Key takeaways:
- Linkup is pure pay-as-you-go: no plans, no seats. You prepay credits and pay per request, with standard search at $0.005 ($5 per 1,000 queries).
- Deep search costs 10 times more, at $0.05 per request ($50 per 1,000). The search depth and output mode you pick, not just the call, decide your bill.
- Every account gets $20 in free credit that tops back up to $20 each month, roughly 4,000 standard searches, so there's a real recurring free allowance, not a one-time trial.
- Linkup's pitch is accuracy. It reports around 91% on the SimpleQA benchmark and sits at the same ~$5 per 1,000 standard price as Brave and Exa, aimed at teams that pay a price when the model is wrong.
Linkup's pricing page is refreshingly blunt: "pay as you go," "run 4,000 queries for free." What that headline leaves out is that the friendly $5-per-1,000 rate is only the standard tier. Ask for a deep search and the price jumps 10x, ask for a composed answer instead of raw results and it ticks up again, and the async research endpoint is a different animal entirely.
This guide breaks down Linkup search API pricing in full, with prices verified live on July 5, 2026. You'll get the per-request cost of every endpoint, how the free credit actually works, the standard-versus-deep math that sets your bill, why teams pay Linkup for accuracy, and a straight comparison against Tavily, Exa, and Serper. By the end you'll know what Linkup costs your agent, not what the front page implies.
What Linkup Is and Who Uses It
Linkup is a web search API built for LLMs and AI agents. You call it, it searches the live web, and it returns current results (or a composed, cited answer) that your model can ground itself on. It's a direct competitor to Tavily, Exa, and Serper, and its whole pitch is factual accuracy, backed by partnerships with premium content sources it's licensed to use.
The buyers are developers wiring live search into AI agents, RAG pipelines, and research copilots. Linkup says it's used in production by AI labs and firms like Databricks, KPMG, and McKinsey, which tells you where it aims: teams where a wrong answer has real consequences.
That accuracy focus exists because inaccurate AI output is a measured problem, not a talking point. In Stack Overflow's 2025 survey, 66% of developers said their top frustration is "AI solutions that are almost right, but not quite". An agent is only as good as the facts it retrieves, so a search API that returns accurate, current sources is how you close that gap, which is exactly the job Linkup sells.
Linkup Search API Pricing at a Glance
*Linkup's pricing page, linkup.so (July 2026).*
Linkup search API pricing has no subscription tiers and no per-seat fees. You prepay credits and spend them per request, and three endpoints each carry their own price. Here's the full lineup, verified July 5, 2026.
Endpoint | What it does | Latency | Price per request |
|---|---|---|---|
Fetch | Extract content from a URL | Under 2s | $0.001–$0.005 |
Search (standard) | Live web search for agents | 1–3s | $0.005–$0.006 |
Search (deep) | Multi-step retrieval | Slower | $0.05–$0.055 |
Research | Async deep research | 1–10 min | $0.25–$2.50 |
Two design choices shape your budget. There's no plan to pick, so you never pay for a tier you don't use, but you also can't cap spend with a subscription; your bill is whatever your usage adds up to. And every plan includes SOC 2 Type II and zero data retention at no extra cost, which matters if you're the kind of buyer (finance, legal, healthcare) who'd otherwise pay more for compliance. Read on for how the search tiers actually price out.
How Linkup's Credits and Free Tier Work
Linkup runs on prepaid credits, and its free allowance is more generous than a trial. Sign up with a work email and you get $20 in credit; eligible accounts are topped back up to $20 every month. At the standard rate that's roughly 4,000 searches a month, free and recurring, not a one-time sample that expires.
A few mechanics matter for budgeting. Top-ups of $1,000 or more earn bonus credits, so heavy users get a volume break without negotiating a contract. Startups can apply for $5,000 in credits through Linkup's startup program. And if you run out mid-workload, the API returns an HTTP 429 rather than silently overcharging, so you top up and continue.
That recurring free tier is landing at a useful moment. When Brave removed its free search-API tier in early 2026, developers went shopping. In an r/openclaw thread on Brave replacements, one dev flagged Linkup specifically because "they have a free tier without the credit card wall," on top of raw results, cited answers, and deep search. For prototyping, a no-card recurring allowance is a real reason to start there.
Standard vs Deep Search: The 10x That Sets Your Bill
The single most important number in Linkup pricing is the gap between standard and deep search. A standard search is $0.005 a request ($5 per 1,000). A deep search, which runs multi-step retrieval to chase harder questions, is $0.05 a request ($50 per 1,000), exactly 10 times more.
There's a second lever on top of depth: output mode. Ask for raw search results and you pay the base rate. Ask Linkup to compose a sourced answer or a structured object (where its model shapes the response) and each call costs a touch more. Here's how the search costs stack up.
Call type | Raw results | Sourced / structured answer |
|---|---|---|
Standard search | $0.005 | $0.006 |
Deep search | $0.05 | $0.055 |
The async Research endpoint is separate again, priced by how much reasoning you ask for: $0.25, $0.50, $1.50, or $2.50 per run from the shallowest to the deepest tier. So your real cost isn't one number; it's how often you reach for deep search and composed answers versus plain standard lookups. An agent that runs everything on deep search with structured output costs 11 times one that sticks to standard raw results. Default to standard, and reserve deep search for the queries that genuinely need it.
