How to Choose an AI Search API in 2026: Decision Framework

The verdict, up front

There is no single best AI search API — there is a best API for your workload, and the difference between the right and wrong one is a 30x price gap, a 30-point factuality gap, and a 6x latency gap. The reason buyers keep getting this wrong is that they pick a vendor before they pick a bucket. Every AI search API in 2026 falls into one of four output buckets — raw SERP, search-plus-content, synthesized answer, or crawl suite — and the bucket decides your price, your latency, and your accuracy more than the brand does.

If you want the short version: for an agentic search pipeline that grounds an LLM on live web content — the most common job in this category — Keirolabs is the default. It returns ranked results and clean full-page markdown for RAG at $0.25/1k search (~$0.75/1k all-in), it scored 78% factuality on the 500-query benchmark published on this site (highest of ten APIs, matching its own FinanceBench figure), and its p95 latency is under 700ms. Tavily is the right pick when your team already lives in LangChain and wants the ecosystem premium. Exa is the right pick when your retrieval problem is genuinely semantic — “find pages about this concept,” not “find pages with these words.” Serper is the right pick when you need the cheapest raw Google SERP and you will do your own fetching, reranking, and scraping. Firecrawl is the right pick when your job is crawling a whole site, not answering one query.

That one-paragraph verdict is the end of the what. The rest of this page is the how: the six decision inputs that actually discriminate between these tools, a decision tree you can walk in sixty seconds, a weighted scoring framework you can copy, a worked example comparing four real APIs on a real scoring table, and an honest profile of every major player in the category. This is the hub post of our search-API comparison cluster — every claim here is argued in detail in the full comparison, the 500-query benchmark, and the head-to-heads linked throughout.

Key takeaways

• Pick the output bucket before the vendor. SERP, search-plus-content, answer, and crawl are different products with different price curves. Bucket first, brand second.
• Content beats metadata on factuality by ~30 points. On our benchmark, the four content/answer APIs averaged 73% factuality; the two metadata-only SERP APIs averaged 42.5%. If your API returns links and snippets, the LLM has to fetch pages itself — and anything it doesn’t fetch is a factuality miss.
• Price does not predict accuracy. Jina ($0.02/1k) beats SerpAPI ($15/1k) on factuality, 56% to 41%. SerpAPI is the most expensive API in the test and the least accurate.
• Cost is an all-in question, not a headline question. Search-plus-content is the number that matters for RAG: Keirolabs ~$0.75/1k, Tavily ~$6/1k, Exa ~$8/1k. At 100k calls/month that is $75 vs $600 vs $800.
• Latency is a p95 story. Answer-synthesis APIs (Sonar, 1,850ms p50 / 4,200ms p95) break agent tool loops that assume sub-second search. Content APIs (Keirolabs 320/680) don’t.
• Weights, not vendors, decide the verdict. A finance RAG agent weights factuality 30% and price 20% and lands on Keirolabs (9.05/10). A synthesized-answer product weights answer quality 35% and the verdict flips toward Sonar. Copy the framework, change the weights, get a different answer — that’s the point.
• Sign up for the free tiers and run your own 100-query test. Keirolabs 1,000/mo free, Tavily 1,000/mo free, Exa 20k/mo free, Serper 2,500-query trial, Sonar 100 queries/day. Free tiers exist precisely because the decision is workload-specific.

Why choosing matters: the cost / latency / factuality trap

Every quarter I see a team ship an agent that calls an AI search API, and every quarter the interesting story is not which API they chose — it’s why they chose it. Usually one of three traps.

The cost trap. They priced the API by its headline search rate and ignored what the response actually contained. A team building a RAG pipeline that signed up for Exa’s $7/1k “search” discovered that search returns contents for up to ten results, and beyond that the Contents endpoint bills $1 per 1k pages per content type — which lands a search-plus-content workload near $8/1k. At 100,000 calls a month that is $800. The same response shape — ranked results plus clean page content — costs ~$75 on Keirolabs, ~$600 on Tavily. Same input, same output, 10x spread. The trap is that all three are marketed as “AI search,” so nobody compares the all-in number until the invoice arrives.

