# Onto vs Firecrawl · Agent-grade alternative with scoring > Both turn the public web into LLM-ready Markdown. Onto adds a 0–100 accuracy score, hallucination-risk flags, and an MCP server — so your agent knows whether to trust a page before grounding on it. **Source:** /compare/firecrawl **Extracted:** 2026-06-24T17:42:54.673Z --- Compared · Onto vs Firecrawl ## Firecrawl gives you Markdown. _Onto gives you Markdown plus the score._ Both turn the public web into LLM-ready content. The difference is what arrives with it. Onto returns clean Markdown _plus_ a 0–100 accuracy score, hallucination-risk flags, and an MCP server — so your agent knows whether to trust a page before it grounds on it. [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. Before the differences, the overlap. Firecrawl is a good product and gets a lot right. So does Onto. Here's the honest shared baseline neither vendor would dispute. Markdown01 HTML → clean Markdown Both strip nav, footer, and chrome. Both preserve semantic structure. Both return content an LLM can consume without further parsing. Whole sites02 Beyond a single URL Both go past one page. Firecrawl crawls + maps; Onto ships /v1/map for discovery and /v1/batch to read a list or a whole site in one billable call. MCP03 Official MCP server Both ship an MCP integration so Claude Code, Cursor, Cline, Zed, and Continue can read URLs directly through the model. Managed04 Auth, quotas, edge caching Both are managed APIs with bearer-token auth, per-tier rate limits, and CDN-backed responses. Neither needs you to operate infrastructure. 02 // Capability matrix ### Feature-by-feature. The full line-up. Where each product is strong, where each is silent. Em-dashes mean "not a current capability," not "intentionally missing." Onto Read API Firecrawl HTML → Markdown ✓Yes Yes AIO accuracy score (0–100) ✓Every response — Hallucination-risk flags ✓Per-field risk labels — Score insights & penalties ✓Traceable to structural causes — Site-side SDK (Serve) ✓@ontosdk/next, request-time inject — Official MCP server ✓@ontosdk/mcp Yes Edge-cache latency ✓Sub-100 ms hit Origin-side cache Response determinism ✓Content-hashed Best-effort Free tier ✓1,000 reqs / month, forever Trial credits Overage handling ✓Credit packs ($5–$200) Plan upgrade Sitemap discovery + multi-page ✓/v1/map + /v1/batch — 1 credit / 50 URLs (≈50× cheaper) Yes (crawl + map) — billed per page Structured extraction ✓Deterministic (JSON-LD / OpenGraph / meta) LLM-based (JSON mode) Cost per extract call ✓1 credit — deterministic ~22 credits — LLM JSON mode LLM in the pipeline ✓None — rule-based, no inference cost Yes (AI extraction) 03 // Where Onto extends the layer ### The scoring + safety layer Firecrawl doesn't ship. Anyone can clean HTML. The interesting question is what arrives with the Markdown — the metadata your agent needs to decide whether to trust a given page. 01 0–100 accuracy score Every read returns a subtractive score grading semantic clarity, structural richness, and content-negotiation health. Your agent knows which pages are worth grounding on before it spends a single context token. 02 Per-field hallucination flags We surface fields the page describes ambiguously or contradicts itself on. Your agent can route low-confidence fields to a fallback model, ask the user, or refuse to answer — instead of confidently making something up. 03 Content-hashed determinism Same URL returns the same content hash until the source actually changes. Cache freely client-side, audit confidently, and stop re-paying for URLs that haven't moved. 04 Three layers, not one Firecrawl is a layer-1 tool — it reads the web into your agent. Onto runs that same Read layer, plus Serve (the @ontosdk/next SDK that feeds AI crawlers a site's own pre-cleaned Markdown) and Act (agent transactions on sites) next. Same engine; two layers Firecrawl doesn't touch. 