How much does Exa Websets cost?

Exa Websets uses a free pricing model.

What are the main advantages of Exa Websets?

Massive Scale: Capable of analyzing 1,000+ web pages simultaneously to find niche entities that traditional search engines miss.. Neural Semantic Search: Understands conceptual queries (e.g., 'Series A AgTech startups in the Midwest') rather than just matching keywords.. Agentic Verification: Uses built-in AI agents to verify that each result strictly matches user-defined criteria before including it in a collection.. Rich Automation: Supports recurring searches to keep lists fresh and catch new companies or people as they emerge on the web.. Clean Structured Output: Exports data in ready-to-use CSV formats or via API with pre-populated fields like funding, industry, and contact info..

What are the drawbacks of Exa Websets?

Asynchronous Latency: High-depth searches are intentionally 'slow' (taking minutes rather than seconds) to ensure comprehensive verification.. Credit-Based Pricing: Heavy users may find the credit consumption high for deep enrichment tasks compared to static database alternatives.. Technical Learning Curve: While the UI is clean, fully utilizing the event-driven API and webhooks requires significant developer effort..

Exa Websets

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Pricing: Free Company: exa-labs Last verified: 2026-05-09

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Updated May 10, 2026

TL;DR

Exa Websets is an AI-native research and data enrichment engine designed to build structured, verified collections of companies, people, and research papers from the live web. Unlike traditional keyword-based scrapers, it uses neural search to understand complex natural language queries and automatically enriches results with custom data points like CEO names or funding stages.

What Users Actually Pay

No user-reported pricing yet.

Our Take

Exa Websets represents a significant leap from search-as-a-service to research-as-a-service, positioning itself as a more powerful, agentic alternative to tools like OpenAI's Deep Research or traditional B2B lead platforms like Apollo. By leveraging a proprietary neural index of tens of billions of pages, it avoids the 'knowledge cutoff' limitations of LLMs while providing structured, tabular data that is ready for CRM or database ingestion. Its ability to process over 1,000 pages simultaneously per query makes it a heavyweight tool for power users in GTM (Go-To-Market), recruiting, and market research. The product's core strength lies in its 'search-verify-enrich' loop, where AI agents act as filters to ensure data accuracy—a major pain point in automated web scraping. However, the asynchronous nature of these 'deep' searches means results aren't instantaneous, which might frustrate users accustomed to the sub-second response times of standard search APIs. It is best suited for engineering and ops teams who need to automate complex, high-volume research tasks that would otherwise require human researchers or fragile custom scraper scripts. While the dashboard is user-friendly, the product truly shines when integrated into developer workflows. Its market position is currently unique; it is neither a simple SERP API nor a broad general-purpose AI, but a specialized utility for high-fidelity web entity discovery. As the AI agent ecosystem matures, Websets is likely to become a foundational component for 'research agents' that require a source of truth broader and more current than any single LLM.

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Pros

+ Massive Scale: Capable of analyzing 1,000+ web pages simultaneously to find niche entities that traditional search engines miss.
+ Neural Semantic Search: Understands conceptual queries (e.g., 'Series A AgTech startups in the Midwest') rather than just matching keywords.
+ Agentic Verification: Uses built-in AI agents to verify that each result strictly matches user-defined criteria before including it in a collection.
+ Rich Automation: Supports recurring searches to keep lists fresh and catch new companies or people as they emerge on the web.
+ Clean Structured Output: Exports data in ready-to-use CSV formats or via API with pre-populated fields like funding, industry, and contact info.

Cons

- Asynchronous Latency: High-depth searches are intentionally 'slow' (taking minutes rather than seconds) to ensure comprehensive verification.
- Credit-Based Pricing: Heavy users may find the credit consumption high for deep enrichment tasks compared to static database alternatives.
- Technical Learning Curve: While the UI is clean, fully utilizing the event-driven API and webhooks requires significant developer effort.

Sentiment Analysis

+0.92 — Very PositiveUpdated May 10, 2026

Sentiment has improved since last capture. Sentiment has significantly improved from 0.75 to 0.92 following the release of Websets and Exa 2.1. Users are particularly impressed by its performance relative to OpenAI's Deep Research, specifically citing its superior scale and the accuracy of its structured data retrieval.

Sentiment Over Time

By Source

G2+1.00

1 mention

Sample quotes (1)

"Ease of use both with a UI or through the API... Nothing [to dislike], its been great for my uses as a GTM engineer."

Reddit+0.85

12 mentions

Sample quotes (1)

"Exa 2.1 and Websets have become my favorite search API for AI agents. It's the fastest search API available, topping benchmarks because it was built specifically for AI."

X+0.90

25 mentions

Sample quotes (1)

"Websets outperformed Google and OpenAI in testing, finding 20 times more correct results than Google and 10 times more than OpenAI Deep Research in complex queries."

