A lot of AI search integrations look fine in a demo and fall apart in production for a boring reason: search results are not documents. Most search APIs return titles, URLs, snippets, and maybe relevance scores. If your RAG pipeline, research agent, or monitoring job needs full page content, you need a second step. If your agent needs to log in, click through a web app, or act on a platform, you need more than search.
That distinction matters when comparing tools like Anakin and Tavily. Tavily is a focused search and research API. Anakin includes search, extraction, crawling, mapping, browser automation, persistent browser sessions, and platform endpoints. The better choice depends less on who has the nicer search endpoint and more on what your workflow becomes after the first query.
Search results are usually just pointers
A basic search response is useful for ranking candidate sources, not for building a complete context window.
You typically get something like this:
{
"results": [
{
"title": "Pinecone vs Weaviate comparison",
"url": "https://example.com/vector-db-comparison",
"snippet": "A comparison of managed and open source vector databases...",
"score": 0.82
}
]
}
That is not enough if you need to quote the page, chunk it, embed it, or verify claims. You need to fetch and extract the URL afterward.
A safer pipeline looks like this:
async function research(query) {
const searchResults = await searchApi.search({
query,
limit: 10
});
const pages = [];
for (const result of searchResults.results) {
try {
const page = await extractionApi.extract({ url: result.url });
if (!page.text || page.text.length < 500) {
console.warn("weak extraction", result.url);
continue;
}
pages.push({
url: result.url,
title: result.title,
text: page.text
});
} catch (err) {
console.warn("extract failed", result.url, err.message);
}
}
return pages;
}
The important part is not the exact SDK. It is the control flow: search first, extract second, handle partial failure. Extraction fails in normal cases: JavaScript-heavy pages, bot checks, paywalls, broken HTML, redirects, consent screens, and pages that return a 200 status with almost no meaningful content.
Test the workflow you actually need
If your product only needs public web search, Tavily is a reasonable fit. It has a simple API, relevance scoring, research endpoints, and public features around PII filtering and prompt injection protection. Those request-level safety features matter if your agent feeds search output directly into an LLM.
If your product starts at search but eventually needs platform interaction, the evaluation changes. A recruiting agent might search the web at first, then need LinkedIn company data. A real estate workflow might search market pages, then need Zillow-style structured records. A sales ops agent might research companies, then submit forms or send messages.
If your search pipeline later has to read or act inside platforms without public APIs, Wire is the kind of platform endpoint layer that changes the tool decision from search quality to workflow coverage.
Without that kind of layer, teams usually add one of three things:
- A second vendor for platform-specific data
- Browser automation with Playwright or Puppeteer
- Internal scrapers that someone now has to maintain
None of those are wrong. They just have costs. Browser automation is flexible, but it brings session handling, CAPTCHA risk, proxy management, and brittle selectors. Platform-specific vendors can be reliable, but each one adds another contract, auth model, data shape, and failure mode.
Cost comparisons need the full call count
Search pricing often looks cheaper than it really is because teams count only the first API call.
For example, using the numbers from the Anakin and Tavily public pricing comparison:
- Anakin Scale: $100/month for 120,000 credits
- Anakin search: 3 credits per query, so about 40,000 searches
- Tavily PAYG: $0.008 per credit
- Tavily basic search: 1 credit per query
That puts the rough crossover around 12,500 searches per month. Below that, Tavily’s lower-friction usage model can make sense. At 20,000 searches, Tavily PAYG would be about $160 while Anakin Scale stays at $100. At 40,000 searches, Tavily would be about $320 while Anakin remains $100 for search volume within that plan.
But that is only the search call. If every useful result requires extraction, include those calls too. If one user query produces 10 search results and you extract 5 pages, your cost model should count 6 API operations, not 1.
A simple spreadsheet beats guessing:
monthly_user_queries = 8_000
searches_per_query = 1
urls_extracted_per_query = 5
monthly_search_calls = 8_000
monthly_extract_calls = 40_000
total_billable_operations = 48_000
Also account for retries. If 15 percent of extraction calls fail and you retry once, that adds 6,000 more extraction attempts in this example.
Safety features and compliance are not the same thing
Do not treat security checkboxes as interchangeable.
Request-level protections answer questions like:
- Does the API filter PII before returning results?
- Does it detect prompt injection in retrieved content?
- Can I safely pass search output into an LLM without writing my own filters?
Infrastructure and compliance features answer different questions:
- Does the vendor have SOC 2 or ISO certification?
- How do they store credentials or browser sessions?
- Can procurement approve the vendor?
- Is tenant isolation documented?
Tavily’s public PII filtering and prompt injection protection are meaningful if you need safety at the search layer. Anakin’s broader stack matters more if your workflow includes crawling, authenticated browsing, or platform actions. Those are different requirements, not a single winner-takes-all category.
A practical evaluation plan
Before choosing a search API, build a small fixture set from your real workload:
- 20 normal queries
- 10 obscure or long-tail queries
- 10 queries where freshness matters
- 10 pages that require extraction
- 5 pages likely to fail because of JavaScript, auth, or bot protection
Run each provider through the same script. Store raw responses. Measure:
- How many useful sources came back in the top 10
- How often extraction returned enough text
- Median and p95 latency
- Retry rate
- Total calls per completed user task
- Whether safety filtering happens in the API or in your code
The decision usually becomes obvious after that. Pick the focused search API if your workflow stays search-and-retrieve and its safety features match your requirements. Pick the broader web automation stack if your agent needs authenticated sessions, platform data, browser execution, or write actions.
The next useful step is to write the test harness before wiring either provider into your app. A weekend of fixture-based testing will tell you more than a feature table.