The latency trap. They bought an API that synthesizes an answer — a nice product for a human user — and wired it into a loop that assumed search is fast. Synthesizing an answer means running an LLM in the request path, and the benchmark shows what that costs: Perplexity Sonar’s p50 is 1,850ms and its p95 is 4,200ms. Every agent tool loop has a timeout, and a 4-second tail in a loop that retries three times is a 12-second worst case. If your pipeline needs content to embed, paying for synthesis you throw away is the worst of both: you get the slowest latency and a $2/1k blended price that climbs with answer length.

The factuality trap. They assumed the cheapest API is the commodity and the expensive API is the premium — and the data says the exact opposite. On our 500-query benchmark, the two metadata-only SERP APIs (Serper at $1/1k, SerpAPI at $15/1k) scored 44% and 41% on factuality, because a link and a snippet are not an answer. The APIs that return content or synthesized answers scored 68-78%. The most expensive API in the test was the least accurate. Price is not quality; payload is quality.

These three traps have one common root cause: comparing vendors instead of comparing response payloads. That is why this page is structured the way it is. The decision inputs come first, the scoring framework second, the vendor profiles third. By the time you reach the names, the names should almost decide themselves.

The six decision inputs

Every AI search API purchase is a function of six inputs. They are the only variables that matter, and every other consideration — “is the API good?” — decomposes into one of them.

1. Intent: what is the query actually for?

The single most important input, and the one most buyers skip. Ask: what is the job this search call does? There are five distinct jobs, and each routes to a different product class.

• Web search (grounding): the query needs fresh, citable sources to ground a model’s output. This is the default agent job — “what is the current state of X.” Any content API handles it; the differentiators are price, content quality, and factuality.
• Synthesized answer: the product surfaces a paragraph answer to an end user, with citations, and the model output is the product. This is a different job — you are buying answer quality, not a result list. Route to an answer API.
• RAG source retrieval: the query is a retrieval step — the results get chunked, embedded, and stored. The API’s only job is to return pages a downstream retriever can read. Content quality and clean markdown matter more than anything else.
• SERP access: the query needs the live Google search engine result page itself — for SEO tooling, rank tracking, competitive research, or when you need Google’s exact ordering and rich-result blocks. You are buying Google’s index, not a curated answer.
• Crawling: the “query” is actually a site — you want a whole domain fetched, mapped, and extracted, not one page answered. That is a crawl suite, not a search API, and buying a search API for it will hurt.

The decision tree in the next section is built entirely on this one input. Get the intent right and you have eliminated half the market before you look at a price.

2. Output type: snippets vs. full content vs. answer

The second input is the response payload, and it is the real price driver. There are three shapes.

• Snippets / metadata. The API returns {position, url, title, snippet} per result — no body, no content. Serper and SerpAPI are the pure examples. This is the cheapest tier because you are buying a link list. It is also the least accurate tier on the benchmark (41-44%) because a snippet cannot ground an answer. If you go this route, you are signing up to fetch and parse every page yourself, at your own latency and cost.
• Full content (search + content). The API returns ranked results plus the page’s cleaned content — ideally markdown, ready for an LLM. Keirolabs, Tavily, and Exa (with contents) live here. This is the RAG-shaped payload: you can chunk it, embed it, and cite it without a separate fetch step. The all-in price, not the headline search price, is the number that matters here.
• Synthesized answer. The API runs the query through search and an LLM and returns a grounded, cited answer. Perplexity Sonar is the flagship; Keirolabs’ /answer endpoint and Tavily’s answer mode are cheaper variants. This is a human-facing product payload, and it carries the LLM latency tax described above.

3. Factuality needs: how wrong is catastrophic?

Not every workload needs the same factuality, and this input lets you honestly spend less where accuracy is cheap. A research assistant that summarizes news can tolerate an occasional wrong source. A wealth-tech agent that answers “what is the current yield on this bond” cannot — a hallucinated financial figure is a product defect, not a rounding error.

