The definitive pricing and capability guide to every web search API powering autonomous AI agents in 2026.
Nebius just acquired Tavily for $275 million. Google sued SerpApi under the DMCA. Microsoft killed the Bing Search API entirely. Brave hit billions of weekly API calls. The search API market that underpins nearly every AI agent on the planet is in violent motion, and the choices you make about which search provider powers your agents will determine both their accuracy and your unit economics for years to come.
This is not a surface-level listicle. This guide documents 35+ web search API providers with exact pricing, benchmark data, latency measurements, and agent integration readiness. Every number comes from primary sources: official pricing pages, independent benchmarks, SEC filings, and acquisition announcements. If you are building, scaling, or evaluating AI agents that need web access, this is the reference document.
Written by Yuma Heymans (@yumahey), who has been building agent infrastructure at O-mega.ai since 2021 and tracks 600+ autonomous AI systems across the industry.
Contents
- Why Search Is the Most Critical Agent Infrastructure
- The Three Categories of Search APIs
- The 2026 Market Landscape: What Changed
- Tier 1: Major Search API Providers (Detailed Profiles)
- Tier 2: SERP Scraping and Data Extraction APIs
- Tier 3: AI-Native and Emerging Search APIs
- Tier 4: LLM-Integrated Search Tools
- The Master Pricing Comparison
- Benchmark Data: Quality, Latency, and Reliability
- First-Principles Analysis: What AI Agents Actually Need
- Decision Framework: Choosing the Right Search API
- Future Outlook
The Master Comparison: All 35 Providers, Weighted Scoring
Before diving into the analysis, here is the complete weighted scoring matrix. Every provider is assessed on seven criteria, each scored 0-10, with weights reflecting what matters most for AI agent builders. The final score is the weighted average (0-10 scale), and the table is ordered best-first.
Weights rationale: For AI agents, result quality (25%) and content extraction (20%) matter most because agents need accurate, ready-to-consume data. Cost (20%) is critical at production scale. Speed (15%) determines interactive UX quality. Agent readiness (10%) affects integration effort. Own index (5%) and AI synthesis (5%) are differentiators but not dealbreakers.
| # | Provider | Type | $/1K | Quality (25%) | Content (20%) | Cost (20%) | Speed (15%) | Agent (10%) | Index (5%) | AI Synth (5%) | Score /10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Exa AI | Index | $7.00 | 9 | 8 | 6 | 9 | 9 | 10 | 2 | 8.0 |
| 2 | Brave Search | Index | $5.00 | 9 | 4 | 7 | 10 | 9 | 10 | 1 | 7.4 |
| 3 | Valyu | AI Synth | $0.10-1.50 | 10 | 9 | 5 | 5 | 5 | 8 | 9 | 7.4 |
| 4 | Tavily | AI Synth | $8.00 | 8 | 9 | 5 | 7 | 10 | 4 | 7 | 7.4 |
| 5 | Perplexity Sonar | AI Synth | $5-12 | 9 | 9 | 4 | 3 | 7 | 6 | 10 | 6.9 |
| 6 | Firecrawl | Extract | ~$5.30 | 9 | 10 | 6 | 5 | 9 | 1 | 3 | 6.9 |
| 7 | You.com | AI Synth | $5.00 | 7 | 7 | 7 | 9 | 6 | 5 | 6 | 6.9 |
| 8 | Linkup | AI Synth | EUR 5 | 7 | 7 | 7 | 7 | 7 | 6 | 6 | 6.9 |
| 9 | Gemini Grounding | LLM Built-in | $14-35 | 9 | 9 | 2 | 5 | 6 | 10 | 9 | 6.5 |
| 10 | OpenAI web_search | LLM Built-in | $10.00 | 7 | 9 | 3 | 5 | 8 | 4 | 9 | 6.2 |
| 11 | Anthropic web_search | LLM Built-in | $10.00 | 7 | 9 | 3 | 5 | 8 | 4 | 9 | 6.2 |
| 12 | Jina AI | Extract | ~$0.02/MTok | 5 | 9 | 9 | 7 | 5 | 1 | 1 | 6.2 |
| 13 | Kagi | Index | $25.00 | 9 | 5 | 1 | 7 | 4 | 10 | 3 | 5.4 |
| 14 | Serper | SERP | $0.30-1.00 | 7 | 2 | 9 | 8 | 7 | 1 | 1 | 5.4 |
| 15 | SearchCans | SERP+Extract | $0.56 | 6 | 7 | 10 | 5 | 3 | 1 | 1 | 5.4 |
| 16 | xAI Grok | LLM Built-in | $5.00 | 6 | 8 | 7 | 5 | 3 | 4 | 9 | 5.4 |
| 17 | Andi Search | Index+AI | Not public | 8 | 7 | - | 7 | 3 | 10 | 7 | 5.3* |
| 18 | Google CSE | Index | $5.00 | 9 | 2 | 7 | 7 | 3 | 10 | 1 | 5.2 |
| 19 | WebSearchAPI.ai | AI Synth | ~$29/mo | 5 | 7 | 6 | 5 | 3 | 3 | 4 | 5.1 |
| 20 | Bing Grounding | LLM Built-in | $35.00 | 7 | 8 | 1 | 5 | 5 | 6 | 7 | 5.0 |
| 21 | Oxylabs | SERP | $2.00 | 7 | 2 | 8 | 7 | 4 | 1 | 1 | 4.8 |
| 22 | DataForSEO | SERP | $0.60 | 7 | 2 | 10 | 2 | 5 | 1 | 1 | 4.8 |
| 23 | Bright Data | SERP | $1.80 | 7 | 2 | 8 | 5 | 3 | 1 | 1 | 4.6 |
| 24 | SerpApi | SERP | $15.00 | 7 | 3 | 2 | 5 | 7 | 1 | 1 | 4.3 |
| 25 | DuckDuckGo | Instant Answer | Free | 4 | 3 | 10 | 8 | 2 | 5 | 3 | 4.3 |
| 26 | SearXNG | Meta (self-host) | Free | 5 | 3 | 10 | 4 | 3 | 5 | 1 | 4.3 |
| 27 | SearchApi.io | SERP | ~$10.00 | 7 | 2 | 3 | 5 | 5 | 1 | 1 | 4.1 |
| 28 | Apify | SERP | $0.15-0.50 | 5 | 2 | 10 | 3 | 5 | 1 | 1 | 4.1 |
| 29 | Mojeek | Index | Contact | 5 | 3 | - | 5 | 2 | 10 | 1 | 3.7* |
| 30 | ValueSERP | SERP | $0.15-2.59 | 5 | 2 | 9 | 5 | 3 | 1 | 1 | 3.7 |
| 31 | HasData | SERP | $0.83-2.45 | 5 | 2 | 8 | 3 | 3 | 1 | 1 | 3.7 |
| 32 | Decodo | SERP | ~$2.50 | 5 | 2 | 8 | 5 | 3 | 1 | 1 | 3.7 |
| 33 | ScaleSerp | SERP | $0.29-3.80 | 5 | 2 | 8 | 5 | 3 | 1 | 1 | 3.7 |
| 34 | ScrapingBee | SERP | $0.49-3.27 | 5 | 2 | 7 | 5 | 3 | 1 | 1 | 3.6 |
| 35 | ZenRows | SERP | $2.80 | 5 | 1 | 7 | 5 | 3 | 1 | 1 | 3.4 |
Scores marked with * exclude Cost (20% weight) from the weighted average because pricing is not publicly available. Their score is calculated from the remaining 80% of weights, renormalized.
