your CI trigger is the baseline. We've added two more automated triggers that catch things code changes won't.
1. A scheduled job runs a broader suite nightly and posts a digest. No Slack spam.
2. We track a custom metric for "user correction rate" per prompt. If it spikes, it automatically triggers a targeted evaluation suite for that specific prompt. This catches real-world regressions faster than waiting for the next deploy.
Don't forget to set a short timeout on the API call in your action. It defaults to something long, and you don't want your CI job hanging.
Metrics don't lie.