Agent Readiness

75/100

Exa Websets is exceptionally ready for autonomous AI agents, evidenced by its official MCP (Model Context Protocol) server and its unique 'llms.txt' documentation designed specifically for ingestion by AI coding agents. The API is async-first and event-driven, utilizing webhooks to notify agents when items are discovered or enriched, which is ideal for long-running research tasks. With native nodes for n8n and Zapier, plus a generous free tier for developers, it serves as a high-performance 'eyes and ears' for any autonomous research stack.

API Surface100

Public APIRESTFree TieropenApi

Protocol Support40

MCP (16 tools)

SDK Availability35

npm: exa-websets-mcp-servernpm: n8n-nodes-exa-websets

Integration Ecosystem100

ZapierMaken8nWebhooksGumloopMCP Server (Model Context Protocol)CursorVercelNetlify

Developer Experience100

Docs: excellentSandboxVersioningChangelogStatus Page

Last checked May 10, 2026

MCP Integrations

1 server16 tools6,950 total uses

Exa Websetsexa-labs/websets-mcp-server

smitheryRemoteHigh match

Create and manage collections of companies, people, and papers, automatically discovering and verifying relevant entities. Enrich items with custom fields like CEO name, funding amount, or company stage, and inspect detailed results. Schedule recurring searches to keep collections fresh and catch new additions.

6,950 uses

16 tools

create_websetCreate a new Webset collection. Websets are collections of web entities (companies, people, papers) that can be automatically searched, verified, and enriched with custom data. IMPORTANT PARAMETER FORMATS: - searchCriteria: MUST be array of objects like [{description: "..."}] (NOT array of strings) - enrichments: Each must have description field, optional format and options - enrichment options: MUST be array of objects like [{label: "..."}] (NOT array of strings) Example call: { "name": "AI Startups", "searchQuery": "AI startups in San Francisco", "searchCriteria": [{"description": "Founded after 2020"}], "enrichments": [ {"description": "CEO name", "format": "text"}, {"description": "Company stage", "format": "options", "options": [{"label": "Seed"}, {"label": "Series A"}]} ] }
list_websetsList all websets in your account. Returns a paginated list of webset collections with their current status and item counts.
get_websetGet details about a specific webset by ID or externalId. Returns full webset information including status, item count, and metadata.
update_websetUpdate a webset's metadata. Use this to add or update custom key-value pairs associated with the webset.
delete_websetDelete a webset and all its items. This action is permanent and cannot be undone.
list_webset_itemsList all items in a webset. Returns entities (companies, people, papers) that have been discovered and verified in the collection.
get_itemGet a specific item from a webset by its ID. Returns detailed information about the item including all enrichment data.
create_searchCreate a new search to find and add items to a webset. The search will discover entities matching your query and criteria. IMPORTANT PARAMETER FORMATS: - entity: MUST be an object like {type: "company"} (NOT a string) - criteria: MUST be array of objects like [{description: "..."}] (NOT array of strings) Example call: { "websetId": "webset_123", "query": "AI startups in San Francisco", "entity": {"type": "company"}, "criteria": [{"description": "Founded after 2020"}], "count": 10 }
get_searchGet details about a specific search, including its status, progress, and results found.
cancel_searchCancel a running search operation. This will stop the search from finding more items.
create_enrichmentCreate a new enrichment for a webset. Enrichments automatically extract custom data from each item using AI agents (e.g., 'company revenue', 'CEO name', 'funding amount'). IMPORTANT PARAMETER FORMATS: - options (when format is "options"): MUST be array of objects like [{label: "..."}] (NOT array of strings) Example call (text format): {"websetId": "webset_123", "description": "CEO name", "format": "text"} Example call (options format): {"websetId": "webset_123", "description": "Company stage", "format": "options", "options": [{"label": "Seed"}, {"label": "Series A"}]}
get_enrichmentGet details about a specific enrichment, including its status and progress.
update_enrichmentUpdate an enrichment's metadata. You can associate custom key-value pairs with the enrichment.
delete_enrichmentDelete an enrichment from a webset. This will remove all enriched data for this enrichment from all items.
cancel_enrichmentCancel a running enrichment operation. This will stop the enrichment from processing more items.
create_monitorCreate a monitor to automatically update a webset on a schedule. Monitors run search operations to find new items. IMPORTANT PARAMETER FORMATS: - cron: MUST be 5-field format "minute hour day month weekday" (e.g., "0 9 * * 1") - entity: MUST be an object like {type: "company"} (NOT a string) - criteria: MUST be array of objects like [{description: "..."}] (NOT array of strings) Example call: { "websetId": "webset_123", "cron": "0 9 * * 1", "query": "New AI startups", "entity": {"type": "company"}, "criteria": [{"description": "Founded in last 30 days"}], "count": 10 }