Firecrawl vs Apify vs Runo: which scraping API to pick in 2026

We make Runo, so this post is not unbiased. What we can promise is that it's accurate. Wrong claims about Firecrawl or Apify get caught in five minutes by anyone who's used them. The goal here is to be useful enough that you walk away knowing which tool fits your job, even when the answer isn't us.

The 30-second version#

If you stopped reading here, you'd already be ahead of the average procurement decision in this space. The rest of the post is the why.

What each one actually is#

Firecrawl#

Firecrawl is a fetch + clean service that returns Markdown (or cleaned HTML, or the raw page). It crawls, scrapes, and converts. It's optimised for one downstream consumer: an LLM that wants tidy text in its context window.

It's a great product at what it does. The Markdown output is genuinely clean, the crawl orchestration is solid, and the pricing is reasonable for RAG-shaped workloads where you want bulk page bodies rather than specific fields.

Apify#

Apify is a platform, not a single API. It's a marketplace of "actors" (containerised Node/Python scrapers) plus the infrastructure to run them: proxy rotation, scheduling, storage. You can use someone else's actor for, say, "scrape Amazon product pages," or write your own.

The flexibility is real and so is the cost. Actors are code; code rots. If the actor you're renting breaks because the target site redesigned, you're either waiting for the actor author to fix it or writing your own.

Runo#

Runo is an AI extraction API. You send a URL plus a schema (field name, type, example value), and you get typed JSON back. We handle the fetch (with multi-tier bypass for bot-walled sites), the cleaning, the LLM extraction, and the type coercion. There are no selectors and no actors to maintain.

How they compare on the things that matter#

Output format#

The interesting case is Firecrawl's /extract endpoint, which uses an LLM to pull fields out of the cleaned page. It works, but it's a second pass over content that's already been cleaned-and-flattened to Markdown. You've lost the structural signal that helps an extractor distinguish a price tag from a footer link. Runo extracts directly from cleaned HTML with the structural signals intact, plus a canonical preamble (titles, OG tags, byline, numeric stats) that bypasses any pre-filtering. In our internal benchmarks the field-level null rate is meaningfully lower this way.

Anti-bot bypass#

Cloudflare, Datadome, and PerimeterX are aggressive in 2026 and getting more aggressive. If your URLs touch any of those (most consumer-facing e-commerce, social, and news does), bypass is the line that decides whether you get data or 403s. We covered the full stack in how to scrape Cloudflare-protected sites.

Schema definition#

This is the biggest categorical difference.

• Firecrawl wants either a Zod-style schema (in their SDK) or a free-form prompt for /extract. Both work; the prompt approach drifts more.
• Apify is whatever the actor outputs. Schema discipline lives in the actor code.
• Runo wants a JSON array: { "field": "name", "type": "string", "example": "Rachel" }. The example value double-duties as a one-shot prompt anchor that grounds the LLM's interpretation of the field.

The example-value pattern matters more than it looks. Telling the LLM { "field": "price", "type": "float", "example": 29.99 } resolves a lot of ambiguity that a Zod schema doesn't communicate. Should "$1,200" map to 1200.0 or 1200? The example shows. We wrote about this in extracting structured JSON from any HTML.

Type handling#

If you're going to coerce in your application code anyway, this doesn't matter. If you're putting the data into a database with a strict schema, it saves you a parser per field type per consumer.

Failure handling#

• Firecrawl distinguishes scrape failures from extraction failures and has reasonable error responses.
• Apify depends on the actor, but the platform surfaces run logs and exit codes well.
• Runo uses a typed error taxonomy (FETCH_BLOCKED, LLM_TRUNCATED, SCHEMA_INVALID, etc.) and explicit null for unresolvable fields, never silent drops.

The honest-null behaviour is the one we'd push hardest. If a price field comes back missing because the page truly didn't have a price, your downstream code needs to know that. Not get a quietly empty string.

Pricing shape#

This is the messiest comparison because all three price differently.

• Firecrawl charges per credit, where credits map to operations (a scrape is 1, a crawl page is 1, certain features cost more).
• Apify charges for compute units, proxy traffic, storage, and per-actor surcharges. Predicting a monthly bill from a workload sketch is genuinely hard.
• Runo charges per request. /extract is 1 request, /batch is 1 per URL, /crawl reserves up-front and refunds unused. Tiers from $0/mo (500 requests) to $449/mo (500K requests). Pro and Scale add prepaid overage credits. See pricing for the current rates.

There's no universally "cheapest" option. At small volume Runo's free tier covers more requests than Firecrawl's. At very high volume Apify can be cheaper if your actor is efficient and if you eat the maintenance cost.

Where each one wins#

Firecrawl wins when#

You're building a RAG pipeline. You want clean Markdown for a document store. You're indexing thousands of pages by content body, rather than by specific fields. The extractor work happens later, in your own LLM calls, where you've got the context to interpret the document holistically.

Apify wins when#

You have a high-volume, niche scrape against a small set of well-known sites, and there's already an Apify actor that targets those sites. Or, you're a team with the engineering bandwidth to write and maintain your own actors and want a managed runtime to host them.

Runo wins when#

You want typed JSON keyed by field name, you don't want selectors or parser code, and your URLs span many sites (e-commerce, news, social, reference, developer docs). You want type coercion at the boundary, honest nulls, and bypass tooling that handles Cloudflare/Datadome without a separate contract. The free tier is 500 requests/month. Enough to test against your real workload before paying.

Where each one struggles#

We owe you the failure modes too.

• Firecrawl isn't great when you need fields, rather than documents. The /extract endpoint exists, but it's working from already-flattened Markdown and the field-level extraction quality reflects that.
• Apify isn't great when you need broad site coverage with stable behaviour. A custom actor for one site is a great fit; coverage of arbitrary user-supplied URLs across the web is what AI extractors are built for.
• Runo isn't great when you genuinely need raw HTML or Markdown for a downstream document pipeline. We return JSON; if your consumer wants Markdown, use a tool that returns Markdown.

There's also an honest weakness common to all three: anti-bot evolves weekly, and any vendor's success rate on a given target site is a snapshot, not a contract. Benchmark on your URLs.

How to decide in an afternoon#

1. Write your real schema (5–10 fields, mixed types). Or, if you genuinely need free-form content, decide that you don't have a schema.
2. Pick 50 representative URLs from your real workload, including hard cases.
3. Run them against the candidate APIs.
4. Measure success rate, field-level null rate, latency P50/P95, and blended cost per successful request (not per request, per success: failures aren't free).
5. Read the error responses for the failures. Are they typed? Actionable?

That's the decision. Marketing pages tell you about happy paths; your real URLs tell you about the long tail, and the long tail is where success rate divides.

If your decision points to AI extraction, the Runo docs are next, and the free tier covers more than enough for the test.

TL;DR#

• Firecrawl: best for RAG/Markdown pipelines. Clean documents, rather than typed fields.
• Apify: best for niche custom scrapers or when an existing actor matches your target site. Maximum flexibility, maximum maintenance.
• Runo: best when you want typed JSON shaped to a schema, no selectors, no actors, with bypass and type coercion at the API boundary.
• They're not really competing for the same job. They're competing to define what the job is. Decide your job first, then the tool falls out.
• Benchmark on your actual URLs. Vendor pricing pages are not benchmarks.