Compare search APIs on predictable pricing for agent workloads by looking at four things: the billable unit, whether there's a hard cap, what happens at overage, and whether prepaid credits expire. Index quality decides which API you want. Those four decide whether you can forecast the bill when an agent decides to run 30 searches instead of three.

Key Takeaways

• Agent workloads break search API budgets because the agent, not a human, chooses how many queries to run per task.

• The billable unit matters more than the headline rate: per query, per result returned and per crawled page produce very different bills for identical work.

• A hard cap is the only control that actually stops spend. Alerts tell you after the money is gone.

• Prepaid credits with an expiry date are a price increase in disguise if your usage is seasonal.

• If you resell search inside your own agent product, you need per-customer attribution of that cost or your margin is invisible.

What should I compare across search API pricing models?

Compare the mechanics that determine your invoice, not the marketing tier names. Run every vendor through the same six checks before you look at price.

• Billable unit. Per query, per result, per page crawled, or per credit that converts at different rates by endpoint.

• Rate variation by feature. Deep search, extraction and summarisation usually cost more than a plain query.

• Hard cap. Whether the vendor can stop serving at a limit you set, or only warn you.

• Overage behaviour. Auto-charge, throttle, or a hard stop.

• Credit expiry. Whether unused prepaid balance rolls over or dies at period end.

• Commitment terms. Whether a discount requires an annual minimum you may not hit.

Why do agent workloads make search API costs unpredictable?

Agent workloads are unpredictable because query volume is a function of task difficulty, not user count. One ambiguous request can trigger a retry loop that issues dozens of searches, and none of that shows up in a seat-based forecast.

• A single agent run can fan out across sub-agents, each issuing its own queries.

• Retries on a failed or low-confidence result multiply spend with no extra output.

• Tool-choice changes in a new model version shift query volume without a code change.

• Volume scales with tasks completed, so a successful month costs more than a quiet one.

How do I estimate and control search API spend for agents?

Instrument queries per completed task first, then set the cap from that distribution rather than from a monthly total. Queries per task is the number that stays stable when your traffic grows.

Caching is the control with the best return here, because agents re-ask the same question constantly across runs and near-duplicate queries inside a single fan-out.