How to read this table: Each cell is a raw score (0-10). The Final Score is the weighted average: (Quality x 0.25) + (Content x 0.20) + (Cost x 0.20) + (Speed x 0.15) + (Agent Ready x 0.10) + (Own Index x 0.05) + (AI Synthesis x 0.05). Higher is better. The table is sorted by final score, best first.
Criteria definitions: Quality (25%): result relevance and factual accuracy, based on AIMultiple/Proxyway benchmarks and index comprehensiveness. Content (20%): does the API return LLM-ready page content, or just links and snippets? Agents need content, not links. Cost (20%): price per 1K queries at production volume. 10 = under $0.50, 7 = $1-5, 5 = $5-10, 3 = $10-15, 1 = $20+. Speed (15%): P50 response latency. 10 = under 500ms, 7 = 500ms-1s, 5 = 1-3s, 3 = 3-5s, 1 = 5s+. Agent Readiness (10%): MCP server availability, SDKs, framework integrations (LangChain, CrewAI, etc.). Own Index (5%): independence from Google/Bing. 10 = fully independent, 5 = hybrid, 1 = pure scraper. AI Synthesis (5%): does it return pre-processed, citation-backed answers? 10 = full synthesis, 5 = partial, 1 = raw results.
The top 5 reveal a clear pattern: Exa AI (8.0) leads because it combines a high-quality independent index with full content extraction and deep agent framework support. Brave (7.4), Valyu (7.4), and Tavily (7.4) tie for second through different strengths: Brave on speed and independence, Valyu on domain-specific quality, Tavily on agent ecosystem integration. Perplexity Sonar (6.9) rounds out the top 5 with the best AI synthesis but slower latency dragging its weighted score down.
1. Why Search Is the Most Critical Agent Infrastructure
The fundamental economics of AI agents rest on a simple principle: intelligence is now cheap, but grounding that intelligence in reality is the hard problem. An LLM can reason, summarize, and generate, but without access to current, accurate information from the web, its outputs decay into hallucination. Search is the bridge between raw language model capability and useful, trustworthy agent behavior.
This is not a theoretical concern. According to a 2026 benchmark by AIMultiple, even the best search APIs show a 37% gap between lab performance and real-world deployment accuracy - AIMultiple. The search API you choose directly determines your agent's hallucination rate, citation quality, and the freshness of its knowledge. Context quality, not model size or parameter count, is the key variable in LLM accuracy.
The market has responded to this reality with explosive growth. The global AI agents market is valued at $7.92 billion in 2026, projected to reach $182.97 billion by 2033 at a 49.6% CAGR - DemandSage. Within this market, 62% of organizations are at least experimenting with AI agents, with 23% actively scaling agentic AI in production - Azumo. Every one of these agents needs web search. As we documented in our analysis of the cost of AI agents, search API costs are often the second-largest line item after LLM inference itself.
The stakes are also legal. Google sued SerpApi in December 2025 under the DMCA, alleging SerpApi bypassed SearchGuard, Google's anti-scraping system - SearchCans. Microsoft retired the Bing Search API entirely in August 2025 - PPC Land. These moves signal that the era of cheap, unrestricted access to major search engines through scraping is ending. The providers that survive and thrive are the ones with their own indices, licensing deals, or architectures that sidestep the scraping problem entirely.
2. The Three Categories of Search APIs
Before evaluating individual providers, you need to understand the three fundamentally different types of search APIs available in 2026. Each category solves a different problem, has different cost structures, and suits different agent architectures. Misunderstanding these categories is the most common mistake teams make when selecting search infrastructure.
The first category is SERP scraping APIs. These services query Google, Bing, or other search engines on your behalf and return structured results. They are essentially proxy layers that handle anti-bot protection, IP rotation, and HTML parsing. The advantage is that you get real Google results with all the ranking signal quality that implies. The disadvantage is legal risk (as the SerpApi lawsuit demonstrates), dependency on third-party infrastructure you do not control, and the need for additional content extraction after you receive the links. Providers in this category include SerpApi, Serper, DataForSEO, Oxylabs, Bright Data, and ScaleSerp.
The second category is index-based search APIs. These providers maintain their own web index and ranking models. When you query them, you are not hitting Google or Bing indirectly. You are querying a proprietary index. The advantage is independence from Google's legal posture, generally faster response times, and often content extraction bundled into the search results. The disadvantage is that no independent index matches Google's comprehensiveness, particularly for long-tail queries. Providers here include Brave Search, Exa, Mojeek, and Andi.
