AI search APIs compared (Perplexity, Tavily, SerpAPI + LLM)

An engineer is building an AI feature for her company’s product — a research assistant that needs to look things up on the web before answering. ChatGPT and Claude can browse the web inside the chat product, but that’s not a building block; she needs an API her code can call. The choice lands at five plausible options: Perplexity’s full search-with-answer, Tavily’s search-results-as-LLM-context, the DIY combination of SerpAPI plus her own LLM, Brave’s neutral search API, or Exa’s semantic-search-for-AI.

Each is a different shape. Choosing wrong means poor result quality, high running cost, or weeks of avoidable integration work. This guide is the side-by-side: honest accuracy comparison, latency, pricing at scale, and the integration-cost differences most marketing pages don’t surface.

For LLM-grounded workflows where you want pre-synthesised answers with sources — Perplexity API. The output is closest to “ready to consume by an AI agent”; the synthesis layer reduces the engineering work of combining multiple search results. Trade-off: opinionated synthesis (Perplexity’s interpretation, not your LLM’s), higher per-query cost. Right for one-shot research, customer-support AI grounded in current info, RAG-over-web workflows.

For AI agents that consume search as raw context for their own reasoning — Tavily. Purpose-built for the “feed search results to an LLM for further processing” use case. Lower latency, structured output, designed for the agent-tool-use pattern. Right answer for agentic workflows where the LLM does the synthesis.

For full control over the search + LLM pipeline — SerpAPI plus your own LLM. The most flexibility; the most engineering. SerpAPI returns raw SERP data; your code decides how to process and what to feed to your model. Right for teams that want fine-grained control over the entire pipeline and have engineering capacity to maintain it.

For cost-sensitive workloads with high query volume — Brave Search API. Lowest per-query cost in the category at meaningful volume. Brave’s index is independent (not Google-derived); quality is generally good but different from Google in some categories. Right for high-volume applications where the per-query cost dominates.

For semantic-search-style queries that don’t match traditional keyword patterns — Exa. AI-curated index optimised for semantic matching (“find sites discussing X concept” rather than “find pages with keyword X”). Right for research agents, recommendation systems, content-discovery features.

At typical SMB volumes, the cost differences are modest; at high volume the per-query cost spreads matter. Pick on integration fit and output shape, not on a few cents per query.

• Pricing volatility. All providers have adjusted pricing in 2024–2025; expect further changes.
• LLM-native search integration. Built-in search in flagship LLM products (Claude, GPT, Gemini) is improving, potentially reducing the need for dedicated search APIs for some workloads.
• Specialised search APIs emerging. Vertical-specific search (legal, medical, scientific) is becoming a category.

Re-verify pricing and feature scope every 3–6 months.

For the broader RAG pattern that often uses these search APIs, see RAG explained without acronyms. For the internal-Q&A pattern that complements web search, see Internal Q&A bot over company docs. For the AI-agent patterns these often feed into, see AI agents for inbound qualification. For the underlying tokens-and-cost math, see Tokens, context windows, and what they cost.