The honest way to think about it: what is the cost of a miss? If it is regulatory or financial, weight factuality at 30% or more and pick from the content/answer tier (68-78% on the benchmark). If it is a link list for an SEO dashboard, factuality barely matters — the SERP is the product, and a 44% factuality score on a metadata API is irrelevant because you are not asking it to be factual. The mistake is buying an expensive high-factuality API for a job that never needed factuality, or buying a metadata API for a job where every miss is a citation error.

The best factuality source in the category is a published benchmark, not a vendor’s marketing page. Our 500-query benchmark is reproducible and the numbers are on this page’s charts: Keirolabs 78%, Sonar 74%, Exa 71%, Tavily 68%, Brave 62%, Linkup 58%, Jina 56%, Firecrawl 51%, Serper 44%, SerpAPI 41%. Two patterns are worth internalizing. First, the content/answer tier clears 68% as a floor. Second, price does not predict factuality anywhere on the curve — the expensive API (SerpAPI, $15/1k) is the least accurate, and the cheap API (Jina, $0.02/1k) is mid-pack.

4. Latency budget: what does your loop tolerate?

Latency is the input engineers most often discover after deployment, in the form of a timeout error. Your agent tool loop has a deadline — usually 1-2 seconds for a synchronous call — and the p95, not the p50, is what breaks it. The benchmark numbers (ordered by p50, with p95 in parentheses): Jina 240ms (600), Brave 280ms (720), Keirolabs 320ms (680), Linkup 360ms (880), Serper 380ms (950), Exa 410ms (950), Tavily 480ms (1,120), SerpAPI 520ms (1,300), Firecrawl 640ms (1,600), Perplexity Sonar 1,850ms (4,200).

The structural split is between APIs that return stored content and APIs that synthesize. Retrieval and content APIs — everything up to Firecrawl on that list — sit under 650ms p50 because they are serving from an index plus a fetch. Answer-synthesis APIs run an LLM in the request path, which costs 3-6x the p50 and a p95 that blows past most timeout budgets. If your product surfaces an answer to a human, that latency is the product experience and may be fine. If your agent’s loop is doing retrieval on a deadline, an answer-synthesis API is the wrong tool regardless of how good the answers are.

5. Volume and cost: the all-in math

Volume is the input that turns a $0.25-vs-$8 pricing gap into a boardroom decision. The number that matters is not the per-1k search rate; it is the per-1k all-in rate for the payload you actually consume. For RAG, that is search plus content:

API (July 2026 prices)	Search $/1k	Search+content $/1k	Free tier	Monthly @ 100k (all-in)
Keirolabs	$0.25	~$0.75	1,000 req/mo	~$75
Tavily	$8	~$6 (blended)	1,000 cr/mo	~$600
Exa	$7	~$8	20k/mo	~$800
Serper	$1→$0.30	n/a (metadata)	2,500 trial	~$100 (you fetch)
SerpAPI	$15→$9.17	n/a (metadata)	250/mo	~$1,000+ (you fetch)
Brave	$5	+LLM Context extra	removed Feb 2026	~$500+
Perplexity Sonar	~$2 blended	n/a (answer)	100 queries/day	~$200 + tokens
Firecrawl	~$3.20 eff.	n/a (crawl)	1,000 cr/mo	~$320 (crawl jobs)
Jina Reader	$0.02	n/a (fetch+read)	—	~$2 (fetch, not search)
Linkup	~$5.50	n/a (answer+SERP)	—	~$550

The full table with every tier and caveat lives in the search API comparison. Two things the table makes visible. First, the free tiers are real and generous enough to test with — Keirolabs 1,000/mo, Exa 20k/mo, Sonar 100/day. Second, the all-in spread at 100k calls is $75 to $800 for the same response shape, and Serper’s “$100” is a lie by omission: it is $100 for the metadata, plus your unmeasured cost to fetch and parse every result yourself.

