SerpApi's answer

We provide more search engines under one subscription.

UnifAPI's answer:

Choose UnifAPI when you want AI agents — not a human clicking through a UI — to do public-data marketing research. Compared with SEO suites like Ahrefs, Semrush, or Similarweb, UnifAPI is the agent-native alternative: instead of dashboards and seats, it returns citable, normalized records straight into Claude, ChatGPT, or Codex. Compared with generic API marketplaces, it delivers platform-native records (posts, profiles, videos, comments, rankings) with source IDs and timestamps, so an agent can cite, rank, and compare across sources in a single brief. Compared with scraping infrastructure, there are no collectors, proxies, or browser actors to operate — you get a supported public-data catalog behind one MCP server. And unlike private-SaaS connectors, UnifAPI is read-only public-data research (eyes, not hands). You also skip extra LLM keys and surprise caps, using your existing agent plan with simple pay-per-record pricing at about $0.001 per operation.

SerpApi's answer

Developers/Companies who need data from search engines.

UnifAPI's answer:

UnifAPI's primary audience is marketing and growth teams that work through AI assistants, plus the developers and agent builders who support them. That includes SEO and GEO (AI visibility) specialists tracking live organic and AI SERPs, influencer and social marketers pricing creators and building shortlists from public profiles, competitive-intelligence and social-listening analysts monitoring public chatter, and content strategists mining real audience demand. On the technical side, it serves developers and AI engineers who build agents in Claude, ChatGPT, Codex, Cursor, and other MCP clients and want live public data without operating scrapers, proxies, or extra LLM keys. In short, anyone who wants their AI agent to return sourced, citable public-data marketing briefs is a good fit.

SerpApi's answer

Ruby on Rails and MongoDB

UnifAPI's answer:

UnifAPI is built around the Model Context Protocol (MCP): a single MCP server exposes its public-data agents to 27+ compatible assistants and clients, including Claude, ChatGPT, Codex, Claude Code, Cursor, VS Code, Windsurf, Zed, Cline, Continue, Perplexity, Grok, Gemini CLI, and more. Alongside MCP, it offers direct HTTP APIs for developers who prefer to call endpoints themselves, with OAuth handling authentication during the tool-call flow when live data is needed. The platform normalizes public records from many sources — TikTok, LinkedIn, Instagram, YouTube, Twitter/X, Threads, Reddit, Hacker News, News, plus SEO/GEO SERPs, Maps, Local Finder, Hotels, and Events — into consistent, citable records with source IDs and timestamps. Metering is usage-based at roughly $0.001 per record/operation, so no separate LLM key or subscription is required. Developer docs are available at docs.unifapi.com.

SerpApi's answer

We're the first web scraping company that focus on scraping search engines.

UnifAPI's answer:

UnifAPI is an agent-native public-data layer, not another dashboard or API marketplace. It gives AI assistants like Claude, ChatGPT, and Codex prebuilt marketing research agents that pull live public data and return normalized, platform-native records — posts, profiles, videos, comments, rankings, and citations — each with source IDs and timestamps an agent can cite, rank, and compare in one brief. You connect a single MCP server (works across 27+ assistants and agents) or call direct HTTP, with no separate LLM key required and pay-per-record pricing at roughly $0.001 per operation. That combination of ready-to-run marketing agents (SEO/SERP, AI visibility/GEO, influencer and KOL pricing, social listening, competitive intelligence, content strategy) plus clean, citable public data is what makes UnifAPI unique.

SerpApi's answer

Back in 2017, Julien Khaleghy, the founder of SerpApi, built an iOS app that can analyze data from a picture. iOS didn't have a proper machine learning framework back then. It was challenging: iPhones' RAM were limited, no GPU or no dedicated chip acceleration were available, using only CPU was painfully slow, and compiling/porting C code from machine learning framework like Tensorflow or Caffe to iOS wasn't straightforward. Oddly, all of this wasn't the most difficult part of this project. Collecting images from Google Images was.

In these projects, 80% of his time ended up being spent on scraping and parsing Google Images. And maybe only 20% on actual machine learning model training, UI design of the actual apps, and iOS programming. This is how SerpApi was born.

UnifAPI's answer:

UnifAPI grew out of a simple observation: AI assistants like Claude, ChatGPT, and Codex are great at reasoning but blind to live public data, and the tools meant to fill that gap were built for humans staring at dashboards, not for agents. Marketers wanting SEO rankings, AI-visibility citations, creator pricing, or competitor signals had to stitch together SEO suites, scraping infrastructure, proxies, and separate LLM keys — none of which return clean records an agent can actually cite. So UnifAPI was built as the public-data layer for agents: one MCP server (and direct HTTP when needed) that delivers normalized, platform-native records with source IDs and timestamps, wrapped in ready-to-run marketing agents. The first release focuses on marketing, creator research, and competitive intelligence, with pay-per-record pricing so teams can start from the assistant they already use.

SerpApi's answer

• Airbnb
• Nvidia
• Meta
• Shopify
• Grubhub
• and more!

UnifAPI's answer:

UnifAPI is currently in design-partner mode, working with a small first cohort rather than publishing named logos, so we don't list specific customer names yet (case studies will be co-authored later — public, anonymous, or never, at the partner's choice). The teams we work with today typically include: - Marketing and growth teams running SEO, AI-visibility (GEO), and competitor research through Claude, ChatGPT, or Codex - Influencer and social marketers pricing creators and building shortlists from public profile data - Startups and agencies building AI agents and internal automations that need public social and web evidence - Developers and AI engineers shipping MCP-based agents who want live public data without operating scrapers or extra LLM keys