What is the difference between semantic search and a web search API?

Semantic search ranks documents by meaning over an index you already built — it answers 'what in my corpus is about this?' A web search API hits a live index of the open web and can return fresh page content — it answers 'what does the web say about this right now?' Your embeddings index only knows what you ingested, so it can't cover information that postdates your last update. The two solve non-overlapping query sets.

Do I need embeddings if I use a web search API?

Not for fresh, one-off web lookups — a live web search API like fastCRW's /v1/search retrieves and extracts the page content directly. You need embeddings when you have a private, owned corpus you want to search semantically and repeatedly. Many production stacks route: semantic search over the owned index first, then fall through to live web search when the index is thin or the query is time-sensitive.

Which semantic search API is cheapest for AI apps?

Raw embeddings (OpenAI, Cohere) are the cheapest per token, but you also pay for the vector database and the engineering to run the search layer. Neural search engines bill per search, with deep full-content retrieval priced higher. The structural cost floor is self-hosting an open-source engine: the fastCRW engine is AGPL-3.0 and free to self-host, so your worst-case cost is your own server. Check /pricing for current managed tiers.

Can fastCRW replace a semantic search API?

Only for the live-web half. fastCRW's /v1/search is a live web search plus full-page extraction tool, not an embeddings index over your private data — it won't replace OpenAI embeddings, a vector store, or a reranker for RAG over an owned corpus. It complements them: when your index can't answer a fresh or time-sensitive query, fastCRW returns LLM-ready markdown you can use directly or re-embed.

How fast is fastCRW search?

On fastCRW's own search benchmark (benchmarks/triple-bench.ts, 100 queries across 10 categories, single point-in-time run), fastCRW search averaged 880 ms with a 785 ms median and a 1,433 ms P95, taking 73 of 100 latency wins at 100% success. That measures search only; enabling full-page extraction on the matched results raises latency with the number of pages scraped, so budget for it on the hot path.

Best Semantic Search APIs for AI Apps in 2026

By the fastCRW team · Facts and pricing verified 2026-05-29 · We build fastCRW, so weight the live-search section accordingly and verify independently.

The best semantic search APIs for AI apps, and where each one fits

Choosing the best semantic search API for an AI app in 2026 is less about which vendor "wins" a leaderboard and more about matching the API to the question you are actually answering. A semantic search API that ranks documents by meaning over an index you control is a different tool from a live web search API that fetches fresh page content on demand. Pick the wrong category and you end up bolting a scraper onto an embeddings store, or paying for neural ranking on data that changes hourly. This guide draws the line cleanly: semantic search over your index, neural search engines over a prebuilt web index, and live web search for everything an index can't already contain.

We build fastCRW, a Firecrawl-compatible open-core engine with a /v1/search endpoint, so treat our live-search recommendations as those of an interested party. We have kept the trade-offs honest and cited real numbers with provenance rather than vague "fast" claims.

Semantic search vs keyword vs live web search

Three retrieval models get lumped together under "search." They solve different problems:

Keyword (lexical) search — BM25, Elasticsearch, Postgres full-text. Matches tokens. Fast, cheap, exact, and blind to synonyms: a query for "wrongful termination" misses a doc that only says "at-will violation."
Semantic search — embeds query and documents into vectors and ranks by cosine similarity, so meaning matches even when wording doesn't. This is the embeddings search API + vector database pattern, and it answers "what in my corpus is about this?"
Live web search — hits a search index of the open web and (optionally) returns full page content. It answers "what does the web say about this, right now?" Your embeddings index can't answer that — it only knows what you already ingested.

The semantic search vs keyword debate is largely settled in favor of hybrid (lexical recall + semantic precision via reranking). The more useful 2026 distinction is index-bound semantic search versus live web retrieval, because they cover non-overlapping query sets.

Comparison table of major semantic search APIs

What each tool actually returns, and what it leaves you to build:

API	Category	Returns	You still build	Best for
OpenAI / Cohere embeddings	Embeddings search API	Vectors	Vector DB, chunking, retrieval, ranking	RAG over private data
Cohere Rerank	Reranker (cross-encoder)	Reordered candidates	A first-pass retriever	Upgrading an existing search
Pinecone / Weaviate / Qdrant	Vector store	Nearest vectors	Embeddings + the search layer	Storing vectors at scale
Exa	Neural search engine	Index hits + highlights	Full-page extraction if you need it	Research-grade discovery
fastCRW `/v1/search`	Live web search + extraction	Results + full-page markdown	Nothing for fresh web data	Up-to-the-minute web retrieval

Read down the "you still build" column: it is the real cost difference. Embeddings and vector stores are components; you assemble the search engine. Neural engines and live web search are closer to turnkey for their respective query types.

Embedding + rerank vs neural search engines

Two ways to get semantic quality, with opposite ownership models.

Embedding + rerank (you own the index)

The classic semantic search for RAG stack: embed your documents with an embeddings model (OpenAI text-embedding-3, Cohere embed-v4), store the vectors in Pinecone/Qdrant/Weaviate, retrieve a broad candidate set, then sharpen the top results with a cross-encoder reranker like Cohere Rerank. You control chunking, freshness, ranking, and residency. The cost is engineering: you maintain the pipeline, re-embed on model upgrades, and own the failure modes. This is the right choice when the corpus is yours and can't leave your infrastructure.

Neural search engines (someone else owns the index)

A neural search API like Exa builds its own web index and ranks it with neural models trained on how knowledge links, not SEO popularity. You get strong discovery and "find pages like this" without running any infrastructure — but you are searching their snapshot of the web, with their refresh cadence, and deep full-content retrieval costs more. For background on the neural-index model, see what is Exa AI and our best Exa alternatives roundup.