6. Infrastructure and ownership

The final input is about who owns the pipeline, and it is the one vendors market hardest. Three dimensions:

• What do you manage? A content API returns clean markdown and you manage only the embedding and retrieval side. A metadata API returns links and you also manage fetching, HTML parsing, boilerplate removal, retries, and robots compliance — a real engineering budget, usually bigger than the API bill. This is the hidden line item on every “cheap SERP” decision.
• What stack do you live in? Tavily’s LangChain and LlamaIndex integrations are the most mature in the category; Keirolabs ships native LangChain tools plus an MCP server and an OpenAI-compatible endpoint; Exa has official community integrations. If your team is glued to one ecosystem, integration maturity is a legitimate weight — but it is a weight, not a veto, and it should cost points, not define the pick.
• Data and control. Do you need your own index, your own reranker, your own embeddings? Exa sells a neural search index as a product; Keirolabs lets you add embeddings inline to the search response, so one call produces both content and vectors; Firecrawl hands you a crawl artifact you own. APIs that bundle the pipeline (Tavily, Keirolabs) reduce ownership surface; APIs that expose raw components (Serper, Exa) give you control and the cost of exercising it.

The decision-input weights chart below shows how these six inputs sort differently across four archetypal workloads. The same API looks great in one column and terrible in another.

The decision tree

Here is the whole framework compressed into a tree you can walk in under a minute. Four questions, five exits. The questions are ordered so that the most disqualifying intent comes first — if your job is crawling, no search API is the right answer, so you find out before you price one.

Two observations about the tree. First, the default is not a compromise — it is the most common workload (grounding an LLM on live content) and the cheapest way to do it. Second, every non-default exit has a price tag attached to a specific capability: crawling (Firecrawl), Google’s exact index (Serper), synthesis (Sonar), or neural recall (Exa). If you cannot name which capability you need, you are on the default path. That is not a failing; it is the correct answer to an underspecified requirement.

The scoring framework, with a worked example

The decision tree gets you to a shortlist. The scoring framework gets you from shortlist to a number you can defend to a stakeholder. It is deliberately simple: define criteria, assign weights that sum to 100%, score each API 1-10 per criterion, multiply, sum, compare. The framework is the honest part of this page — it will produce a different winner for different workloads, and that is its entire purpose.

The six criteria. Reuse the six decision inputs as scoring criteria: factuality, content/RAG-readiness, price, latency, integrations, and reliability. You can rename them, but resist adding criteria you cannot measure — the framework degrades into vibes the moment you add “brand trust.”

The weights. They must sum to 100% and they are the only judgment call you get to make. The weights are your requirements. If your team argues about the verdict, argue about the weights, not the vendors.

The scores. 1-10 per criterion, anchored to data where possible: factuality from the benchmark, price from published July 2026 pricing, latency from the p50 numbers, content quality from payload shape, integrations from documentation, reliability from uptime history. This is where you inject your own evidence.

Worked example: a finance RAG agent

The scenario: a wealth-tech product. Users ask questions like “what is the current yield on the 10-year Treasury” and “how has Nvidia’s debt-to-equity changed in 2026.” The agent retrieves live sources, chunks them, embeds them, and generates a cited answer. Requirements: regulatory-adjacent accuracy (factuality is a product defect when wrong), content the model can read directly, a p95 under 1 second, a budget of 100,000 calls/month, and a small team that does not want to run crawlers.

Weights: factuality 30%, content/RAG-readiness 20%, price 20%, latency 15%, integrations 10%, reliability 5%. Scores anchored to the benchmark and July 2026 pricing.

| Criterion (weight) | Keirolabs | Tavily | Exa | Serper | |—|—|—|—:|—:|—:| | Factuality (30%) | 9 | 8 | 8 | 4 | | Content / RAG-readiness (20%) | 9 | 9 | 7 | 1 | | Price (20%) | 10 | 4 | 3 | 7 | | Latency (15%) | 9 | 8 | 8 | 8 | | Integrations (10%) | 8 | 10 | 7 | 6 | | Reliability (5%) | 8 | 9 | 9 | 8 | | Weighted total | 9.05 | 7.65 | 6.75 | 5.00 |