Why Teams Pay Linkup for Accuracy
Linkup costs more than the cheapest raw-SERP APIs, and its whole case for that premium is accuracy. It reports around a 91% F-score on SimpleQA, a single-fact benchmark built by OpenAI researchers, which it presents as state of the art against rivals like Perplexity's Sonar. Treat that as Linkup's own published figure, but the direction is consistent across independent tests.
Accuracy matters because hallucination is a structural LLM problem, not a rough edge. Peer-reviewed work in Nature showed that language models produce confident but wrong "confabulations" that require real methods to detect. Feeding your model accurate, current sources is the practical defense, so the quality of the search layer directly shapes how often your agent is right.
Practitioners weigh this tradeoff explicitly. In an r/Rag thread comparing search APIs, one developer tested Tavily, Exa, and Linkup, then chose Linkup, writing that the "main reason is quality of responses. It was better than Exa and much better than Tavily." He ranked Exa fastest and Linkup most accurate, which is the exact tradeoff Linkup asks you to buy: a bit more cost and latency for better facts.
Linkup vs Tavily, Exa, and Serper on Price
Linkup isn't alone, and the AI-search field prices in noticeably different ways. Here's how the standard rates compare, verified July 5, 2026.
Tool | Standard price | Free tier | Best for |
|---|---|---|---|
Linkup | $5 per 1,000 | $20/mo (~4,000) | Accuracy-critical agents |
Tavily | from $30/mo | 1,000/mo | Easy RAG integration |
Exa | $7 per 1,000 | 20,000/mo | Semantic and neural search |
Serper | $1 per 1,000 | 2,500 total | Cheapest raw Google results |
Brave | $5 per 1,000 | None (ended 2026) | Independent search index |
At the standard tier, Linkup, Brave, and Exa cluster around $5 to $7 per 1,000, so the headline price isn't the real difference; the free allowance, deep-search cost, and accuracy are. Serper undercuts everyone on raw Google results at $1 per 1,000, but it hands you SERP data to process yourself rather than agent-ready answers.
The market shifted under all of them in early 2026. Brave killed its free tier, and Nebius acquired Tavily in a deal worth up to $400 million, which sent teams re-evaluating their search stack. Tavily's subscription model ($30 and $100 tiers) can hit cost cliffs when parallel agents burn through a plan, which is the tradeoff Linkup's flat per-request pricing is pitched against. Pick on how you'll actually query, not the sticker rate.
How to Estimate Your Real Linkup Bill
Because Linkup bills per request, you can forecast your spend precisely before you commit. Run the number in four steps instead of guessing from the $5-per-1,000 headline.
Step 1: Count Searches per Agent Run
Break one unit of work into its calls. An agent answering a single question might fire 1 main search plus 3 follow-ups, so 4 searches per run. Write down the real number, not the best case.
Step 2: Split Standard vs Deep
Flag which of those calls genuinely need deep search. If 3 are standard and 1 is deep, that run costs 3 × $0.005 plus 1 × $0.05, or $0.065, not four cheap calls. Deep search is where budgets balloon, so count it separately.
Step 3: Add Output Mode and Research Runs
If you request sourced answers instead of raw results, bump each call to the $0.006 or $0.055 rate. Add any async Research runs at $0.25 to $2.50 each. These extras are easy to forget and easy to overspend on.
Step 4: Check the Total Against the Free Credit
Multiply by your monthly run volume, then subtract the recurring $20. An agent doing 3,000 standard searches a month lands near $15, inside the free allowance; the same agent leaning on deep search blows past it fast. That's your real bill.
Run those four steps and you get a spend figure you can defend, which is the whole advantage of per-request pricing over a subscription you might not fully use.
Is Linkup Worth It?
For teams building production agents where accuracy has consequences, Linkup earns its price. If you're in finance, legal, research, or any workflow where a confidently wrong answer costs real money, paying a bit more per query for better factual grounding and transparent, no-contract pricing is a sensible trade, and the recurring free credit makes it cheap to test.
It's the wrong pick in a few cases. If you just need raw Google results to process yourself, Serper is five times cheaper per query. If you want semantic or embeddings-style retrieval, Exa is built for that and faster. And if your agents lean heavily on deep search, the 10x deep rate means Linkup can get expensive quickly, so model your query mix before you scale.
Weigh it against your workload, not the marketing. For an accuracy-sensitive agent that runs mostly standard searches and reaches for depth only when needed, Linkup is a strong, predictable choice. For high-volume raw scraping or latency-critical semantic search, a cheaper or faster specialist fits better.
Choosing Linkup with Clear Eyes
Linkup pricing is genuinely simple once you see past the headline: you prepay credits and pay per request, standard search is $0.005, deep search is 10 times that, and composed answers cost a hair more. The recurring $20 free credit gives you a real allowance to test with, and the accuracy pitch is the reason to pay over a cheaper raw-SERP API.
Start on the free credit, map your agent's query mix with the four-step estimate above, and default to standard search with raw results unless a task truly needs depth or a composed answer. Do that and Linkup is a predictable, accurate search layer for your agent. Skip the math and the 10x deep-search rate will surprise you on the invoice.