When you need live web search instead

An embeddings index, however good, only contains what you put in it. The moment your query touches information that postdates your last ingest, semantic search over the index returns confidently wrong or stale answers. Live web search is the correct tool when:

The answer changes faster than you can re-embed — prices, docs, release notes, news, availability.
An agent needs to investigate something it has never indexed — and won't, because it's a one-off lookup.
You want the content of the matched pages, not just a list of links, so an LLM can read it directly.

This is where fastCRW's /v1/search fits. It runs a SearXNG web-search sidecar and can scrape the matched results in the same call, returning LLM-ready markdown instead of bare URLs. One request covers discovery and extraction, so you don't stand up a separate scraping pipeline behind your search API. It is Firecrawl-compatible: point the Firecrawl SDK at fastCRW by swapping api_url and the call shape is unchanged.

from firecrawl import FirecrawlApp

# Same Firecrawl SDK, fastCRW base URL
app = FirecrawlApp(api_key="fc-YOUR_KEY", api_url="https://api.fastcrw.com")

# Live web search + full-page markdown in one call
results = app.search(
    "2026 vector database pricing changes",
    limit=5,
    scrape_options={"formats": ["markdown"]},
)
for r in results["web"]:
    print(r["title"], "->", r["url"])

For a deeper look at how live search slots into agent loops, see search API for AI agents.

Combining semantic search with fresh retrieval

The strongest production retrieval layers in 2026 are not either/or — they route. A practical pattern:

Try the index first. Run semantic search (embeddings + rerank) over your own corpus. For stable, owned knowledge this is the cheapest and most private path.
Fall through to the web when the index is thin. If retrieval confidence is low, or the query is explicitly time-sensitive, call a live web search API to pull fresh full-page content.
Optionally re-embed the fresh pages. Markdown that comes back from live search drops straight into your chunker, so today's web answer becomes tomorrow's index hit.

This split keeps the embeddings store authoritative for what you own and uses live retrieval only for the gap it can't cover. If you are assembling that retrieval layer, our best RAG tools guide covers the ingestion side; fastCRW supplies the fresh-content half via github.com/us/crw (self-host) or the managed cloud.

Pricing and latency considerations

Two numbers decide most retrieval-API choices: cost per query and tail latency on the path that blocks a user or an agent.

Latency, with provenance

On fastCRW's own search benchmark — benchmarks/triple-bench.ts, 100 queries across 10 categories, run concurrently against three providers as a single point-in-time measurement — fastCRW search averaged 880 ms, with a median of 785 ms and a P95 of 1,433 ms, taking 73 of 100 latency wins at a 100% success rate. That is the search benchmark only; it does not measure scrape throughput. When you enable full-page extraction on the matched results, latency rises with the number of pages scraped — budget for it on the hot path.

Honest scrape caveat: on the separate 3-way scrape benchmark (Firecrawl's public 1,000-URL dataset, 819 labeled, diagnose_3way.py, 2026-05-08), fastCRW had the highest truth-recall of the three tools at 63.74% of 819 labeled URLs with 91.8% scrape-success of reachable URLs and 0 thrown errors, and a median latency of 1914 ms. In fast mode its p90 is 4348 ms — the lowest of the three (Crawl4AI 4754 ms, Firecrawl 6937 ms). The chrome-stealth fallback that recovers the 34 pages others miss keeps the tail competitive rather than inflating it. If a tight tail matters more than recall, measure on your own URL mix. Full numbers live at /benchmarks.

Cost model

Embeddings are cheap per token but you pay for the vector store and the engineering around it. Neural search engines bill per search, with deep full-content retrieval priced higher. fastCRW live search costs 1 credit per query; adding extraction on the matched results bills per scraped page. Managed answer-synthesis (the equivalent of an include_answer mode) runs on a managed LLM, available on paid plans, metered in credits based on usage with a per-request credit cap; the FREE plan has no LLM features. The fastCRW tiers are Hobby $13, Standard $69, Growth $279, and Scale $549 per month, plus a free tier of 500 one-time credits; the live, authoritative table is at /pricing.

The structural cost lever is self-hosting: the fastCRW engine is AGPL-3.0, a single ~8 MB static binary in one container (versus a multi-service stack of roughly 2–3 GB across five containers), so the worst-case cost has a real floor — your own server. For private-corpus semantic search that can't egress, that residency property is often the deciding factor, the same way it is for an embeddings-on-your-infra RAG stack.

How to choose

RAG over private, owned data? Embeddings + a vector store + a reranker. fastCRW only supplies the fresh-web half when you need it.
Research-grade discovery over the open web? A neural search engine like Exa for its index quality.
Fresh, full-page web content for queries an index can't cover? Live web search — fastCRW /v1/search, Firecrawl-compatible, self-hostable.
Upgrading a search that already works? Add a reranker; don't rip out the backend.

Sources

fastCRW search benchmark: benchmarks/triple-bench.ts — 100 queries, avg 880 ms, median 785 ms, P95 1,433 ms, 73/100 latency wins, 100% success (single point-in-time run).
fastCRW scrape benchmark: diagnose_3way.py, Firecrawl public 1,000-URL dataset (819 labeled), 2026-05-08 — 63.74% truth-recall (highest), 91.8% scrape-success of reachable URLs, 0 errors, p50 1914 ms (fastest), p90 4348 ms in fast mode (lowest).
fastCRW repo, pricing, and benchmarks: github.com/us/crw · /pricing · /benchmarks