The third category is AI-synthesized search APIs. These go beyond returning ranked links. They use LLMs to process search results and return structured, citation-backed answers ready for direct consumption by your agent. The advantage is dramatically reduced post-processing in your agent pipeline. The disadvantage is higher latency, higher cost per query, and less control over the raw source material. Providers include Perplexity Sonar, Tavily, You.com, Valyu, and the built-in search tools from OpenAI, Anthropic, Google, and xAI.
Understanding which category your agent needs is the first and most important architectural decision. As we explored in our guide to retrieval augmented generation, the RAG pipeline architecture determines whether raw links or pre-processed content serves your use case better.
3. The 2026 Market Landscape: What Changed
Three seismic events reshaped the search API market in the first months of 2026, and understanding them is essential to making informed provider choices. These are not incremental shifts. They represent structural changes to the economics and availability of web search for AI agents.
The most significant event was Nebius acquiring Tavily for $275 million in February 2026, with earn-outs potentially reaching $400 million - Bloomberg. Tavily had become the default search API for AI agent developers, with over 3 million monthly SDK downloads and more than one million developers in its community. The acquisition by Nebius (the AI infrastructure company we profiled in our Nebius complete guide) raises questions about pricing stability, data handling, and product direction under new ownership. For teams that built their entire agent stack around Tavily, this introduces vendor risk that did not exist six months ago.
The second event was the retirement of the Bing Search API in August 2025, with Microsoft replacing it with "Grounding with Bing" at $35 per 1,000 transactions, a 40-483% price increase depending on the previous tier - PPC Land. This eliminated what had been a primary search source for many AI applications. Notably, 92% of ChatGPT's agent searches reportedly relied on the Bing Search API - Digital Applied. The replacement is designed as an add-on for Azure AI services rather than a standalone search API, making it impractical for most independent agent builders.
The third structural shift is Google's legal offensive against SERP scrapers combined with the closure of the Google Custom Search JSON API to new customers, with full retirement scheduled for January 2027 - Expertrec. Google now recommends Vertex AI Search as its replacement. For the AI agents ecosystem, this means the two largest search engines on earth are either shutting down their APIs or making them prohibitively expensive. The independent index providers (Brave, Exa, Mojeek) are the primary beneficiaries of this tectonic shift.
4. Tier 1: Major Search API Providers (Detailed Profiles)
These are the providers that dominate developer adoption, have the deepest integrations with agent frameworks, and handle the highest query volumes. If you are building a production AI agent in 2026, your shortlist likely starts here.
4.1 Brave Search API
Brave operates the largest independent search index at 40 billion web pages, making it one of only three search engines worldwide (alongside Google and Bing) that does not depend on another engine's results - Brave. This independence became a critical differentiator after Microsoft retired the Bing API and Google escalated legal action against scrapers.
The numbers tell the story of adoption: nearly 700,000 OpenClaw users have signed up for the Brave Search API, and the service handles billions of weekly API calls - Brave Blog. Brave supplies most of the top ten LLMs with real-time web data. In the AIMultiple benchmark, Brave achieved the highest Agent Score of 14.89 with the lowest latency at 669ms - AIMultiple.
Pricing: $5 per 1,000 requests. New users receive $5 in monthly credits (roughly 1,000 queries) with attribution required. The previous free tier of up to 5,000 queries was replaced in February 2026 with this credit-based system - Implicator. Rate limit: 50 requests per second.
What it returns: Raw SERP results with web, news, image, and video endpoints. Clean JSON with snippets, but no full-page content extraction built in.
Agent readiness: MCP server available. Python and JavaScript SDKs. Zero Data Retention mode for privacy compliance. Works with LangChain, LlamaIndex, and CrewAI.
Key differentiator: The only high-volume independent index that is not a Google/Bing scraper and not under legal threat from either.
4.2 Tavily
Tavily built its reputation as the "research librarian" of search APIs, optimizing for source authority and citation quality rather than raw speed. The platform achieved 3 million monthly SDK downloads and a community of over 1 million developers before the Nebius acquisition in February 2026 - Nebius.
Pricing (credit-based): Free tier provides 1,000 credits/month. Paid plans range from $30/month (4,000 credits) to $500/month (100,000 credits). Pay-as-you-go costs $0.008 per credit. Basic search costs 1 credit, advanced search costs 2 credits. The Research API is highly variable at 4-250 credits per request, making cost prediction difficult - Tavily Docs.
What it returns: AI-synthesized answers with citations, structured content optimized for LLM consumption. Includes search, extract, map, crawl, and deep research endpoints.
Agent readiness: Official MCP server, LangChain package (langchain-tavily), LlamaIndex integration, CrewAI support. The most comprehensive framework coverage of any search API - Tavily Docs.
Key differentiator: Citation-first design. Every result includes source attribution optimized for grounding LLM responses. The acquisition introduces vendor risk but also access to Nebius's global infrastructure.
4.3 Exa AI
Exa uses neural embeddings rather than keyword matching to search the web. This semantic approach means you can query with natural language descriptions of what you want to find, and Exa returns conceptually relevant results rather than keyword-matched pages. The company raised an $85M Series B at a $700M valuation, led by Benchmark with participation from Nvidia's NVentures - Exa Blog.
Pricing: Simplified in March 2026. Search with contents costs $7 per 1,000 requests with 10 results including text and highlights. Additional results beyond 10 cost $1 per 1,000 - Exa Pricing. Free tier: 1,000 searches/month. Pro plans start at $40/month.
What it returns: Semantically ranked results with full content extraction included. Also offers exa-code for searching across billions of GitHub repos, documentation pages, and StackOverflow posts.
Agent readiness: MCP server, Python and JavaScript SDKs, 30+ integrations including LangChain, CrewAI, LlamaIndex, Vercel AI SDK, and OpenAI-compatible endpoints - Exa MCP Docs.
Key differentiator: Semantic search. When your agent needs to find "companies doing X" rather than pages containing specific keywords, Exa's neural approach outperforms keyword-based alternatives. Latency under 450ms with 100+ queries per second capacity.
4.4 Perplexity Sonar API
Perplexity took the "AI-synthesized search" category further than anyone by turning their consumer search product into a developer API. With 30+ million monthly active users on the consumer product and a $750 million Azure commitment in January 2026, the infrastructure backing Sonar is enterprise-grade - GetPanto.
