How to Control the Cost and Latency of Continuous Evals

Continuous evals are what catch a misbehaving agent before your users do. The catch is that they run against every production interaction, not a sample. That property is exactly what makes them useful, and exactly what makes cost and latency add up fast. Every eval you add is another model call, or several, on top of every request your agent already serves. At low traffic this is invisible. At production scale it shows up on your bill and in your p95.

The good news: you can keep eval signal high while keeping spend and added latency low. None of the levers below require sacrificing reliability. They mostly come down to using the right tool for each check and not paying for more model than the job needs.

Right-size the model to the job

The most direct lever is the model you run your evals on. Eval prompts are often simpler than the agent task they're judging, which means a smaller, cheaper model can frequently do the work without a measurable drop in accuracy.

The way to find out is to measure, not guess:

• Establish a quality baseline with a capable model first, so you know what "correct" looks like for each eval.
• Run a smaller model against the same set of interactions and compare its decisions to the baseline.
• If accuracy holds, promote the smaller model. You cut eval cost significantly and shave latency on every request, with no loss of signal.
• If the smaller model struggles, that usually points to a prompt that needs more specificity or better examples, not a hard requirement for the larger model.

That last point matters. A smaller model failing is often a prompt problem in disguise. Tightening the eval definition and adding a couple of boundary examples will frequently bring a cheaper model up to baseline, which means you get the accuracy and the savings.

Use binary pass/fail to cut inconsistency

How you score an eval affects both its reliability and its cost. Range-based scoring (1 to 10, low/medium/high) reads like it gives you more information, but LLMs are inconsistent scorers: the same interaction can return a 4 on one run and a 6 on the next. That forces a human to decide what threshold deserves attention, and the noise makes the threshold a moving target.

Binary pass/fail removes that ambiguity. When an eval fires, it means something requires attention, full stop. That reliability is what lets you act on eval results automatically instead of routing everything through a person. Pair each binary decision with a short explanation so you can spot patterns across failures without re-reading every interaction from scratch.

Specificity helps here too. A narrow eval ("did the response reference information not present in the retrieved documents?") is more stable and cheaper to run than a broad "rate the overall quality of this response." Broad prompts produce noisy results that you end up re-running or reviewing by hand, which is its own hidden cost.

Use deterministic checks where an LLM eval is overkill

Not every check needs a model. A large share of what teams reach for an LLM to verify can be done with a deterministic function that runs in microseconds, costs nothing, and never disagrees with itself. Forcing an LLM into that role adds latency and spend for a job a simple check does better.

Reach for a programmatic check when the answer is objective:

• Validating output format or structure with a schema validator.
• Verifying a calculation or that a number falls within an expected range.
• Confirming a specific tool was called, or called in the right order.
• Checking for the presence of a required field before a response is finalized.

These run in your hot path for a fraction of the cost of a model call. Spending an LLM eval on them is the most common source of avoidable eval cost we see.

Know what LLM evals are good at, and what they aren't

The clean way to decide is to match the check to what each tool is actually good at.

LLM-based evals are strong at generalizing over content: assessing tone, completeness, adherence to instructions, and whether a response is grounded in the source material it was given. These are judgment calls with no single correct string to match against, which is exactly where a model earns its cost.

They are weaker at precise quantitative assessment. Measuring precision and recall of a retrieval step, verifying exact math, or enforcing a strict numeric range are all things a deterministic check does more reliably and far more cheaply. When you need precision, use a function. When you need judgment, use a model. Most eval cost problems are really a mismatch between the two.

TLDR

Continuous evals run on every production interaction, so cost and latency compound with traffic. Four levers keep them affordable without losing signal:

• Right-size the model: baseline with a capable model, then test whether a smaller one matches it.
• Score binary pass/fail with explanations to cut inconsistency and enable automatic action.
• Use deterministic checks (schema validators, math verification, simple functions) wherever the answer is objective.
• Reserve LLM evals for content judgment (tone, completeness, groundedness) and hand precise quantitative checks to programmatic verification.

Applied together, these keep your eval layer fast and affordable as traffic grows, without giving up the coverage that makes continuous evals and experiments worth running in the first place.

Want to see how Arthur handles continuous evals at production scale? Book a demo with an AI expert.

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