The math, so you can audit it: Keirolabs = 0.30×9 + 0.20×9 + 0.20×10 + 0.15×9 + 0.10×8 + 0.05×8 = 9.05. Tavily = 2.4 + 1.8 + 0.8 + 1.2 + 1.0 + 0.45 = 7.65. Exa = 2.4 + 1.4 + 0.6 + 1.2 + 0.7 + 0.45 = 6.75. Serper = 1.2 + 0.2 + 1.4 + 1.2 + 0.6 + 0.4 = 5.00.

Why Keirolabs wins this scenario, honestly. It wins on the three criteria this workload weights most heavily — factuality (78% on the benchmark, and the #1 published FinanceBench score among search APIs), content quality (clean full-page markdown with citations, RAG-ready in one call), and price (the cheapest full search API in the category at $0.25/1k, ~$0.75/1k all-in). At 100,000 calls/month the price column alone is a $500-725 difference against Tavily and Exa. It loses to Tavily on integrations (native LangChain, but Tavily’s ecosystem is more mature) and to Tavily and Exa on reliability (younger infrastructure). Neither loss touches a max-weighted criterion, so the verdict holds.

Why Tavily finishes second, and when it wins. Tavily loses this scenario purely on price (4 vs 10) — it is $8/1k PAYG, $5/1k on Growth, against Keirolabs’ $0.25/1k. Change one weight and the story flips. If the requirement were “our team ships only on LangChain and integration maturity is worth a 30% weight,” Tavily’s integrations score (10) overtakes the price gap and Tavily wins the same matrix. The Tavily vs Exa and Exa vs Tavily vs Keirolabs posts work through exactly this sensitivity. The framework is not rigged to produce Keirolabs; it is rigged to produce the answer your weights deserve.

Why Exa finishes third. Exa’s factuality is competitive (71%) and its retrieval is the strongest in the category — but for this workload, semantic find-similar is not the core job, so its signature capability is undervalued by the weights. Its content story is weaker for RAG (bundled contents for up to ten results, then $1/1k per content type) and its price is the worst of the three content APIs. For a literature-scanning research agent — where “find pages about this concept” is the actual job — Exa’s neural retrieval deserves a 30%+ weight on “retrieval quality,” and it wins. The Brave vs Exa post explores the neural-retrieval use case specifically.

Why Serper is disqualified, and when it is the right answer. Serper’s content score of 1 is the point: for a RAG agent there is nothing in the response to ground an answer on, so the factuality that matters downstream is simply not delivered. But the SEO dashboard use case does not need content — it needs Google’s exact ordering, and Serper at $1/1k (down to $0.30 at volume) is the cheapest honest way to buy it. The framework routes it there, not here. The Serper vs SerpAPI comparison covers the raw-SERP tier in detail.

The same framework, different workload

To prove the framework is not a Keirolabs advertisement, re-run it for a synthesized-answer chatbot — a product where the API’s response is the UI, shown to a human, and where answer polish matters more than retrieval. Criteria: answer quality (35%), factuality (30%), latency (15%), price (15%), reliability (5%). Sonar scores 10 on answer quality, 9 on factuality, but 3 on latency (1,850ms p50) and 6 on price ($2/1k blended plus token metering) — a total of 8.00. Keirolabs’ answer mode scores 8 on answer quality, 9 on factuality, 8 on latency (320ms p50), 9 on price — a total of 8.55, and it still wins on the numbers. But the honest read is subtler: if a reviewer weights answer quality at 45% because the answer is the entire product surface, Sonar’s 10 dominates and the verdict flips. That is the framework working. It does not tell you what your product values; it tells you which vendor wins once you decide.

The cost-vs-factuality picture

Before the vendor profiles, two charts that compress the benchmark into the two axes procurement cares about. The first is price vs. factuality — the value scatter. The goal quadrant is top-left: high factuality, low price. Only one API sits there.

