Exa
Neural web search built for scripted research pipelines
Our verdict
The strongest search backend for scripted research, less rewarding if you just want a chat answer.
Exa is a search engine trained on embeddings rather than keywords: describe the kind of page you want and it returns matching results with their contents, which is why it is popular for programmatic research and agent workflows. The web app is free to try; the API is where teams spend money.
How we scored it
What we liked
- ✓Semantic matching beats keywords for narrow asks
- ✓Clean JSON results
- ✓Free credits to evaluate
What to watch
- ✕Developer-oriented
- ✕Returns sources, not synthesised answers
Key features
- ◆Neural search
- ◆Contents endpoint
- ◆Answer generation
- ◆Result highlighting
- ◆Usage dashboard
Best for
Alternatives to Exa
Perplexity
AI search that answers with citations you can actually check
Perplexity searches the live web and writes an answer with every claim linked to its source, so follow-ups stay grounded in what's actually online. Pro unlocks stronger models and longer Deep Research reports, but the free tier already covers most day-to-day questions.
NotebookLM
Grounded AI notebook that answers only from your own sources
NotebookLM loads your documents, sites, videos and slides into a model that answers strictly from them, with every sentence linked back to the passage it came from. Audio Overviews turn a stack of papers into a podcast-style discussion, which is genuinely good for skimming.
Elicit
Literature-review assistant that screens papers into structured tables
Elicit reads academic papers and pulls methods, sample sizes and findings into a sortable table, which makes screening hundreds of titles for a review dramatically faster. It works on peer-reviewed corpora, so it is the wrong tool for general web questions.
Consensus
Ask a research question, get an evidence-backed yes or no
Consensus turns a question into a synthesis of what peer-reviewed studies conclude, with a consensus meter and quotes from each paper. It is built for answerable claims ('does X work?'), not open-ended exploration.