Pricing: Token-based plus per-request fees. Base Sonar: $1/$1 per million tokens (input/output) plus $5-12 per 1,000 requests depending on context depth. Sonar Pro: $3/$15 per million tokens plus $6-14 per 1,000 requests. A simple search costs roughly $0.006, while a deep research query can reach $0.41+ - Perplexity Pricing.
What it returns: AI-generated answers with inline citations. The model searches the web, processes results, and returns a synthesized response, not raw links.
Agent readiness: OpenAI-compatible API format. Works with any framework that supports the OpenAI chat completions format. MCP server available through Composio.
Key differentiator: Highest answer quality for factual queries in the Proxyway benchmark - Proxyway. Best for agents that need synthesized answers rather than raw search results. The trade-off is higher cost and latency compared to raw SERP APIs.
4.5 Serper
Serper positions itself as the fastest and cheapest Google Search API, and the pricing supports that claim. It is one of the most popular choices for AI agent builders who want Google-quality results without SerpApi-level pricing. Serper achieved this by focusing exclusively on speed and cost efficiency rather than building its own index.
Pricing: Starts at $50 for 50,000 queries ($1.00/1K). At higher volumes, pricing drops to $0.30 per 1,000 queries. Free tier: 2,500 searches with no credit card required. All credits last 6 months. Rate limit: up to 300 queries per second - Serper.
What it returns: Raw Google SERP results in JSON. Organic results, knowledge graph, people also ask, related searches. No content extraction built in.
Agent readiness: Multiple community MCP servers available. Works with LangChain, CrewAI, and most agent frameworks through simple REST API integration - GitHub.
Key differentiator: Speed and price. At $0.30/1K for high-volume users, Serper is among the cheapest ways to access Google results. However, the SearchCans analysis noted that adding a separate content extraction service brings the true cost to roughly $2.30/1K, comparable to bundled alternatives - SearchCans.
4.6 SerpApi
SerpApi is the oldest and most feature-rich SERP scraping service, supporting 20+ search engines including Google, Bing, Yahoo, Baidu, and Yandex. It returns the richest structured output of any SERP API, with detailed parsing of every SERP element. However, the Google lawsuit creates significant uncertainty.
Pricing: Free tier with 100 searches/month. Developer plan: $75/month for 5,000 searches ($15/1K). Higher tiers reduce per-search cost, but SerpApi remains the most expensive SERP API at every tier. Unused searches do not roll over - SerpApi Pricing.
What it returns: The most comprehensive structured SERP data available: organic results, ads, shopping, knowledge graph, people also ask, local results, images, videos, and dozens more SERP features across 20+ search engines.
Agent readiness: Official MCP server released in 2026 - SerpApi Blog. Python and Ruby SDKs. Integration with LangChain and other frameworks.
Key differentiator: Multi-engine coverage and parsing depth. If your agent needs to search Baidu, Yandex, or specialized Google verticals (Maps, Shopping, Scholar), SerpApi is the only option with structured parsing for all of them. The legal risk from Google's DMCA lawsuit (hearing May 2026) is the primary concern.
4.7 You.com Web Search API
You.com rebuilt its API around the agentic use case, delivering what they describe as the world's fastest web search API for LLMs. The $100 in free credits on signup makes it one of the most generous trial offers in the market.
Pricing: Web Search API at $5 per 1,000 calls. Contents API at $1 per 1,000 pages. New pricing effective March 12, 2026. Live crawl included at no extra charge. Research API also available - You.com.
What it returns: Structured search results with contextual snippets optimized for LLM grounding. Standard endpoint delivers answers in under 445ms.
Agent readiness: Native integrations with major agent frameworks. OpenAI-compatible format available.
Key differentiator: Speed. The standard search endpoint consistently delivers sub-450ms responses, making it one of the fastest AI-optimized search APIs available.
4.8 Jina AI Reader/Search
Jina AI takes a different approach: instead of competing on search results, they focus on converting any URL into LLM-friendly content. The Reader API at r.jina.ai turns web pages into clean markdown with a simple URL prefix, which has made it enormously popular as a companion to other search APIs.
Pricing: Every new API key includes 10 million free tokens across all Jina services. After that, token-based pricing at roughly $0.02 per million tokens - Jina AI. Rate limits: Free tier at 100 RPM, Paid at 500 RPM, Premium at 5,000 RPM.
What it returns: Clean markdown content extracted from web pages. The Search API returns search results; the Reader API converts any URL to LLM-ready text.
Agent readiness: REST API with simple prefix-based URL conversion. Works with any framework.
Key differentiator: The simplest web-to-markdown conversion available. If you already have URLs from another search API and just need clean content extraction, Jina Reader is the most popular standalone solution.
4.9 Google Custom Search JSON API
The legacy Google search API is closed to new customers as of 2025, with full retirement scheduled for January 1, 2027 - Expertrec. Existing users can continue until retirement. Google recommends Vertex AI Search as the replacement.
Pricing: 100 free queries/day. Paid tier: $5 per 1,000 queries. Hard cap of 10,000 queries/day, translating to a maximum monthly cost of approximately $1,500 - Google Developers.
What it returns: Standard Google search results in JSON. Limited to 10 results per query.
Key differentiator: Official Google results. But the impending shutdown makes it unsuitable for new projects. Any agent infrastructure built on this API needs a migration plan before January 2027.
4.10 Firecrawl
Firecrawl is not a search engine in the traditional sense. It is a web crawling and data extraction API that converts live web pages into clean, structured formats optimized for LLM consumption. Its search capability (using external search engines under the hood) combined with its extraction capability makes it a full-stack solution for agent web access.
Pricing: Free tier with 500 one-time credits. Hobby plan: $16/month (3,000 credits). Standard: 100,000 credits/month. Growth: 500K credits for $333/month. Search costs 1 credit per result, web search costs 2 credits per 10 results - Firecrawl Pricing.
What it returns: Clean, structured content extracted from web pages. Supports markdown, JSON, and other formats. In the AIMultiple benchmark, Firecrawl recorded the highest Mean Relevance score of 4.30 - AIMultiple.