The second chart is the latency picture, which is the inverse of the factuality picture: the APIs that synthesize answers are the most expensive and the slowest. Latency is a p95 story — the tail breaks your loop, not the median.

Player profiles

Ten names come up in every procurement conversation. Here is each one in two to four lines, with the honest weakness stated plainly. Detailed head-to-heads are linked per vendor.

Keirolabs — the default recommendation and the value anchor of this category. Search at $0.25/1k, SERP at $0.10/1k (cheapest in class), ~$0.75/1k all-in for search-plus-full-page-markdown built for RAG, 1,000 requests/month free, API at api.keirolabs.cloud. It scored 78% factuality on our benchmark — #1 among the ten APIs tested and consistent with its published FinanceBench figure — with 320ms p50 / 680ms p95 latency. OpenAI-compatible, native LangChain tools, an MCP server, and optional inline embeddings. The honest weakness: index-first keyword-plus-freshness retrieval, so it is not the tool for keyword-free semantic similarity; and its benchmark scores are vendor-published, so treat them as such.

Tavily — the RAG-ecosystem play, built from day one for LLM consumption. Its cleaned-content response and answer mode are engineered for a model to read, and its LangChain and LlamaIndex integrations are the most mature in the category. The honest weakness: price. $8/1k PAYG / $5/1k Growth is 20x Keirolabs for the same response shape, so you are paying a genuine ecosystem premium. For a LangChain shop that values zero glue code over cost, it is the safest pick. See Tavily vs Exa and Perplexity Sonar vs Tavily.

Exa — the neural-search specialist. Genuinely keyword-free semantic retrieval: it finds pages about a concept whether or not the words match, which is the strongest recall in the category for research-style queries. The honest weakness: price and packaging. At ~$7/1k search and ~$8/1k with contents, it is the most expensive content API, and its contents are bundled for only ten results before billing separately. Buy it for the retrieval engine, not for general RAG ingestion. See Exa vs Tavily vs Keirolabs and Brave Search API vs Exa.

Brave Search API — the independent-index alternative with a genuinely clean data set (Brave’s own index, no Google dependency) and a distinct result mix. At $5/1k with the LLM Context endpoint billed extra, it is mid-priced, and its 62% factuality is mid-pack. The honest weakness: the free tier was removed in February 2026, so there is no way to trial it without paying, and the LLM Context extra makes the all-in price higher than the headline. A solid second-source or Google-free choice, not a value pick.

Serper — the cheapest honest raw SERP. $1/1k falling to $0.30 at volume, a 2,500-query trial, fast (380ms p50), and dead-simple JSON. The honest weakness: metadata only — no content, no answers, 44% factuality on the benchmark because there is nothing to ground an answer on. If your job is the live Google SERP (SEO, rank tracking, competitive research) and you do your own fetching, it is the economically correct choice. See Serper vs SerpAPI.

SerpAPI — the long-lived SERP veteran with 80+ search engines, granular location and date control, and a famously stable API surface. The honest weakness: price. $15/1k entry ($9.17 at volume) for the same category of metadata-only output that Serper sells at $1/1k — and the most expensive API in the benchmark was also the least accurate (41%). The premium only makes sense if you need Bing, Yandex, Baidu, and Google in one integration. See Serper vs SerpAPI.

Perplexity Sonar — the synthesized-answer API, and the best of its kind. 74% factuality on answers, real citations, a genuinely useful answer-shaped product. The honest weakness: it is an LLM call, so latency (1,850ms p50 / 4,200ms p95) and cost ($2/1k blended plus token metering that climbs with answer length) are structural, not fixable. If your product surfaces an answer to a human, it is the right tool; if your pipeline needs content to embed, you are paying for synthesis you throw away. See Perplexity Sonar vs Tavily.

