Exa is our first pick for agents that need to discover web content by meaning, especially when a useful result may use different vocabulary from the query. We like it for exploratory research and finding pages related to a known document. For content you already own, we’d evaluate a database such as Pinecone or Weaviate instead.
This is a different emphasis from our web search API roundup, which focuses on grounded answers and broader web access. Here, the question is how to retrieve relevant material when you don’t know the exact terms to search for. These are our opinions on product fit, with pricing checked in September 2026.
The options we’d consider
| Rank | Tool | Role | Where we’d use it |
|---|---|---|---|
| 1 | Exa | Semantic web retrieval | Exploratory research and related-page discovery |
| 2 | TinyFish | Web search, extraction, and browser workflows | Collecting live content from dynamic pages |
| 3 | Context.dev | Web search and structured extraction | Gathering page content and company context for retrieval |
| 4 | Tavily | General web retrieval | Search and extraction for agent workflows |
| 5 | Perplexity Sonar | Grounded answer generation | Applications that want a composed answer with sources |
| 6 | Firecrawl | Web search and content extraction | Discovering pages and ingesting their contents |
| 7 | Pinecone | Managed vector database | Retrieval over a corpus you supply |
| 8 | Weaviate | Vector and hybrid database | Retrieval over your own data with deployment choices |
| 9 | Brave Search API | Independent web search | A general search candidate to compare on your queries |
These tools sit at different layers. The order reflects where we’d start for semantic web discovery; Pinecone and Weaviate become the relevant choices when the job is searching a private corpus.
1. Exa: our pick for discovery by meaning
Exa builds search for AI applications. Its neural retrieval approach is particularly interesting when a user describes an idea rather than names a known page. Exa also offers related-page discovery through findSimilar, which takes a URL as the starting point. The capability is documented in Exa’s own Haystack integration.
That is useful for a research product that starts with one relevant article and needs more material on the same subject. It gives the product another way to express intent without asking the user to invent a better keyword query. We would evaluate the relevance and diversity of those related results, rather than assume that every similar page adds useful information.
Exa’s pricing page lists standard Search at $7 per 1,000 requests, Deep Search at $12, and Deep-Reasoning Search at $15. The base prices cover up to ten results; each additional result adds $1 per 1,000 requests. The separate Contents API is priced at $1 per 1,000 pages per content type. The free plan lists $20 at signup and $10 in monthly credits.
Our take: start with Exa for open-ended research and related-document discovery. Put exact identifiers, recent pages, and non-English queries in the pilot too. We would measure those cases rather than assume semantic retrieval is automatically better or worse at them.
2. TinyFish: live content for retrieval workflows
TinyFish combines Search and Fetch with Agent and Browser products. We’d consider it when a retrieval workflow needs to continue beyond finding a page into collecting content from a dynamic site or completing a multi-step interaction.
Its pricing page lists Search and Fetch as free within published limits, with Agent steps and browser time billed separately. Evaluate whether the collected content supports the user’s question and how much the complete workflow costs. Its role here is web access and content collection; semantic ranking over the resulting corpus is a separate decision.
3. Context.dev: structured context from discovered pages
Context.dev offers web search, scraping, crawling, and extraction of page content, brand information, and images. We’d shortlist it when an agent needs both source text and structured website information, such as company context for a research or enrichment feature.
Its pricing page lists 500 free monthly credits and paid plans starting at $25/month. Search and extraction have different credit costs. Compare the quality of the fields and passages returned, then decide how your application will index or rank them for semantic retrieval.
4. Tavily: a general retrieval alternative
Tavily offers search, extraction, and broader research capabilities. We would compare it with Exa when the product’s queries are a mix of exploratory questions and straightforward factual lookups.
Its free allowance is 1,000 API credits per month, with pay-as-you-go credits at $0.008 each. Different operations consume different numbers of credits. Compare cost per completed query in your application, rather than equating one credit with one search.
5. Perplexity Sonar: when you want an answer
Perplexity Sonar combines generation with web grounding. We would consider it when the desired output is already a composed answer with citations and the product doesn’t need to own every retrieval decision.
If you want source results for your own model, evaluate Perplexity’s separate Search API. Keeping that distinction clear helps avoid paying for an answer layer your application then discards.
6. Firecrawl: collecting the material you retrieve
Firecrawl offers search alongside scraping and crawling. We like it for products that need to move from discovery into substantial content collection, such as assembling a documentation corpus or refreshing a set of source pages.
Decide whether your task ends with a relevant passage or with a stored collection of documents. The second also requires deduplication, refresh scheduling, and a policy for handling deleted content.
7. Pinecone: retrieval over your own corpus
Pinecone is a managed vector database. We’d consider it for internal knowledge, support material, product catalogs, or another collection the application supplies.
Your team still owns the quality and permissions of that corpus. A good retrieval result must come from a document the requesting user is allowed to see. A database and a web search service can coexist in the same product, with routing based on the source needed.
8. Weaviate: vector and hybrid retrieval
Weaviate supports vector and hybrid search, with open-source and managed deployment options. We’d consider it when we want to combine semantic relevance with lexical matching over our own data.
Evaluate the operational work alongside retrieval quality. Choosing how to host the database also means choosing who handles upgrades, capacity, backups, and incidents.
9. Brave Search API: a useful comparison point
Brave provides search from its own index, including context intended for LLMs. We would include it in a pilot to see whether general web retrieval already answers the product’s queries well enough.
Its Search price is $5 per 1,000 requests. That is a published rate, not evidence that it is the cheapest choice for every workflow. Extra fetching and generation can matter more than a small difference in search price.
When semantic retrieval earns its place
Semantic retrieval is useful when the query and the source describe the same idea differently. A request about preventing invented answers might need a page about grounding model outputs in evidence. Exact matching can still be valuable for a product code, an error string, or a named document. Many systems benefit from combining the two approaches.
We would test relevance, answer support, and coverage separately. A related page may be interesting without containing the fact the user needs. Keep the model and prompt fixed during the comparison, and measure end-to-end cost and latency at representative concurrency.
Bound result counts and retries. Cache where the freshness requirements and provider terms permit it. Preserve source URLs so the application can show where its answer came from.
For B2B applications, retrieval often spans web content and customer records. Our agent integration overview covers connecting the latter, while our field-mapping guide explains why customer-specific schemas need attention. The build-versus-buy framework can help decide which parts of that infrastructure your team should own.