An AI agent can find the right webpage and still miss the data it needs.
Some details are embedded in a page’s code and may not appear in the text a tool returns. Others are spread across subpages. To gather everything it needs, an agent may have to extract that underlying data or follow links through the site.
The web tools you choose shape how much information reaches the agent and what it can do with it.
This Apify vs. Exa comparison tests how well each tool supports web data access for AI agents when a task requires accurate, complete information.
How do Apify and Exa differ? #
Exa gives AI agents tools for searching the web and retrieving page content. An agent can describe what it needs, refine its search, and fetch relevant pages. With Exa’s advanced search tool, it can also filter results by domain and date, control content freshness, and crawl subpages.
Apify provides access to web data through Actors, programs that run in the cloud to perform specific tasks. Some search the web and retrieve pages. Others collect particular types of data, such as product catalogs, social media posts, or real estate listings.
Both providers use Model Context Protocol (MCP), a standard that lets AI applications connect to external tools. Through Apify’s MCP server, an agent can find a suitable Actor, check its inputs, run it, and retrieve the results.
Apify’s RAG Web Browser searches Google and retrieves pages, while Web Fetch retrieves content from individual URLs. An agent can therefore use Apify for search without adding a separate provider.
The distinction lies in how the agent adapts to the task. Exa’s search tools let it refine how it searches and what content it retrieves. Apify lets it choose an Actor built for the website or type of data it needs.
What I tested and why #
I tested how well Apify and Exa help an AI agent find relevant sources, retrieve precise details, and collect a complete dataset. I used Allbirds, a footwear brand, as the subject of a competitor research task that covered all three: finding independent reviews, checking stock at its US store, and collecting its public catalog.
ChatGPT served as the agent, handling the reasoning while each provider supplied the web tools. I ran the tests in separate Work chats using GPT-5.6 Sol at Extra High reasoning, with identical prompts apart from the provider name. Each chat could access the web only through its assigned provider, and neither used separate research agents. The results reflect both the available tools and how ChatGPT chose to use them.
1. Search and discovery: which finds stronger evidence? #
Let’s start with a question you might ask during competitor research: what do people who’ve worn the Allbirds Tree Runner Go say about its comfort and durability?
To answer it, an agent needs to open each review, confirm the model tested, and find firsthand evidence about both qualities. A review that describes the fit but says little about how the shoe holds up leaves part of the question unanswered.
I connected ChatGPT to each provider through a separate MCP app. Here’s how to set up the same comparison:
- Open Settings in ChatGPT. Go toSecurity and login , then turn onDeveloper mode (availability depends on your account tier and workspace policy).
- Open ChatGPT Plugins and select the** plus (+)** button. Name the connectionApify benchmark and describe it asSearch and collect web data with Apify .
- Under Connection , enter the Apify URL below, create the connection, and complete any authorization step.
- Repeat the process for Exa benchmark , using the descriptionSearch and retrieve webpages with Exa and the Exa URL below.
- Review each connection’s tool list. Then start two new chats with the same model and reasoning setting. I used GPT-5.6 Sol with Extra High reasoning.
- Use the tools menu beneath the message box to select only Exa benchmark in one chat and onlyApify benchmark in the other.
Apify connection URL
https://mcp.apify.com?tools=actors,apify/rag-web-browser,apify/web-fetch
Exa connection URL
https://mcp.exa.ai/mcp?tools=web_search_exa,web_fetch_exa,web_search_advanced_exa
The Exa URL selects three search and retrieval tools. The Apify URL selects RAG Web Browser, Web Fetch, and tools to find and run Actors. Apify also includes helpers to check run status and retrieve output when Actor tools are enabled.
With the connections ready, paste this prompt into each chat. Replace [APP NAME] with Exa in the first chat and Apify in the second:
Use only the [APP NAME] benchmark app for all external web access. Do not use built-in web search or another app. ChatGPT is the only reasoning agent in this test.