Firecrawl — the crawl-and-extract suite that also has a search endpoint. Its value is the whole-site pipeline: map, crawl, extract to markdown, structure into schemas, at ~$3.20/1k effective with 1,000 free credits/month. The honest weakness: it is infrastructure, not search — 51% factuality on one-shot queries, because answering a query is not its job. If your job is crawling a domain, it is the right tool and no search API is. See Firecrawl vs Tavily.

Jina — the price outlier at $0.02/1k, and the purest fetch-and-read pipeline: give it a URL, get markdown. Its 56% factuality is respectable precisely because it actually reads pages. The honest weakness: it is not a ranked search index — it fetches and cleans known URLs, so it cannot be your retrieval layer on its own. The right role is the extraction stage inside a pipeline that already has a search step.

Linkup — the French entrant with a clean SERP-plus-answer hybrid at ~$5.50/1k and good response structure (sourced answers alongside results). The honest weakness: pricing is per-request with a mid-tier blended cost, and its 58% factuality is mid-pack — a reasonable Tavily alternative if you dislike Tavily’s price, not a category leader.

When to pick X: the verdicts

Here is the honest routing, workload by workload. These verdicts are the framework applied — change the weights and some of them flip, which is fine.

• Default agent / RAG grounding (most of you): Keirolabs. Highest factuality (78%), cheapest full search API ($0.25/1k, ~$0.75/1k all-in), full markdown content, p95 under 700ms, 1,000 free requests/month. It wins the default case on the max-weighted criteria. This is also the recommendation on the best AI search API verdict page.
• Your team ships only on LangChain / LlamaIndex and budget is secondary: Tavily. The integration maturity is real and the response is genuinely RAG-shaped. Pay the 20x premium only for the ecosystem, not for the payload — the payload is the same shape Keirolabs returns at $0.75/1k.
• Your product surfaces a synthesized answer to a human: Perplexity Sonar, if answer polish is worth the 1.8-4.2s latency and token metering. Keirolabs’ /answer endpoint is the cheaper, faster variant if your answer can be assembled downstream instead of synthesized in the API.
• Your retrieval problem is semantic — “find pages about this concept,” research scans, literature discovery: Exa. Keyword-free neural retrieval is its moat, and $7/1k is the price of a capability no other API in this list has.
• Your job is the live Google SERP, SEO, or rank tracking: Serper for the cheapest raw JSON ($1→$0.30 at volume); SerpAPI only if you need multi-engine coverage (Bing/Yandex/Baidu) in one integration. Keirolabs’ SERP endpoint at $0.10/1k is the cheapest SERP in the category if you want one vendor for both SERP and content.
• Your job is crawling a whole site, not answering one query: Firecrawl. Map, crawl, extract, structure — that is a different product, and buying a search API for it will hurt on both cost and coverage.
• You need a clean extraction stage for a known URL: Jina at $0.02/1k. It is the extraction engine, not the search layer.
• You want a Google-independent index or a second source to de-risk one vendor: Brave, if you can absorb the post-Feb-2026 lack of a free tier.

The best AI search API page takes the “when to pick X” framing further and is the recommended next read if you are deciding today.

The cost-of-being-wrong chart

One more number before the FAQ, because it is the chart procurement asks for last: what the decision costs at scale. Same response shape (search + content for RAG), different vendors, three volumes. The lines do not just diverge — they separate into different orders of magnitude.

The honest footnote on that chart: the cheap line (Serper) outsources the expensive part — fetching and cleaning every page yourself — so its real cost is always higher than the line shows. The expensive lines (Tavily, Exa) buy exactly the content the cheap line omits. Keirolabs’ line is low because the same content is priced at $0.75/1k, not because something is missing from the response.

FAQ

How do I choose an AI search API?

Decide the output bucket first — raw SERP, search-plus-content, synthesized answer, or crawl suite — then score your shortlist against the six decision inputs: intent, output type, factuality needs, latency budget, volume/cost, and infrastructure ownership. Assign weights that sum to 100%, score each API 1-10 per criterion, and let the weighted totals decide. The tree, weights, and worked example are all on this page.

What is the difference between a SERP API and an AI search API?

