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Reading time: 12 min
July 27, 2026
# Brave Search API vs. Parallel: independent index or agent-native retrieval?
Brave and Parallel are two of the few companies in this category that crawl and rank the web themselves rather than reselling someone else's results. That shared independence makes the comparison unusually clean: you are choosing between two real indexes, not between two wrappers around Google. Where they diverge is who the index was built for. Brave's serves a consumer search engine that also sells API access. Parallel's was built for machines from the start.
Tags:Comparison
Reading time: 8 min## **Two independent indexes, built for different readers**
Brave runs its own crawler and its own ranking, and describes the result as the largest independent web index outside the big incumbents. It is not a Google or Bing scraper, which means it does not inherit their rate limits, their terms, or their outages. The same index powers Brave Search for consumers and Ask Brave, which the company says serves 22 million answers a day. Privacy is a first-class design constraint: Brave does not build user profiles from queries.
That independence is a genuine strategic asset and worth weighing on its own. If your concern is concentration risk, or you need a supplier whose index is not derived from a company you also compete with, Brave is one of a very short list of options.
Parallel's index exists only to serve agents. There is no consumer product on top of it, no ranking tuned for click-through, and no result page to reproduce. Retrieval is organized around a natural-language objective, and what comes back is compressed excerpts sized for a context window.
## **Search**
Brave's Search plan covers several endpoints under one price. Web Search returns human-readable URLs and text snippets with schema-enriched metadata. LLM Context is the AI-oriented sibling: Brave compacts the relevant web context into a form meant for model consumption, and it is the same layer that backs Ask Brave. News, Video, and Image search have dedicated endpoints, as does Place Search for physical locations. Autosuggest and Spellcheck round it out.
Goggles are Brave's most distinctive feature: user-defined rulesets that re-rank and filter results, so you can bias retrieval toward a corpus you trust without hard-coding a domain allowlist on your side. Nothing in the LLM-native category has an equivalent.
Parallel's Search API takes an objective plus optional explicit queries and returns ranked URLs with excerpts, sized by max_chars_per_result and max_chars_total. Three modes set the latency and depth: Turbo at ~200ms and $1 per 1,000 requests, Basic at ~1s and $5 per 1,000, and Advanced at ~3s and $5 per 1,000, which is the default. A Source Policy handles domain inclusion, exclusion, and freshness; a Fetch Policy decides between the index and a live crawl. Parallel also sells an Extract API at $1 per 1,000 URLs for when an agent needs the whole page.
Brave has no content extraction endpoint. If your agent needs full page text, that is a second vendor or your own fetcher.
## **Generated answers**
Brave's Answers plan is a separate product on a separate meter. It exposes an OpenAI-compatible chat completions endpoint that grounds a generated answer on one search or several, returns citations, and supports streaming. Pricing is $4 per 1,000 queries plus $5 per million input tokens and $5 per million output tokens. Note that Summarizer Search, which older tutorials still point at, is deprecated in favour of Answers and remains available only to customers on the discontinued Pro AI plan.
Parallel covers the same ground with the Responses API: OpenAI-compatible agentic research that returns a synthesized, cited answer, priced by reasoning effort at $10 per 1,000 requests for low, $50 for medium, and $250 for high, with latencies of roughly 5 to 10 seconds, 15 to 20 seconds, and 30 to 60 seconds respectively.
Both are drop-in for an existing OpenAI integration. Brave's token-based component makes cost vary with answer length; Parallel's is a flat rate per request at each effort tier, which is easier to forecast but less forgiving on short answers.
## **Accuracy and cost inside an agent loop**
Parallel's July 2026 benchmarks ran a GPT-5.4 agent with up to 20 tool calls per question across several search backends. On BrowseComp, OpenAI's set of questions requiring persistent browsing to locate hard-to-find facts, Parallel Turbo reached 51% accuracy at a total cost of $350 per 1,000 questions. Brave Search reached 38.3% at $336 per 1,000. Costs include LLM tokens as well as tool calls, and answers were graded by an LLM judge.
Two caveats belong with those numbers. These are Parallel's own results on Parallel's harness. And Brave, having no extract API, ran without the web_fetch tool that engines like Parallel, Exa, and Tavily were given, which accounts for some of the gap on a benchmark built around digging through pages. At near-identical total cost, though, Brave placed second among the search-only engines tested and ahead of several that did have fetch tools.
