Every pipeline costs a lot
If you aren’t careful, debugging a complex multi-agent pipeline can burn through your token quota (and your team’s budget) before your morning coffee break.
Even if you are using Anthropic and paying upfront rather than after-the-fact, an infinite loop bug in your code can still easily plunge your balance a few hundred dollars into the negative. Their API doesn’t cut you off instantly the second you hit zero.
When your test runner starts acting like a credit card machine, you have to re-engineer your approach. Here are go-to budget-saving strategies for ML Eval from our project:
- Mocks are your best friend: We simulate model responses for the purely structural and deterministic parts of the pipeline. We only hit the actual LLM API when testing the “intelligence” or non-deterministic reasoning itself.
- Module-level isolated entry points: We write small
if __name__ == "__main__":entry points for specific sub-modules. This allows us to test isolated logic and edge cases without spinning up the entire, expensive end-to-end agent loop. - Micro-batch debugging: We debug code with exactly 1-3 highly specific data rows. Only when the harness is structurally flawless do we scale it up to the full evaluation set.
In our project, we don’t just optimize reactively – we’ve built dedicated metrics to continuously track execution latency and token costs for both the production agent pipelines and our evaluation runs. We have both Langfuse tracking and internal metrics. Cost and speed are treated as first-class architectural constraints. If token consumption suddenly spikes or execution speed drops, it’s an immediate signal for the team to step in and optimize before the cloud bill hits.