Agent readiness: MCP server, Python and JavaScript SDKs, LangChain integration, and direct API access.
Key differentiator: Best-in-class content extraction. If your agent's primary need is reading and understanding web page content (rather than just finding links), Firecrawl's extraction quality leads the market. We covered this in depth in our Firecrawl guide.
5. Tier 2: SERP Scraping and Data Extraction APIs
These providers focus on the infrastructure layer: proxies, anti-bot bypass, and structured data extraction from search engines. They tend to be cheaper at high volume but require more engineering effort to integrate into agent pipelines than the AI-native alternatives in Tier 1.
5.1 DataForSEO
DataForSEO is the cheapest SERP API available, with pricing that undercuts every competitor by a significant margin. Their pay-as-you-go model charges per query with three speed tiers: Live at $0.002/query, Priority at $0.0012/query, and Standard (queued) at $0.0006/query. That means 1,000 SERPs at the Standard rate costs just $0.60 - DataForSEO Pricing.
The pricing is not a loss leader. DataForSEO supports Google, Bing, Yahoo, Baidu, and other engines, with structured parsing of organic results, SERP features, keyword data, and backlink information. Free trial: $1 credited on registration. No monthly minimums. For developers building automated SEO pipelines or agents that need massive search volumes at minimal cost, DataForSEO is the clear winner on pure economics.
The trade-off is latency. Standard (queued) requests can take up to 5 minutes. Even Live mode has higher latency than index-based alternatives. For real-time agent interactions where the user is waiting, DataForSEO's queued mode is not suitable. But for background research, batch processing, or any use case where you can tolerate async results, the cost advantage is overwhelming.
5.2 SearchApi.io
SearchApi.io enters the market with 100 free requests to start and paid plans from $40/month. It supports over 20 search engines and has landed notable clients including Anthropic and Ahrefs - SearchApi.io. The Legal Protection Guarantee covering up to $2M on Production+ plans addresses the growing concern about SERP scraping legality.
The effective cost per query at entry level is approximately $0.01/query. For teams that need multi-engine support without SerpApi's premium pricing, SearchApi.io offers a middle ground with solid legal protection included.
5.3 Bright Data SERP API
Bright Data brings the infrastructure of the world's largest proxy network to SERP scraping. Pay As You Go starts at $3/CPM (cost per thousand), with the Micro Package at $1.80/CPM for up to 5,555 requests at $10/month. Growth Plan: $499/month for approximately 217K requests at $2.30/CPM. Business Plan: $999/month for approximately 492K requests at $2.03/CPM - Bright Data SERP Pricing.
The key advantage is that you only pay for successful requests, failed requests are not billed. Response time is under 5 seconds, and async mode lets you collect responses multiple times within 48 hours. For enterprise teams that need reliable, high-volume SERP data with strong infrastructure behind it, Bright Data is the premium proxy-based option.
5.4 Oxylabs SERP Scraper API
Oxylabs offers both subscription and pay-per-request models. Entry-level subscription: $49/month for 24,500 results ($2/1K). Pay-per-request: $0.0006 (standard queue), $0.0012 (priority), or $0.02 (live mode) - Oxylabs. In the Proxyway benchmark, Oxylabs returned SERP pages nearly 20% faster than the claimed 700ms minimum for Google-wrapping APIs, hitting roughly 600ms P50 - Proxyway.
Minimum payment: $50. The combination of competitive pricing and top-tier speed makes Oxylabs a strong choice for teams that need real-time Google results at scale.
5.5 Decodo (formerly SmartProxy)
Rebranded from SmartProxy to Decodo, this provider offers SERP scraping starting at $29/month with flat pricing across difficulty tiers - Decodo. Their scraping templates cover Google Search with AI Overview, AI Mode, Travel Hotels, Lens, and Ads, plus Bing Search templates. The flat-pricing-across-difficulty-tiers model is a differentiator for budget-conscious teams.
5.6 ValueSERP and ScaleSerp
Both operated by Traject Data, these services target the budget end of the market. ValueSERP offers Google Search data for as little as $2.59/1K searches, dropping to $0.15/1K with high-volume commitments - ValueSERP. ScaleSerp starts at $19/month with rates from $0.29/1K - ScaleSerp. Both offer straightforward tiered pricing without the complexity of credit-based systems.
5.7 SearchCans
SearchCans positions itself as the all-in-one solution for AI agents, combining SERP scraping with a built-in Reader API at $0.56/1K requests. Credits are valid for 6 months (addressing the "use it or lose it" trap of monthly subscriptions). The platform offers parallel search lanes with zero hourly limits, and response times consistently between 1-2 seconds. The 99.65% Uptime SLA is backed by redundant infrastructure - SearchCans.
The key insight from their pricing analysis: Serper's $0.30/1K looks cheap until you add content extraction ($2.00/1K from a separate service), making the true cost $2.30/1K. SearchCans bundles both for $0.56/1K, which represents genuine total-cost-of-ownership savings.
5.8 ZenRows
ZenRows starts at $69.99/month for 250,000 credits (25,000 SERP requests at 10 credits each), translating to $2.80/1K requests. However, ZenRows returns organics-only data with zero SERP extras (no People Also Ask, related searches, AI Overviews, shopping results, or news) - ZenRows. At $2.80/1K for organics-only data, when competitors offer the same at $0.43-$0.60/1K, ZenRows is difficult to recommend for SERP-specific use cases.
5.9 HasData
HasData offers a free tier with 1,000 API credits (no credit card required), then scales from $49/month for 20,000 searches ($2.45/1K) down to $0.83/1K at 300K searches/month. Per API call price starts at $0.003 and decreases with volume. Response time averages 3.80 seconds - HasData. A solid budget option for teams that need moderate volumes without premium pricing.
5.10 Apify Google Search Scraper
Apify's marketplace approach offers multiple Google Search scrapers with pay-per-event pricing. The primary scraper costs approximately $0.50/1K SERPs, while alternative scrapers offer rates as low as $0.15/1K results. Free plan includes $5 of credit (1,000+ results). Apify's strength is its marketplace: if one scraper breaks, you can switch to another without changing your pipeline - Apify.
