Exa is a neural-embedding search engine over the open web. UnifAPI is 14 platform-native search endpoints. Pick by whether you want concept or platform.

Exa is a neural-embedding search engine that indexes the open web and answers semantic queries ("find me content conceptually similar to X") in sub-200ms. UnifAPI is 14 platform-native search endpoints — TikTok search, Reddit search, YouTube search, LinkedIn search — that return the upstream platform's own ranked results in a unified shape. They solve adjacent problems: Exa picks URLs, UnifAPI picks records on a specific platform.

What each one is

Exa (formerly Metaphor) is the AI-native search incumbent: it crawls the open web, builds a neural index, and exposes a single search endpoint that returns conceptually-relevant URLs plus extracted page content. The win condition is "find pages I would have struggled to find with keyword search" — and it does that well enough that Cursor's @web feature is powered by Exa.

UnifAPI ships search per platform, not over the whole web. A query against /v1/tiktok/search hits TikTok's own ranking surface and returns videos with platform metadata (hashtags, like counts, creator handles). A query against /v1/reddit/search returns posts with subreddit, score, comment count. The platform's own ranking is the feature, not a bug.

Search semantics: neural vs. platform-native

Exa answers questions like "find articles about LLM context-window scaling techniques." The query is conceptual, the corpus is the open web, the result is a ranked list of URLs whose embeddings sit close to the query embedding. Excellent for research-style agents and RAG ingestion.

UnifAPI answers questions like "find TikTok videos tagged #anthropic posted this week" or "find top Reddit threads in r/MachineLearning mentioning context windows." The query is platform-specific, the corpus is the platform's catalog, the result is records with all the platform's structured metadata. Excellent for social listening, brand monitoring, and platform-bounded research.

Pricing comparison

Exa: $0.005 per neural search returning 1-25 results, $0.025 for 26-100, $1 for 100+. Webpage contents for up to 10 results per request are bundled. Deep-research mode is $0.015/$0.075 at the same tiers. Free tier: 1,000 requests + $10 credit.

UnifAPI: flat $0.001 per record returned, across every endpoint, every platform. A search call that returns 20 TikTok videos is 20 records ($0.02). A search returning 10 results is $0.01. Free tier: 1,000 records / month.

The math depends on result-count distribution. For agents that always pull ~10-20 results, the two are in the same ballpark; for agents that need many small queries, UnifAPI's per-record floor is gentler; for agents pulling 100+ results in one shot, Exa's slab pricing is cheaper.

When Exa wins

Pick Exa if your agent's job is to find conceptually-relevant pages from anywhere on the open web — RAG ingestion, deep-research workflows, "give me articles about X" prompts. Exa's neural index is unmatched for queries that don't map to a specific keyword or platform.

When UnifAPI wins

Pick UnifAPI if your agent needs platform-specific data — social signal, hashtag tracking, creator research, subreddit monitoring, LinkedIn lookups. Neural search over the whole web can find a relevant tweet, but it can't tell you the like count, the reply tree, or the most-recent posts from that creator. Platform-native search can.

Using both

A research agent that combines them works well: Exa picks the conceptually-relevant URLs across the open web, UnifAPI fans out to enrich each URL with platform-specific data (full transcript for YouTube URLs, comment tree for Reddit URLs, creator-history for TikTok URLs). One MCP server can host both — they don't compete for slots in the tool list.

Browse the UnifAPI catalog at unifapi.com/apis, see per-record pricing at unifapi.com/pricing, or wire the MCP server alongside Exa at unifapi.com/mcp.