04 // Which to pick ### The honest decision matrix. A comparison page that only says "pick us" isn't a comparison. Here's when each one is actually the right call — from a vendor that has skin in the game. Pick Onto if01 * ✓You're building an AI agent and need to know whether to trust a page before grounding on it. * ✓You want hallucination risk flagged at the field level, not handled post-hoc. * ✓You're planning to ship the same content surface to your own users via the Serve SDK — one engine, one accuracy bar. * ✓You want predictable spend: free up to 1,000 reqs, then subscriptions plus credit packs for spikes. Pick Firecrawl if02 * →You need recursive deep-crawl that follows the link graph to arbitrary depth — Onto discovers via sitemap + on-page links, not unbounded recursion. * →You want LLM-powered extraction that infers fields a page never explicitly states — Onto's extract is deterministic and only returns structured data the page actually declares. * →You've already integrated Firecrawl deeply and the scoring layer isn't yet a fit for your use case. 05 // Common questions ### What developers ask before switching. Can I migrate from Firecrawl to Onto?+ Yes. Both APIs accept a URL and return Markdown. The Onto Read API is a single `POST /v1/read` with bearer-token auth — most integrations swap in under an hour. The new fields you'll get back (`score`, `insights`, `hallucination_risk`, `hash`) are additive; nothing breaks if you ignore them. Does Onto have a crawl or map endpoint?+ Yes. `POST /v1/map` discovers a site's URLs (sitemap, then on-page links), and `POST /v1/batch` reads, scores, or extracts a whole site — or an explicit list of URLs — in a single billable call, so you don't spend a credit per page. Where Firecrawl still wins is unbounded recursive crawl that follows the link graph to arbitrary depth; Onto's discovery is sitemap- and link-based. Does Onto do AI extraction like Firecrawl's JSON mode?+ No — on purpose. `/v1/extract` returns the structured data a page already declares (JSON-LD, OpenGraph, meta) deterministically: no model in the loop, so there's nothing to bill and nothing to hallucinate. Firecrawl's JSON mode runs an LLM to _infer_ fields a page doesn't state — genuinely useful, and a real reason to pick Firecrawl if that's what you need. We keep the model out of the data layer so your agent's own model never pays twice. [More on why](/blog/no-ai-inside). Firecrawl says it can "act" on the web — isn't that Onto's Act layer?+ Different direction. Firecrawl's interact/agent feature drives a browser — clicking, scrolling, filling forms — to _reach content while it scrapes_. Onto's Act layer (Q3 2026) is the inverse: site owners opt in to let external agents transact on _their_ site through semantic intent. One reads pages by puppeteering a browser; the other lets a site be acted upon safely. Both products fully overlap only at layer 1 (reading the web) — Onto's Serve and Act layers have no Firecrawl equivalent. What is 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, etc. Every penalty is traceable to a named cause via the `insights` array, so you can see _why_ a page scored what it scored. [Full methodology](/scoring). How does pricing compare?+ Onto: free 1,000 reqs/month forever, then $9 / $49 / $250 subscriptions plus credit packs ($5–$200) for overages. [See full pricing](/pricing). On a per-call basis we measured the gap directly: in our test the same structured extract cost **1 Onto credit vs ~22 on Firecrawl**, and a 50-URL batch is **one Onto credit vs per-page billing — ≈50× cheaper**. Subscription tiers still move often on both sides, so check Firecrawl's site for current plan numbers. Is the comparison on this page fair?+ We try. Firecrawl is a good product and is excellent at what it ships. The honest framing is that they're optimized for crawl + scrape; we're optimized for read + score. If you need both, run them side by side on a few of your real URLs (try the scanner above) and let your own data decide. Try it on your URLs ### Run both on the same page. Let the data decide. The scanner is free. Drop any URL and get an AIO score in seconds — then run it through whichever scraper you already use and compare the output side by side. [Open the scanner](/scanner)[// Or get an API key](https://app.buildonto.dev/signup)