5.11 ScrapingBee
ScrapingBee's credit-based pricing creates variable per-request costs. Plans range from $49/month (Freelance) to $599+/month (Business+). Google Search API calls cost 15 credits each (reduced from 25 in January 2026). At base rates, SERP scraping works out to roughly $0.49-$3.27/1K depending on plan and features. Free trial: 1,000 API calls - ScrapingBee.
6. Tier 3: AI-Native and Emerging Search APIs
These providers are building specifically for the AI agent use case. They are not retrofitting traditional search or SERP scraping for LLM consumption. They are designing from first principles for the agentic era.
6.1 Linkup
Linkup raised a $10M seed round led by Gradient to build what they describe as "the Google Search for AIs." Their /fast endpoint delivers sub-second web search results, and the /deep endpoint handles complex multi-step research tasks - TechCrunch.
Pricing: Free tier with EUR 5 monthly credit (1,000 standard or 100 deep queries). Standard search: EUR 5 per 1,000 queries. Deep search: EUR 50 per 1,000 queries. No credit card required for free tier - Linkup Pricing.
Linkup's legal approach is distinctive: they connect LLMs with premium content sources through licensing agreements rather than scraping, addressing the legal risk that threatens many SERP providers. Native integrations with LangChain, LlamaIndex, and MCP make it easy to add to existing agent stacks.
6.2 Valyu
Valyu ranked first on all five domains in a cross-domain search benchmark covering time-sensitive questions (FreshQA), factual retrieval (SimpleQA), finance, economics, and medical queries - AIMultiple. Their differentiation is access to premium data sources beyond the open web: SEC filings, PubMed, clinical trials, academic journals, and textbook content from leading publishers.
Pricing: Dual model: search data costs plus AI processing at $12 per million tokens. Answer API: Fast mode $0.10, Standard $0.50, Heavy $1.50 per request, plus search costs. Deep Research API offers flat per-task pricing regardless of internal search count. New users get $10 free credit - Valyu Pricing.
For agents operating in regulated industries (finance, healthcare, legal), Valyu's access to structured proprietary data sources represents a fundamental advantage that no web-scraping API can match.
6.3 Kagi Search API
Kagi, the paid search engine known for ad-free, privacy-focused results, offers a developer API at $25 per 1,000 queries ($0.025 per search). Enrichment APIs are priced at $2/1K searches ($0.002 per search). Universal Summarizer API costs $0.03 per 1,000 tokens - Kagi Docs.
The API is in "v0" beta status with a Python SDK available. Kagi's premium search quality (their paid model means no ad-driven result manipulation) makes it appealing for agents where result quality matters more than cost. At $25/1K, it is among the most expensive options but returns consistently high-quality, unbiased results.
6.4 WebSearchAPI.ai
WebSearchAPI.ai targets the budget tier at $29/month for a starter plan, designed for teams that want Google-quality search results with content automatically extracted and cleaned for AI pipelines - WebSearchAPI.ai. The platform positions itself as a Tavily alternative, particularly for teams concerned about the Nebius acquisition's impact on Tavily's independence and pricing.
6.5 Andi Search API
Andi built a proprietary AI index called Trantora that analyzes full page content for meaning and credibility across 15 billion pages. The API offers two modes: Fast (sub-second) and Deep (with AI summaries). Compatible with Google Custom Search and OpenSearch standards for easy migration - Andi AI.
Andi claims 87% accuracy on search tasks, beating Google, ChatGPT, and Perplexity. PCMag recognized Andi as Best Free AI Search Engine in March 2026 for the third consecutive year. The privacy-first, ad-free approach means results are not influenced by advertising relationships.
6.6 Mojeek API
Mojeek is the UK-based search engine operating its own crawler-based index independently of Google and Bing. It is one of only a handful of truly independent search indices worldwide. Pricing is not publicly listed, requiring direct contact for quotes, but is reportedly significantly lower than Bing API pricing was - Mojeek. For teams that need a European, GDPR-compliant, independent index, Mojeek is a unique option.
6.7 SearXNG (Self-Hosted)
SearXNG is the open-source, self-hosted metasearch engine that queries 70+ search engines simultaneously and aggregates results without tracking users. It is completely free, with the only costs being your server infrastructure. A basic VPS with 1 vCPU and 512MB RAM is sufficient - SearXNG Docs.
The API returns JSON with title, URL, content, and engine fields per result. Railway offers a one-click deploy template. Current version is v1.5.2. For teams with infrastructure capability that want zero marginal cost per query and full control over their search pipeline, SearXNG is the only option with genuinely unlimited queries at no API cost. The trade-off: you are dependent on the upstream search engines' tolerance of automated queries, and result quality varies by configuration.
6.8 DuckDuckGo Instant Answer API
DuckDuckGo's API is free and provides instant answers for specific query types (weather, time, conversions, celebrities, places) but does not return full web search results. It pulls from over 100 sources for direct answers - DuckDuckGo. This makes it unsuitable as a primary search API for general-purpose agents but useful as a supplementary source for factual lookups where a direct answer exists.
7. Tier 4: LLM-Integrated Search Tools
The major LLM providers have all launched their own search capabilities, either as API tools or built-in model features. These represent a fundamentally different approach: instead of your agent calling a search API and processing results, the LLM provider handles search end-to-end as part of the inference call. For AI agents, this creates a build-vs-buy decision: do you manage your own search pipeline or let the LLM provider handle it?
7.1 OpenAI Web Search Tool
OpenAI's web_search tool costs $10 per 1,000 calls plus token costs. For smaller models (GPT-4o-mini, GPT-4.1-mini), search content tokens are billed as a fixed block of 8,000 input tokens per call, which adds significant cost on top of the per-call fee - OpenAI Pricing.
The convenience is that search is handled entirely within the API call. Your agent's prompt includes the web_search tool, and the model decides when to search and incorporates results automatically. The cost can compound quickly: if your agent triggers 5 searches per task, that is $0.05 in search fees alone per task, plus the token costs for processing 40,000 tokens of search context.