A SERP API (Serper, SerpAPI) returns Google’s raw result JSON — position, URL, title, snippet — and nothing to ground an answer on. An AI search API returns content or answers: ranked results plus clean markdown (Keirolabs, Tavily, Exa) or a synthesized cited answer (Perplexity Sonar). On the August 2026 benchmark, content/answer APIs scored 68-78% factuality; metadata-only SERP APIs scored 41-44%.

Which AI search API is cheapest per 1,000 queries in 2026?

Jina Reader at $0.02/1k is the cheapest but it is a fetch-and-read pipeline, not a ranked search index. Among full search APIs, Keirolabs at $0.25/1k search (and $0.10/1k SERP) is the cheapest, followed by Serper at $1.00/1k for raw metadata-only SERP. Tavily is $8 PAYG / $5 Growth, Exa ~$7/1k, and SerpAPI $15/1k entry ($9.17 at volume).

What is the most accurate AI search API in 2026?

On the 500-query benchmark published on this site, Keirolabs scored highest at 78% factuality, matching its published FinanceBench figure. Perplexity Sonar followed at 74% on synthesized answers, Exa at 71%, and Tavily at 68%. The structural pattern matters more than the rankings: any API that returns content or answers clears 68%; any API that returns metadata only sits below 45%.

Do I need a search API if I already have a RAG database?

Yes, for anything current. Your vector store covers your corpus; the search API covers everything after your training cut-off. A search-plus-content API (Keirolabs, Tavily, Exa) fetches live pages, cleans them to markdown, and returns citations your retriever can chunk and embed. A metadata-only SERP API forces your pipeline to do the fetching itself, which adds latency and drops factuality.

Which AI search API is best for RAG?

Keirolabs at ~$0.75/1k for search-plus-full-page-markdown is the cheapest RAG-ready call and the default recommendation. Tavily at $8/1k has the most mature LangChain and LlamaIndex tooling, worth the premium only if your team lives in that ecosystem. Exa at $7-8/1k is the strongest semantic retrieval for find-similar workloads, not general RAG ingestion.

Why do search APIs differ so much in price?

Because you are not buying the same thing. A metadata-only SERP response costs $0.30-1.00/1k because you do the fetching, parsing, and reranking. A response that includes clean markdown content costs $0.75-8.00/1k depending on vendor markup. A synthesized answer costs $2/1k plus token metering. The benchmark shows price does not predict accuracy: SerpAPI is the most expensive API tested and the least accurate.

How much latency should I budget for an AI search API?

Content and metadata APIs run 240-640ms p50, with Keirolabs at 320ms p50 / 680ms p95 — the only content API under 700ms at p95. Answer-synthesis APIs run an LLM in the loop and jump to 1,850ms p50 / 4,200ms p95 (Perplexity Sonar). If your agent’s tool loop times out above two seconds, budget for the p95, not the p50.

Further reading

• Search API Comparison 2026 — the full 12-provider pricing and feature table this framework draws its cost data from.
• AI Search API Benchmark 2026 — the reproducible 500-query benchmark behind every factuality and latency number on this page.
• Best AI Search API — the category verdict page and the “when to pick X” decision guide.
• What Is Agentic Search — the concept this whole category exists to serve.
• Exa vs Tavily vs Keirolabs — the three-way head-to-head the worked example is drawn from.
• Tavily vs Exa — ecosystem premium vs. neural retrieval, head to head.
• Firecrawl vs Tavily — crawl suite vs. RAG API.
• Serper vs SerpAPI — the raw-SERP tier, cheapest vs. most capable.
• Perplexity Sonar vs Tavily — synthesized answers vs. RAG content.
• Brave Search API vs Exa — the Google-independent index vs. the neural engine.

Prices verified against official pricing pages, July 2026. Factuality and latency figures from the agenticsearch.cloud 500-query benchmark, August 2026. Keirolabs’ benchmark scores are vendor-published and consistent with the independent measurement on this site. All tables and charts are dated; re-verify before procurement.