best · Evidence checked 2026-09-02

Choose an AI search tool by its evidence universe

A practical, evidence-led decision guide. Product capabilities and limits are separated from anything that would require hands-on testing.

AI search is not one market. A cited web answer, a scholarly review assistant, permission-aware enterprise retrieval and a developer search API solve different problems. The wrong shortlist begins by asking which one writes the nicest answer. The right shortlist begins with the evidence universe and who is accountable for missing material.

Shortlist by evidence job

Product	Best evaluation job	Meter or contract	Failure that matters most
Perplexity	cited current-web research	consumer, enterprise, API and Computer costs are separate	citations that do not fully support the answer
Elicit	scholarly discovery, screening and extraction	tiers change review allowances	missing known included papers
Consensus	question-led paper exploration	free/Pro plus deeper-review and integration limits	flattening methodological disagreement
Scite	examining how later papers treat a claim	individual plans plus API/MCP boundaries	trusting citation classification without context
Glean	internal enterprise knowledge	quote-led deployment	leaking restricted titles, snippets or answers
Algolia	search inside a product or catalogue	requests, records and capabilities	poor relevance or runaway frontend operations
Exa	web retrieval for AI applications	search, contents and agent units	useful retrieval that becomes costly after content calls
Tavily	credit-metered agent search, extraction and crawl	credits vary by endpoint and depth	automatic routing that doubles or broadens spend

Four markets hiding under “AI search”

External-web research: Perplexity gives an end user a fast cited synthesis. Validate claims, primary-source selection and freshness.

Scholarly evidence: Elicit supports the review pipeline; Consensus makes question-led exploration accessible; Scite exposes citation treatment. None removes protocol or paper reading. The Perplexity vs Elicit analysis explains why one generic prompt is an invalid comparison.

Enterprise knowledge: Glean is evaluated through identities, connectors and permission revocation. Relevance comes after access safety.

Search infrastructure: Algolia serves buyer-controlled records; Exa and Tavily retrieve the web for applications and agents. The buyer owns routing, citation logic, caching and downstream answer quality.

Run one portfolio benchmark

Build 100 queries across stable facts, recent facts, disputed questions and deliberate no-answer cases. Add a known-paper set for scholarly tools, restricted documents for enterprise search and request telemetry for APIs.

Record source recall, claim support, freshness, contradiction handling, permission failures, latency, cost and reviewer time. Report each class separately. A global score can let excellent easy-query performance hide a dangerous permission or unsupported-claim failure.

Budget by verified outcome

For seats, use cost per defensible research brief or completed review stage. For APIs, include searches, fetched pages, summaries, deep modes, agent runs, downstream models and retries. For enterprise systems, include connectors, identity work, cleanup and administration.

The common denominator is a verified useful answer, not a query sent.

Final recommendation

Choose Perplexity for broad, current research with active citation review.
Choose Elicit, Consensus or Scite according to the scholarly step—not as interchangeable academic chatbots.
Choose Glean only after a permission-negative pilot.
Choose Algolia for owned application search.
Choose Exa or Tavily for developer retrieval after a source-and-cost benchmark.

Primary action: select the evidence universe first, open the corresponding product profile, and run its failure-focused benchmark before buying seats or sending production traffic.

Official sources

Sources checked 2026-09-02. Recheck current pricing, coverage and contractual controls.