7.2 Anthropic Claude Web Search Tool
Anthropic's web search tool (web_search_20260209) costs $10 per 1,000 searches plus standard token costs - Anthropic Pricing. The web_fetch tool for retrieving specific URLs is also available. Notably, retrieved search context is charged as input tokens. This means a single search that returns substantial context could cost significantly more than the $0.01 base search fee.
For teams already building on Claude's API, this is the most seamless integration possible. No external API key management, no additional latency from external calls. The model decides when to search, what to search for, and how to incorporate results. We explored Claude's broader tool ecosystem in our guide to the Anthropic ecosystem.
7.3 Google Gemini Grounding with Google Search
Google offers search grounding directly within the Gemini API. For Gemini 2.5 Pro, you get 1,500 free grounded requests/day, then $35 per 1,000 requests. For Flash models, it costs $14 per 1,000 requests with 500 RPD free - Google AI.
A critical distinction emerged with Gemini 3: billing shifted from per-prompt to per-search-query. This means if the model decides to execute multiple search queries within a single prompt, each query is billed separately. Retrieved context (text or images) is not charged as input tokens. The free tier is generous enough for prototyping, but production costs at $35/1K can compound rapidly for high-volume agents.
7.4 xAI Grok Web Search
Grok's web search costs $5 per 1,000 calls for web search, X (Twitter) search, code execution, and document search. Currently only available on the Responses API with the grok-4.20-reasoning model - xAI Docs. The previous Live Search ($25/1K sources) was deprecated in December 2025.
The unique value here is X/Twitter search: Grok is the only LLM with native, real-time access to Twitter's firehose. For agents that need social media monitoring, brand tracking, or real-time public sentiment, this is unmatched.
7.5 Microsoft Grounding with Bing
The replacement for the retired Bing Search API costs $35 per 1,000 transactions for both regular and custom search. Designed as an add-on for Azure AI services rather than a standalone API - Microsoft. At $35/1K, this is among the most expensive search options available and is practical only for organizations already deeply invested in the Azure ecosystem.
8. The Master Pricing Comparison
The pricing landscape spans two orders of magnitude, from $0.60/1K (DataForSEO queued) to $35/1K (Google Gemini grounding). The right choice depends entirely on your volume, latency requirements, and whether you need content extraction included.
The chart above tells a clear story, but it conceals an important nuance: the cheapest SERP APIs (DataForSEO, Serper, SearchCans) return raw links without content extraction. If your agent needs to read the actual page content, you must add extraction costs. The AI-native APIs (Brave, Exa, Tavily, You.com) typically include content in their response, making the true cost comparison more balanced than raw numbers suggest.
Here is the volume economics model. For an agent handling 1,000 queries per day (30,000/month), monthly costs range from $18 (DataForSEO) to $1,050 (Google Gemini grounding). A production agent doing 100,000 queries per month faces a spread from $60 to $3,500. At these volumes, the difference between providers is not a rounding error. It is a material business cost that affects your unit economics.
Gartner's March 2026 analysis found that agentic workflows require 5 to 30 times more tokens per task than a standard chatbot, with a single user-initiated task triggering 10-20 LLM calls - Moltbook AI. If even half of those calls involve a web search, your actual search volume per user is far higher than the task count suggests.
9. Benchmark Data: Quality, Latency, and Reliability
The most rigorous public benchmark of search APIs for AI agents was conducted by AIMultiple in early 2026, testing 8 providers across 100 real-world AI/LLM queries and evaluating 4,000 retrieved results using GPT-5.2 as the evaluator with 10% human verification.
The benchmark revealed a critical insight: the top four providers showed no statistically meaningful quality differences. Brave (14.89), Firecrawl (14.58), Exa (14.39), and Parallel Search Pro (14.21) all fell within overlapping confidence intervals. The only significant gap was between Brave and Tavily (roughly 1 point), large enough to be meaningful rather than random chance.
Where the providers diverge dramatically is latency. Response times span a 20x range: from 669ms (Brave) to 13,600ms (Parallel Search Pro). In a multi-step agent workflow with 5 sequential searches, this means the difference between a 3-second total wait and a 68-second total wait. For interactive agents where a human is waiting for a response, latency is the deciding factor when quality is comparable.
The Proxyway benchmark added a complementary perspective, testing 2,000 requests per provider across three days in January 2026. Their key finding was that index-based APIs (Perplexity, Exa, Tavily) achieved effectively identical P50 response times under 0.4 seconds with minimal variance between P50 and P95 - Proxyway. Real-time APIs (Oxylabs, Serper) were fastest among the scraper category at 0.6-0.7 seconds P50, but P95 times sometimes exceeded 5 seconds.
A separate benchmark finding from Valyu's cross-domain evaluation tested FreshQA, SimpleQA, finance, economics, and medical queries. The critical takeaway: SimpleQA scores do not predict domain performance. A provider that excels at general factual retrieval may underperform on time-sensitive, financial, or medical queries. If your agent operates in a specific domain, generic benchmarks may not reflect your actual quality experience.
10. First-Principles Analysis: What AI Agents Actually Need
To choose the right search API, you need to start from the structural question: what does an AI agent fundamentally require from web search? The surface-level answer is "search results." The structural answer is more nuanced and leads to different architectural choices depending on your agent's purpose.
An AI agent performing web search faces five core requirements, and each one maps to different provider strengths.
Freshness is the first requirement. An agent that cannot access information newer than its training data cutoff is fundamentally limited. For news monitoring, competitive intelligence, or any time-sensitive task, the search API must return results from the last hours or days. Index-based APIs (Brave, Exa) update their indices continuously but may lag hours behind Google. Real-time SERP APIs (Serper, DataForSEO) return whatever Google shows right now, which is the freshest possible. LLM-integrated search (OpenAI, Anthropic tools) add latency but handle freshness within the inference call.
Accuracy is the second requirement, but it breaks into sub-dimensions. Factual accuracy (are the search results correct?) is different from relevance accuracy (do the results answer the agent's actual question?). Semantic search providers like Exa excel at relevance for natural language queries. SERP scrapers inherit Google's ranking quality. AI-synthesized providers like Perplexity and Tavily optimize for factual accuracy with citations. The AIMultiple benchmark suggests that for general-purpose queries, quality differences between top providers are statistically insignificant. Domain-specific accuracy is where providers diverge.