Find up to five independent, hands-on reviews of the Allbirds Tree Runner Go published since January 1, 2024.
Every included source must discuss both comfort and durability. Open each source before including it.
Return:
- article title;
- publisher;
- publication date;
- exact product reviewed;
- direct URL;
- brief evidence about comfort;
- brief evidence about durability.
Exclude Allbirds pages, retailer listings, copied reviews, search snippets and sources that do not show first-hand testing. Do not include a weak source merely to reach five results.
State every [APP NAME] tool used. Do not invent missing information.
The prompt is deliberately strict. The date keeps the research current. The firsthand requirement filters out marketing copy and articles that repeat product claims. Requiring both comfort and durability tests whether each source supports the full answer.
Opening every source and confirming the exact model helps the agent avoid relying on snippets or confusing the Tree Runner Go with another shoe.
I asked for up to five reviews, not exactly five, so ChatGPT could stop if the remaining sources were weak.
You can inspect the complete tool calls and answers in my Exa benchmark chat and Apify benchmark chat.
What the review searches returned
Both chats returned four reviews, but their sources offered different evidence.
Exa’s result:
The Exa chat found reviews from PEOPLE, Forbes Vetted, Woman & Home, and WeTried.it.
PEOPLE provided the strongest durability evidence in either run. Its tester wore the shoes frequently for a month and reported minimal wear. Forbes compared the Go with the original Tree Runner and found the new model sturdier.
The other tests were shorter. Woman & Home covered five days of long walks and rough terrain. WeTried.it found the redesigned knit better suited to daily wear.
Together, these sources gave ChatGPT clear comfort evidence and a firmer basis for discussing durability. It used web_search_advanced_exa, web_search_exa, and web_fetch_exa.
Apify’s result
Apify’s chat also found four reviews. Woman & Home and WeTried.it appeared in both results. Apify added Run Oregon and the New York Post.
Those sources supported the comfort claim well. Run Oregon tested the shoes over several long days of walking. The New York Post used them for walks, commutes, errands, and other daily activities.
The durability evidence was more limited. Run Oregon covered one trip, while the New York Post tested the shoes for one week. Both described how the shoes handled short periods of use, but neither established how they held up over longer periods.
The answer made those limits clear. It also caught a Woman & Home purchase link that led to the Utility version. It excluded a CNN article that lacked firsthand testing and noted that the PEOPLE review was inaccessible during this run.
ChatGPT used apify/rag-web-browser, apify/web-fetch, get-dataset-items, and get-actor-run.
Exa had the edge in this test because its sources offered stronger evidence about durability. Apify still produced a careful and useful answer, but its sources covered shorter test periods.
The next test asks what happens when an agent needs information that's harder to retrieve from the page itself.
2. Extraction depth: can they verify what is actually in stock? #
For the next test, I asked ChatGPT to find Tree Runner Go listings on the official Allbirds US store, check their prices, and identify which sizes were in stock.
Product titles and prices often appear in the page text. Size availability can be harder to confirm because a page may list both available sizes and sold-out sizes. The agent needs each size’s stock status, which may require reading product data embedded in the page.
Reopen the chats from the first test and confirm that each still has the correct app selected. Then send this prompt in both:
Continue the same research task.
Now verify the current Tree Runner Go range directly from the official Allbirds US store.
Find every publicly listed Tree Runner Go product page you can access.
For each listing, return:
- product title;
- men’s or women’s category;
- colour;
- current price;
- original or compare-at price, if shown;
- available sizes;
- availability status;
- direct product URL;
- source evidence.
Use only official Allbirds pages for these product fields. Search snippets and third-party prices do not count as evidence.
Mark any field you cannot verify as unavailable. Do not infer it.
State every additional provider tool used.
Finding every relevant product page tested coverage of the range. Identifying which sizes were in stock tested how much detail each provider could retrieve.
The prompt used “unavailable” for information ChatGPT couldn’t verify. Here, that means unknown, not out of stock. The agent had to report uncertainty instead of inferring a stock status.
