# Onto vs Tavily · Different jobs — research vs read + score > Tavily is an AI research API: ask a question, get a synthesized answer pulling from multiple web sources. Onto is a read + score API: give it a URL, get clean Markdown plus an accuracy score. Often complementary — here's the honest breakdown. **Source:** /compare/tavily **Extracted:** 2026-05-20T20:59:17.586Z --- Compared · Onto vs Tavily ## Tavily synthesizes answers. _Onto reads and scores sources._ Different primary jobs, often complementary. Tavily takes a question and returns a synthesized answer with citations across multiple sources. Onto takes a URL and returns clean Markdown plus a 0–100 accuracy score and hallucination flags for that specific source. Many research agents benefit from chaining both: Tavily for breadth, Onto for per-source depth. [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 structured output for LLM consumption. AI-first01 Built for agents Both APIs are designed for LLM consumption from the ground up — not retrofitted scraping or general search. Both expose clean, parseable JSON for agent integrations. Content extraction02 Return clean text, not raw HTML Both return readable extracted content rather than dumping raw HTML at your model. Both handle JS-rendered pages. Managed03 Auth, quotas, edge caching Both are managed services with API-key auth, per-tier rate limits, and CDN-backed responses. Neither needs you to operate crawlers or search infrastructure. Real free tier04 Start without a card Both let you start free — Tavily with a per-month credit allowance, 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 Tavily Primary input ✓URL Natural-language query Primary output ✓Clean Markdown + score Search results + optional answer synthesis Multi-source synthesis — Yes (core competency) AIO accuracy score (0–100) ✓Every response — Hallucination-risk flags ✓Per-field risk labels — Per-source attribution ✓Single URL, content-hashed Multi-source with citations Site-side SDK (Serve) ✓@ontosdk/next, request-time inject — Official MCP server ✓@ontosdk/mcp Yes (Tavily MCP) News / recency-tuned — Yes Use-case fit ✓Grounding on known URLs Answering questions from the web 03 // Where Onto extends ### What per-source scoring adds. Synthesis APIs smooth over the question of which source is trustworthy. Onto's scoring layer surfaces that decision back to your agent. 01 Read + score, not research If you already have URLs (user input, sitemap, internal list), you don't need a search-and-synthesize layer. Onto turns a known URL into clean Markdown plus an accuracy score in one call. 02 0–100 accuracy score per URL Tavily synthesizes across sources but doesn't expose a per-source trust score. Onto scores every read, so when you chain multiple URLs your agent can weight sources differently — or refuse to ground on low-score ones. 03 Per-field hallucination flags Tavily returns synthesized text that may smooth over contradictions across sources. Onto surfaces specific fields on a single page where the source itself is ambiguous, so the agent can route low-confidence fields explicitly. 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 with the same scoring engine. Tavily reads the public web; Onto lets site owners participate. 04 // Which to pick ### The honest decision matrix. For many research workloads the right answer is "both" — Tavily for breadth, Onto for depth on the sources that matter. Pick Onto if01 * ✓You already have the URL and want clean Markdown + accuracy in one call. * ✓You need per-source trust signals when chaining multiple reads in an agent workflow. * ✓You're a site owner who wants to serve agents pre-cleaned content (Serve SDK), not just consume the web. * ✓You want hallucination risk flagged at the field level on a specific source, not smoothed into a synthesized answer. Pick Tavily if02 * →Your input is a research question ("what changed in the X market this week?") and you want a synthesized answer with citations. * →You're building a research/Q&A agent that needs to discover and synthesize across many sources, not read pre-selected ones. * →You need news / recency tuning — Tavily indexes for currentness in ways Onto doesn't. * →You want a single API call that returns both candidates and a synthesized summary. 05 // Common questions ### What developers ask before choosing. Can I use both Tavily and Onto together?+ Yes — and for agent workloads that combine research with grounded extraction, it's a strong pairing. Use Tavily to discover and synthesize a research question, then pipe the top source URLs into Onto's `/v1/read-and-score` for clean per-source extraction plus accuracy scoring. Tavily for breadth, Onto for depth on the sources that matter. Does Onto answer questions like Tavily?+ No. Onto stops at clean Markdown + an accuracy score per URL. It doesn't synthesize answers across multiple sources — that's an explicit non-goal. We'd rather you use a synthesis API (Tavily, Perplexity API, etc.) on top of our per-source extraction. How does Onto's MCP server differ from Tavily's?+ Tavily's MCP server exposes `search` tools — give it a query, get synthesized results. Onto's MCP server exposes `read_url`, `score_url`, `read_and_score` — give it a URL, get extraction + scoring. Different verbs for different jobs; both work in Claude Code, Cursor, etc. 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. Tavily is one of the best AI-research APIs we've seen — calling them a "competitor" oversells the overlap. They synthesize answers; we extract + score per URL. Most agent workloads benefit from both. If you only need one, the choice falls out of the input shape: starting with a question (Tavily) or starting with URLs (Onto). Try it on your URLs ### Get per-source trust scores 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)