Structured data is the third requirement. An agent needs to process search results programmatically. Raw HTML is useless. Clean JSON with titles, URLs, snippets, and ideally full page content is what an agent pipeline can consume. This is where the AI-native APIs have a clear advantage: Exa, Tavily, and Firecrawl return LLM-ready content by default. SERP APIs return links and snippets but require a separate extraction step to get page content. If you factor in the extraction cost, the total-cost-of-ownership gap narrows significantly, as the SearchCans analysis demonstrated (Serper at $0.30/1K becomes $2.30/1K with extraction).
Speed is the fourth requirement, and its importance depends entirely on your agent's interaction model. For a conversational agent where a human is waiting, every second of search latency translates directly to perceived sluggishness. For a background research agent that runs overnight, latency is irrelevant. The 20x latency spread across providers (669ms to 13.6 seconds) means this decision matters enormously for interactive use cases. As we explored in our analysis of AI agent costs, latency also affects total cost because slower searches mean longer agent execution times and more LLM tokens consumed while waiting.
Cost at scale is the fifth requirement. A prototype agent doing 100 searches a day can use any provider. A production agent doing 100,000 searches a day faces costs ranging from $60/month (DataForSEO) to $3,500/month (Google Gemini grounding). The first-principles insight here is that cost-per-query is a misleading metric if it does not include the full pipeline cost (search + extraction + processing). The cheapest search API paired with an expensive extraction service may cost more than a moderately priced API that bundles both.
The structural answer to "what does an agent need from search" is: it depends on the agent's interaction model (interactive vs. batch), domain (general vs. specialized), and scale (prototype vs. production). There is no single best provider. There is only the best provider for your specific combination of these three variables.
11. Decision Framework: Choosing the Right Search API
Rather than prescribing a single recommendation, here is a decision framework based on the structural analysis above. Find your use case and follow the recommendation.
If you are building a prototype or MVP (under 1,000 queries/day): Start with Tavily or Exa. Both offer generous free tiers, excellent framework integrations, and LLM-ready output that minimizes your engineering effort. Tavily gives you 1,000 free credits/month, Exa gives you 1,000 free searches/month. The $100 free credits from You.com are also worth considering.
If you need the cheapest production search (10,000+ queries/day): DataForSEO at $0.60/1K (queued) is unbeatable on raw cost. Pair it with Jina Reader for content extraction if needed. For real-time results with content included, SearchCans at $0.56/1K with built-in Reader API offers the best total cost of ownership.
If latency is critical (interactive/conversational agents): Brave Search at 669ms P50 with its own independent index, or Exa at under 450ms with semantic understanding. Both avoid the legal risks of Google scraping.
If you need AI-synthesized answers (not raw links): Perplexity Sonar for highest answer quality, You.com for fastest response at $5/1K, or Valyu for domain-specific research in finance, healthcare, or legal.
If you are already using a major LLM provider: Consider their built-in search tool first. OpenAI and Anthropic both charge $10/1K, Google Gemini $14-35/1K, xAI $5/1K. The integration convenience may outweigh the cost premium for lower-volume use cases.
If legal risk concerns you: Avoid pure SERP scrapers (SerpApi, Serper, DataForSEO). Use Brave (own index), Exa (own index), Linkup (licensed content), or SearXNG (self-hosted). The Google lawsuit against SerpApi and the Bing API shutdown signal that scraping-dependent providers face existential risk.
If you need a self-hosted, zero-cost solution: SearXNG is the only option. It queries 70+ search engines, returns JSON, and runs on a minimal VPS. The trade-off is reliability and the engineering burden of maintaining it.
Platforms like O-mega.ai abstract away many of these search infrastructure decisions by providing agents with built-in web access through managed browser sessions. For teams that want autonomous agents without managing search API selection and pipeline engineering, a platform approach eliminates this entire category of infrastructure decisions.
12. Future Outlook
The search API market is converging on a new equilibrium driven by three structural forces.
First, the end of free scraping. Google's legal action against SerpApi, the Bing API shutdown, and Google Custom Search's retirement signal that the major search engines are closing the door on cheap access to their results. This is an economic inevitability: as AI agents consume more and more search queries (often without generating ad revenue), the search engines must either charge for access or cut it off. Within 18 months, expect most SERP scraping providers to face significantly higher upstream costs or legal challenges that get passed to customers.
Second, the unbundling and rebundling of search. The Tavily acquisition by Nebius, Exa's $85M raise, and the LLM providers' built-in search tools all point toward search becoming an infrastructure layer bundled into larger AI platforms rather than a standalone API category. The standalone search API will persist for developers who want maximum control, but the default path for most agent builders will be "use whatever search comes with your LLM provider or agent platform." As we analyzed in our guide to the big pipe of LLM inference, the major inference providers are absorbing adjacent functions into their APIs, and search is next.
Third, the rise of domain-specific search. Valyu's success in financial and medical domains demonstrates that general web search is insufficient for specialized agents. An agent advising on SEC filings needs access to EDGAR, not just web pages that mention SEC filings. An agent doing medical research needs PubMed abstracts, not just health blog posts. The next generation of search APIs will differentiate on proprietary data access, not just web crawling quality.
The MCP protocol adoption (97 million monthly SDK downloads in March 2026, governed by the Linux Foundation's Agentic AI Foundation) is creating a universal integration layer that makes it trivial to swap search providers - The New Stack. This commoditization pressure will push search API providers to compete on quality, domain depth, and legal safety rather than integration convenience, which is rapidly becoming table stakes.
For agent builders, the practical advice is: do not lock yourself to a single provider. Use MCP or framework abstractions to keep your search layer swappable. Start with a provider that matches your current constraints (cost, latency, quality), but architect for change. The market is moving too fast for any five-year commitment to make sense.
This guide reflects the web search API landscape as of April 2026. Pricing, features, and market positions change frequently. Verify current details on official pricing pages before making purchasing decisions.