I checked both answers against a separate reference snapshot containing product availability. I kept its source, totals, and product URLs out of both chats so ChatGPT would find the products independently.
I collected the reference snapshot on September 5, 2026. Stock changes quickly, so the figures below describe that collection period.
What the product checks returned
Both chats found all 11 product pages in the reference snapshot. They also reported the correct prices: $120 for the eight standard Tree Runner Go listings and $130 for the three Utility listings.
The difference appeared when they checked which sizes were in stock.
Exa’s result:
Exa retrieved the product pages, prices, and labels such as “Final Few” and “Select A Size.” But those labels didn’t reveal which sizes were available.
ChatGPT left the size fields unverified instead of guessing. The answer still couldn’t tell a shopper whether their size was in stock.
Apify’s result:
Apify retrieved the structured product data behind the listings and reported stock status for all 143 variants. A variant is a specific version of a product, such as a particular size. Every status matched the reference snapshot.
At the time of collection, only two variants were in stock: size 5 in Women’s Medium Grey and size 5 in Women’s Rustic Brown. The other 141 were out of stock.
Both providers found the listings and verified their prices. Only Apify confirmed which sizes were available, giving it the edge in this test.
3. Structured collection: can they deliver a complete catalog? #
For the final test, I expanded the request to cover the full public Allbirds US catalog.
I asked for a downloadable CSV with one row for every variant. A single row per product could hide differences in size, color, price, SKU, and availability. Keeping each variant separate preserves those details for later analysis.
A large file can look convincing even when half the catalog is missing. I therefore asked each chat to keep collecting until no new products remained and report totals, duplicates, failed pages, and any truncated output.
For this test only, I limited each chat to one paid collection job and a maximum spend of $2. A job can keep running after a ChatGPT tool call times out, so starting another could repeat the work and increase the cost. I instructed both chats to keep checking the original job if that happened.
Here is the prompt I sent to both chats:
Continue the same task.
Expand the research into a complete snapshot of the public Allbirds US product catalogue.
Collect every publicly listed product and every variant. Return one row per variant with:
- product ID;
- product title;
- handle;
- product type;
- product URL;
- variant ID;
- variant title;
- SKU;
- price;
- compare-at price;
- availability;
- option values.
Continue through the catalogue until no new products remain. Do not present a search sample or partial result as the complete catalogue.
Save the results as a downloadable CSV file. If the provider stores the complete output separately, retrieve it or provide the dataset ID and export link.
Also report:
- total unique products;
- total unique variants;
- duplicate records;
- failed or inaccessible pages;
- any truncated output;
- UTC collection time;
- every provider tool used.
Use any suitable tool available through the enabled provider app. Do not use another web-access service.
Do not start a replacement run if a tool call times out while the task continues server-side. Poll the same run. Run no more than one paid collection job. Do not spend more than $2. If the task cannot be completed within that limit, stop and report why.
The totals and error checks would help reveal coverage gaps. The CSV would also show whether each provider could deliver data that remained useful outside the chat.
I compared the results with the same hidden reference snapshot used in the stock test. It contained 294 unique products and 2,857 unique variants. Neither chat received those figures before attempting the task.
What the catalog collections returned
Exa’s results
Exa didn’t produce a downloadable CSV or any variant data. It found individual product pages but didn’t collect the catalog as structured data through the MCP connection I tested.
Apify’s result:
Apify launched Shopify Product Scraper and returned a downloadable CSV with 142 unique products and 1,434 unique variants.
The dataset covered roughly half of the reference catalog:
| Measure | Returned in CSV | Reference snapshot | Coverage |
|---|---|---|---|
| Unique products | 142 | 294 | 48.3% |
| Unique variants | 1,434 | 2,857 | 50.2% |
The run used the full $2 budget and returned SUCCEEDED. That means it finished successfully, not that it collected the entire catalog. Its completion record explicitly classified the output as PARTIAL and CAPPED_BUDGET. ChatGPT correctly labeled the CSV as partial.