## **Pricing**
Brave restructured its plans in early 2026, retiring the old free tier that allowed a couple of thousand queries a month. The current shape is:
- - Search: **$5 per 1,000 requests**, covering web search, LLM Context, news, videos, images, and place search, at 50 requests per second
- - Answers: **$4 per 1,000 queries** plus $5 per million input tokens and $5 per million output tokens, at 2 requests per second
- - Enterprise: custom terms, capacity, and endpoints, with full-funnel zero data retention
Both plans include $5 in credits every month, applied automatically. A card is required to subscribe even on a credit-only plan, as an anti-fraud measure.
Parallel's free tier is structured identically: $5 in credits every month, applied automatically, with a card on file. Because Turbo costs $1 per 1,000 requests, that covers up to 5,000 searches a month against Brave's 1,000.
Paid rates line up as follows. Brave Search is $5 per 1,000 requests. Parallel is $1 per 1,000 in Turbo and $5 per 1,000 in Basic and Advanced. So Brave sits at parity with Parallel's higher-quality modes and at five times the cost of Turbo. Everything else on the Parallel side is priced separately: Extract at $1 per 1,000 URLs, the Task API at $5 to $2,400 per 1,000 runs across nine processors, Monitor at $3 per 1,000 executions on lite or $10 on base, Entity Search at $5 per 1,000, and FindAll on a fixed cost plus $0.03 to $1.00 per match.
_Note: For the latest pricing, always check official documentation._
## **Throughput**
Brave's Search plan allows 50 requests per second, which works out to 3,000 per minute, enforced on a one-second sliding window with X-RateLimit headers on every response. That is meaningfully higher than Parallel's default of 600 requests per minute for Search, and it is a real advantage if you are running heavy parallel fan-out and do not want to negotiate limits.
The Answers plan is the opposite story at 2 requests per second, which is restrictive enough to shape your architecture. If generated answers are on your hot path, that number deserves attention before anything else in this comparison.
Parallel's defaults are 600 per minute for Search, Extract, and Entity Search, 300 for Monitor, and 300 per hour for FindAll runs, with GET polling excluded and custom limits available on enterprise plans.
## **The storage rights clause**
One Brave term is easy to miss and expensive to discover late. Storing results, in part or in whole, including for training or tuning a model, requires a plan that explicitly grants storage rights. Plenty of ordinary architectures touch this: caching search results to cut cost, persisting retrieved passages in a vector store, keeping a record of what an agent saw for audit purposes, or building an evaluation set from production traffic.
If any of that describes your design, raise it with Brave during evaluation rather than after you have built around it.
## **Developer experience**
Brave's API is a clean REST surface with an API dashboard, published skills for AI coding agents, and an API assistant in the docs. The Answers endpoint being OpenAI SDK-compatible makes it easy to try inside an existing app.
Parallel ships Python and TypeScript SDKs, an MCP server, and a playground, with LangChain and the usual agent frameworks supported:
12345678910111213from parallel import Parallel
client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])
search = client.beta.search(
objective="your goal",
mode="turbo",
excerpts={"max_chars_per_result": 10000},
)``` from parallel import Parallel client = Parallel(api_key=os.environ["PARALLEL_API_KEY"]) search = client.beta.search( objective="your goal", mode="turbo", excerpts={"max_chars_per_result": 10000}, )``` ## **Compliance**
Brave achieved SOC 2 Type II attestation in October 2025 and offers full-funnel zero data retention, custom agreements and NDAs, invoicing, and enterprise support on its Enterprise plan.
Parallel is SOC 2 Type 2 certified, offers a Data Processing Addendum and zero data retention, and commits contractually to not training on customer data, with a public status page and trust center.
## **When to use each**
Choose Brave when index independence, privacy, or throughput is the deciding factor. Owning the crawl end to end means no dependency on a search incumbent's terms, and the privacy posture is a real differentiator for products where query data is sensitive. Goggles give you re-ranking control nothing else offers, Place Search covers physical locations, and 50 requests per second on the Search plan is generous without a negotiation. If you already run your own extraction layer, the missing content endpoint may not matter to you.
Choose Parallel when the priority is cost and quality of context per call. Turbo delivers a 200ms median at $1 per 1,000 requests, a fifth of Brave's Search rate, and returns excerpts selected against your objective so the model spends fewer input tokens per answer. Extract closes the loop when an agent needs the full page, and the Task, Responses, FindAll, Entity Search, and Monitor APIs cover deep research, list building, and change tracking without another vendor. There is also no storage rights clause to design around.
Both companies are building independent infrastructure rather than reselling a SERP, which is the harder and more durable path. The choice comes down to whether you want a general-purpose index you can shape yourself, or a stack purpose-built to feed a model.
**Related reading: **SerpApi vs. Parallel[SerpApi vs. Parallel] · You.com vs. Parallel[You.com vs. Parallel] · Linkup vs. Parallel[Linkup vs. Parallel].
By Parallel
July 27, 2026