The best search API is the smallest layer you can verify

There is no universal best AI search API. There is a costly architectural mistake: buying an answer endpoint when the application only needs ranked URLs, or assembling five retrieval calls when a research endpoint already meets the quality target.

Benchmark 200 frozen queries with a known-relevant URL set, freshness timestamps and prohibited domains. Record recall@10, primary-source rate, latency p50/p95, empty results, duplicate domains, extracted bytes and total bill. For answer endpoints, add claim-support review; for raw search, add your own model cost.

Primary action: run the same versioned fixture against the smallest viable endpoint from each provider and buy on cost per accepted application answer—not price per call.

Start with the response your application needs

For a link-discovery feature, the useful output is a ranked set of URLs with enough metadata to decide what to fetch. For a research assistant, the job may also require page extraction, dated sources and an answer whose claims can be checked. These are different workloads. Paying for a generated answer and then discarding it is waste; choosing raw links and forgetting the extraction and model bill understates cost.

The shortlist above describes candidates, not results from a BenPicks benchmark. Choose two that match the response layer you need. A researcher tracing primary sources and a support assistant searching approved documentation should not share a winner merely because both use the word search.

What to check in the API documentation

These are verification questions, not a statement that every provider includes each capability. Read the selected endpoint and plan documentation, rather than borrowing a feature from the vendor's consumer subscription. Keep API keys on the server and use an approved test corpus without customer secrets.

Model completed work, including retries

Suppose a hypothetical research application receives 1,000 questions a month and makes two search calls for each. That is 2,000 search calls. If 100 questions need one additional search, the total becomes 2,100. Extracting three pages per question would add 3,000 extraction operations if your chosen design bills them separately. Model requests and tokens belong on their own rows.

Those counts are planning assumptions, not a vendor's credit conversion. Apply the actual endpoint rates and billing rules only after checking them. A successful request that returns irrelevant sources still consumes budget. Likewise, a cached response can reduce repeated work only when reuse is permitted and the freshness requirement allows it.

Use the API cost guide to separate these layers. Record cost per accepted answer alongside the raw request bill. Define an accepted answer before the evaluation: for example, the required source is present, its date meets the task and the written claim is supported. That definition should come from the application, not from the provider's score.

Build the smallest useful evaluation

Start with 20 representative questions to debug the fixture before spending on the larger 200-query panel. Include a known answer, a newly changed fact, an obscure primary source, an ambiguous name and a question that should produce no supported answer. Freeze the prompts and relevant URLs so that a later run has a meaningful comparison.

Inspect the failures individually. A missing source may reflect coverage, a filter or a query that describes the wrong thing. A plausible answer with the wrong citation is a grounding failure even if the prose reads well. Follow the retrieval freshness and source-coverage guide for the next layer of the evaluation. No evaluation has been executed for this shortlist.

Switching providers without rebuilding the product

Keep your application's internal result fields separate from a vendor's response format. Retain the URL, retrieval time, source date where known and any attribution requirement. Before changing providers, replay the frozen questions and inspect where result structure, filters, citation mapping or billing units differ. Do not silently substitute one endpoint for another in production.

A raw-search endpoint is a good first candidate when your team already owns extraction and answer generation. An answer or research layer deserves evaluation when it removes work you would otherwise implement and passes the source checks. Neither is a good fit if the required sources cannot be retrieved or the usage terms do not permit the intended product. Resolve that constraint before comparing headline prices.

Official sources

• Tavily documentation ↗
• Exa documentation ↗
• You.com API pricing ↗
• Brave Search API plans ↗
• Kagi Search API ↗ — API behavior rechecked 18 October 2026; no current price established by that page.
• Perplexity API pricing ↗