Apify delivered the stronger result in this stage because it gave the agent a working path to collect and export reusable variant data. Neither provider delivered the complete catalog within the test’s constraints.
How accurate was Apify’s partial dataset? #
I matched the CSV’s 1,434 variants to the reference by variant ID. Every ID was unique and present in the reference.
Before comparing the fields, I accounted for formatting differences. For example, 0.8 and 0.80 counted as the same price, while true and True represented the same stock status. I also aligned the JSON option values with the reference columns.
The reference collection took place about 11 minutes before the Apify run, so stock could’ve changed in between. Even so, all 1,434 availability values matched: 322 variants in stock and 1,112 out of stock.
Each row contained 12 fields, giving me 17,208 individual values to compare:
| Validation check | Result |
|---|---|
| Returned variant IDs matched to the reference | 1,434 of 1,434 |
| Duplicate variant IDs | 0 |
| Matching values across all 12 fields | 17,189 of 17,208 (99.9%) |
| Matching values excluding compare-at price | 15,774 of 15,774 (100%) |
| Existing compare-at prices captured | 0 of 19 |
| Reference variants absent from the CSV | 1,423 |
All 19 mismatches involved compare-at prices, the reference prices a store may display alongside its selling prices. Those values were missing from the output. The other 1,415 variants had no compare-at price in the reference, so their missing values counted as matches.
Across the other 11 fields, every value matched. The returned records were therefore highly consistent with the snapshot, but the 99.9% figure applies only to the rows collected. It excludes the 1,423 variants missing from the CSV and does not show how well the Actor captured existing compare-at prices.
Speed and cost: how did the workflows compare? #
Exa answered the first two prompts faster, while Apify’s search and page retrieval tools recorded lower usage costs in this benchmark.
| Stage | Exa | Apify |
|---|---|---|
| Independent review research | 5m 22s | 16m 9s |
| Official product verification | 5m 17s | 13m 22s |
| Catalogue collection | 52s (no dataset) | 5m 56s (includes dataset) |
| Total across all three stages | 11m 31s | 35m 27s |
ChatGPT reported these times for one run of each prompt. They include reasoning, provider tool calls, and waiting, so they measure the full workflow up to the final response.
Exa found stronger review evidence but couldn’t confirm size availability or produce a catalog dataset. Apify took longer, completed the stock check, and returned a downloadable catalog dataset. Exa’s shorter catalog response doesn’t mean faster collection.
The dashboards grouped usage costs across the benchmark by tool:
| Recorded usage | Exa | Apify |
|---|---|---|
| Search and page retrieval | $0.47 | Approximately $0.32 |
| Catalogue collection Actor | No collection job or dataset | $1.99 displayed |
| Total dashboard usage | $0.47 | $2.32 |
These figures show recorded usage costs. Free credits and account discounts can change what you actually pay. Apify’s RAG Web Browser accounted for $0.29, and Web Fetch added $0.03. Together, they cost approximately $0.32, about 32% less than Exa’s $0.47 for search and page retrieval.
Shopify Product Scraper explains most of Apify’s $2.32 total. The dashboard displayed $1.99 for that Actor, while its completion record reported reaching the $2 budget cap. The run returned 142 products and 1,434 variants in the partial CSV.
Exa didn't launch a catalog collection job; therefore, it produced no dataset and had no equivalent collection charge. Its lower overall total reflects that missing run. For search and page retrieval across this benchmark, Apify recorded the lower cost.
Conclusion #
Exa was slightly better at finding useful sources, but Apify gave the agent more to work with as the tasks grew more demanding. It verified stock for individual sizes and collected structured catalog data, making it the right choice for broad web data access.
Apify’s MCP server comes preconfigured with established Actors, but you can build a custom multipurpose Actor around your own needs. And if it could help others, you can publish it on Apify Store and monetize it.
To try it with your own workflow, sign up for Apify and utilize $5 in free monthly credits.