# Onto vs Exa · Different jobs — search vs read + score > Exa is a neural search API: give it a query, get relevant URLs. Onto is a read + score API: give it a URL, get clean Markdown plus an accuracy score. Often complementary, not competing — here's the honest breakdown. **Source:** /compare/exa **Extracted:** 2026-05-20T20:59:17.632Z --- Compared · Onto vs Exa ## Exa finds URLs. _Onto reads and scores them._ Often complementary, not competing. Exa is a neural search API — give it a query, get a ranked list of relevant URLs. Onto is a read + score API — give it a URL, get clean Markdown plus a 0–100 accuracy score and hallucination flags. Many AI workloads benefit from chaining the two: Exa to find candidates, Onto to decide which ones to ground on. [Try the scanner free](/scanner)[Read API docs](https://docs.buildonto.dev/api/read) 0–100 Accuracy score per read <100 ms Cache-hit latency 1,000 Free reqs / month, forever 01 // Shared ground ### What both products do well. Despite serving different primary jobs, the overlap is real: both are managed AI-first APIs that return clean text for LLM consumption. AI-first01 Built for agents Both are managed APIs designed specifically for LLM consumption — not retrofitted scraping tools. Both ship MCP integrations and SDKs aimed at AI developers. Content extraction02 Return clean text, not raw HTML Both return extracted page content as readable text instead of dumping raw HTML. Both handle JS-rendered pages. Managed03 Auth, quotas, edge caching Both are managed services with API-key auth, rate limits, and CDN-backed responses. Neither needs you to operate crawlers or proxies. Free starts04 Real free tiers Both let you start without a credit card — Exa with credit-based trials, Onto with 1,000 reqs/month forever on every key. 02 // Capability matrix ### Different jobs, different feature sets. Em-dashes mean "not a current capability," not "intentionally missing." In several rows, the em-dash is honest about product scope. Onto Read API Exa Primary input ✓URL Natural-language query Primary output ✓Clean Markdown + score Ranked list of URLs (+ optional content) Semantic / neural search — (single-URL focus) Yes (core competency) AIO accuracy score (0–100) ✓Every response — Hallucination-risk flags ✓Per-field risk labels — Find-by-similarity — Yes Site-side SDK (Serve) ✓@ontosdk/next, request-time inject — Official MCP server ✓@ontosdk/mcp Yes Content extraction depth ✓Markdown + structured insights Highlights / summaries / full text Use-case fit ✓Grounding on known URLs Discovering relevant URLs 03 // Where Onto extends ### What read-and-score adds that search alone can't. Search returns ranked candidates. The agent still has to decide which ones to trust. Onto's scoring layer is what closes that loop. 01 Read + score, not search If you already have URLs (a sitemap, a user's bookmark, a competitor's pricing page), Exa's search is overkill — you want extraction. Onto turns a known URL into clean Markdown plus an accuracy score in one call. 02 0–100 accuracy score per URL Exa returns content from search results but doesn't score how trustworthy each result is. Onto scores every read, so your agent knows which sources to weight and which to skip. 03 Per-field hallucination flags We surface specific fields the page describes ambiguously or contradicts itself on — pricing, model names, dates. Your agent routes low-confidence fields differently. 04 Site-side SDK (Serve layer) Onto isn't just a reader — sites can install @ontosdk/next and serve agents their own pre-cleaned content. Exa indexes the web; Onto lets site owners participate. 04 // Which to pick ### The honest decision matrix. For many agent workloads the right answer is "both" — use Exa to find candidates, Onto to read + score them. Pick Onto if01 * ✓You already have the URL (user input, sitemap, internal list) and want clean Markdown + accuracy in one call. * ✓You need to know whether to trust a specific source before grounding on it — Onto scores every read. * ✓You're a site owner who wants to serve agents pre-cleaned content alongside the human HTML (Serve SDK). * ✓You want hallucination risk flagged at the field level, not handled post-hoc. Pick Exa if02 * →Your input is a natural-language question and you need to discover relevant URLs — Onto starts from URLs, not queries. * →You're building a research agent that searches the web semantically rather than reading specific known pages. * →You need find-by-similarity ("more pages like this one") — that's Exa's core competency. * →Your workflow benefits from neural ranking over keyword/recency-based ranking. 05 // Common questions ### What developers ask before choosing. Can I use both Exa and Onto together?+ Yes — and it's often the right answer. Use Exa to find relevant URLs (semantic search), then pipe the top results into Onto's `/v1/read-and-score` for clean extraction plus accuracy scoring. Exa finds candidates; Onto decides which ones the agent should ground on. They're complementary, not competing. Does Onto have a search endpoint?+ No, and we don't plan to add one. Onto is intentionally single-URL focused — that lets us do scoring and hallucination flagging well. Search is a different product with different tradeoffs; we'd rather you use a best-in-class search API (Exa, Tavily, etc.) and pipe results through us. How does Onto's content extraction compare to Exa's?+ Both extract clean text from pages. Exa offers highlights, summaries, and full content from search results. Onto returns clean Markdown with preserved structure (headings, lists, JSON-LD intact) plus the AIO score and hallucination flags. Different output shapes, different downstream uses. What's the AIO score actually grading?+ The score is _subtractive_: a page starts at 100 and loses points for specific structural problems — missing headings, schema not parseable, content-negotiation absent, JS-rendered content with no SSR fallback. Every penalty is traceable to a named cause. [Full methodology](/scoring). Is this comparison fair?+ We tried. Exa is one of the best AI-search products we've seen — calling them a "competitor" oversells the overlap. They're best at discovery; we're best at extraction + scoring. If you need both jobs, run them together. The right framing for most workloads is: Exa to find, Onto to read. Try it on your URLs ### Already have URLs? Score them in one call. Drop any URL into the scanner — see the AIO score, the hallucination risk, the per-field penalties. Free, no signup. [Open the scanner](/scanner)[// Or see pricing](/pricing)